[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_134":135},"routes/registry-package",{"_9":10,"_11":12,"_13":14,"_15":16,"_17":18,"_19":20},"pkg",{"_26":27,"_28":29,"_30":31,"_32":33,"_34":35,"_22":23,"_36":37,"_38":39,"_40":41,"_42":43,"_24":25,"_44":45},"comments",[],"versions",[21],"selectedFile","requirements.txt","canonical","https://agentlefs.com/registry/text-rule-trends/requirements.txt","historical",false,{"_22":23,"_24":25},"version",1,"publishedAt","2026-10-12T22:41:20.967Z","slug","text-rule-trends","kind","skill","title","Text rule trends","summary","Measure how the rules in versioned text files change over time: extract, cluster into themes, and compare periods.","publisher","agentleFS","body","# Text rule trends\n\nTurns per-file git histories into numbers about what those files say, and how that changed.\n\nIt extracts line-level items (by default \"rules\": imperative or normative lines) from every version\nof each file and follows each item's life. It reads each file at an age you choose (or at the end of\nevery period), embeds the items with a sentence model (bge-small by default), clusters them with\nyour own k, maps clusters to themes from your JSON, bins by month, quarter or year, labels each\ntheme rise, fall or steady with your threshold, bootstraps a before/after comparison around any\ndate, and counts one file kind's share of new files against another. The extractor can be swapped\nfor a regex or your own plugin function. It reads histories from `github-file-history` and writes\nthe JSON that `tufte-charts` and `watercolor-data-art` draw.\n\nExample output (`analyze.py` on the made-up smoke test below):\n\n```\n== themes, share of a file's items 2025Q2 -> 2026Q2\n rise Git: main branch, commits, secrets 25.0% -> 37.5%\n rise Docs and changelog 12.5% -> 20.8%\n fall Tests 37.5% -> 29.2%\n fall Code style 25.0% -> 12.5%\n```\n\nUse it when:\n- you collected many versions of one kind of file (with `github-file-history`) and want to know\n which kinds of lines appear, grow or disappear: rules in CLAUDE.md or `.cursor/rules`,\n sections in READMEs, dependencies in package.json, steps in CI files\n- you want input for `tufte-charts` or `watercolor-data-art`\n\n## Requirements\n\nPython 3.8+ and `pip install -r requirements.txt` (numpy, scikit-learn, fastembed). fastembed\ndownloads the embedding model the first time (bge-small is about 70 MB). Use `--cache-dir` to\nchoose where. `sentence-transformers` is an optional second backend.\n\n## Inputs\n\n- One folder per sample, holding `vers/` as written by `github-file-history`: one `.tsv.gz`\n or `.tsv` per file, one row per commit (sha, author date, Claude co-author 0/1, base64\n body). A `manifest.tsv` beside it, if present, maps keys to paths.\n- A themes file (your JSON) that names clusters and groups them into themes, bottom to top:\n `{\"centroids_id\": \"sha256:...\", \"clusters\": {\"0\": \"name\", ...}, \"themes\": [{\"name\": \"...\",\n \"clusters\": [0, 3]}, ...]}`. Without one, every cluster is its own theme.\n `centroids_id` is the `\"id\"` printed in the `centroids.json` the names were written for. Cluster\n numbers mean nothing outside that one set of centroids, so `analyze.py` refuses a themes file\n whose `centroids_id` differs from the labels' (`--force-themes` overrides), and refuses samples\n labelled against different centroids.\n- Optionally a config file with the analysis settings (see `examples/*/config.json`).\n\n## Steps\n\nRun the scripts by their path, and write outputs to a folder of your own, never inside the skill\nfolder. Below, `SKILL` is this skill's folder and `P`, `R` are your sample folders.\n\n```bash\n# 1. items and their lives, per sample -> P/rules.json\npython3 $SKILL/scripts/extract.py $P # the default \"rules\" extractor\npython3 $SKILL/scripts/extract.py $P --extractor regex --regex '^#{1,3}\\s+(.+)' # Markdown headings\npython3 $SKILL/scripts/extract.py $P --extractor plugin --plugin my_items.py:items\n\n# 2. embeddings, per sample -> P/emb.npy (a local working file)\npython3 $SKILL/scripts/embed.py $P [--model BAAI/bge-small-en-v1.5] [--cache-dir DIR]\n\n# 3. clusters: fit on one sample, assign the others to its centroids\npython3 $SKILL/scripts/cluster.py fit $P --k 40 > clusters.txt # -> P/labels.json, P/centroids.json\npython3 $SKILL/scripts/cluster.py assign $R --centroids $P/centroids.json\n\n# 4. read clusters.txt, name the clusters and group them into themes.json,\n# with \"centroids_id\" set to the \"id\" in P/centroids.json\n\n# 3b. or reuse someone's named clusters: assign EVERY sample to their centroids.json\npython3 $SKILL/scripts/cluster.py assign $P --centroids their/centroids.json\n\n# 5. trends -> OUT/*.json\npython3 $SKILL/scripts/analyze.py --sample a=$P --sample b=$R --themes themes.json \\\n --config config.json --out OUT\n```\n\n### Extractors (`extract.py`)\n\n| `--extractor` | what is an item | default words |\n|---|---|---|\n| `rules` | a list item, or a line starting with a capital letter, whose text (after an optional bold label) starts with an imperative or normative word: always, never, use, run, avoid, must, should, ... Fenced code, headings and table rows are skipped | 4 to 45 |\n| `lines` | every non-blank line outside fenced code, list markers removed | 1 to 45 |\n| `regex` | lines matching `--regex`; the item is group 1 if there is one, else the whole match | 1 to 45 |\n| `plugin` | whatever your function returns: `--plugin FILE.py:FUNC`, where `FUNC(body)` returns a list of strings | 1 to 45 |\n\nOther settings: `--min-words`, `--max-words`, `--match` (default 0.6: an item keeps its identity\nthrough a rewording when its words overlap that much, by token Jaccard, with an unmatched item\nof the previous version), `--kind-map` (a JSON `{\"regex\": \"label\"}` matched against each file's\npath, to group file names into kinds; the default kind is the file name), and `--order`.\n\n**Version order.** Versions are replayed oldest first. `--order datetime` (default) sorts by the\nparsed author date, so mixed UTC offsets come out in the right order. `--order string` sorts by\nthe date text, which some older runs used; pass it to reproduce one of those.\n\n### Analysis settings (`analyze.py`, or keys in `--config`)\n\n| setting | default | meaning |\n|---|---|---|\n| `mode` | `cohort` | `cohort`: bin files by creation period, read each at creation + `age`. `snapshot`: in every period, read every live file as it stood on the period's last day |\n| `age` | 30 | days after creation |\n| `created` | `first-item` | a file's creation date: its earliest item, or `first-version` (its first commit) |\n| `period` | `quarter` | `quarter`, `month` or `year` |\n| `periods` | all with data | the bins to report |\n| `from`, `to` | first, last bin | the two bins compared for rise and fall |\n| `threshold` | 0.15 | relative change in a theme's share that counts as rise or fall. A theme that starts at 0 and grows is `rise`. If `from` or `to` is an empty bin, its shares are `null` and every theme is `too few` |\n| `min_files` | 20 | if a compared bin has fewer files, every theme is `too few`; a window side with fewer is flagged too few |\n| `before`, `after` | none | windows `A:B` and `C:D` by creation date, `[A, B)` and `[C, D)` |\n| `split` | none | one date instead: before = every file created earlier, after = created on it or later |\n| `kinds` | all, most files first | kinds reported separately, in this order (the bootstrap draws follow it) |\n| `share` | none | `A:B`: kind A's share of new files among A and B, per period |\n| `boot`, `seed` | 3000, 3 | bootstrap resamples and numpy `default_rng` seed |\n| `today` | **the current date** | files younger than `age` on this day are left out, and share.json leaves out files first committed after it. Set it to make a run repeatable |\n\nWindows compare files by when they were created, each read at creation + `age`, in both modes. In\nsnapshot mode the bins are calendar periods, but the windows still work this way.\n\n## Outputs\n\n| file | schema |\n|---|---|\n| `/rules.json` | `{nfiles, nhistory, life: [{kind, born, died, text, norm, repo}], files: {key: {kind, first, deleted}}, co_month, settings}`. `nhistory` counts history files found, `nfiles` those with a version. `repo` is the history file's name (one file in one repo), kept under that name for compatibility |\n| `/emb.npy` | float32 `[items, dim]`, same order as `life`. A local working file |\n| `/labels.json` | `{k, labels, centroids_id}` |\n| `/centroids.json` | `{k, dim, centroids: [[...]]}`, unit length, plain text. Values are the shortest decimals that read back as the same float32, so assigning with the JSON gives the same labels as a `.npy`. `cluster.py convert` turns one into the other |\n| `OUT/pooled.json` | `{Q, nfiles, tot, med, pf, files_with_rules, overlap}`: per period, files, mean and median items per file, mean items per file in each cluster. An empty bin is `null`, never 0 |\n| `OUT/themes.json` | `{q, nfiles, tot, med, from_q, to_q, threshold, groups: [{name, kind, v, share_from, share_to}], rest, files_with_items, items, samples: {name: {history_files, files, files_with_items, items}}}`. `kind` is `rise`, `fall`, `steady` or `too few`. `share_from` and `share_to` are **fractions** (0.181 = 18.1%). Empty bins are `null`. The input to both chart skills |\n| `OUT/compare.json` | per sample and kind: `periods` (n, mean, median, pf per period, whatever the unit) and `window` (means, medians, difference, bootstrap 95% CI, and `too_few`) |\n| `OUT/share.json` | per sample, with `share`: `{Q, files, history_files, : [new files per period], share_of, share_vs, share}`. `share` is in **percent** (87.2 = 87.2%). Counts every file by its first commit, with or without items, over `periods` if set (else every period), leaving out files first committed after `today` |\n\n## Examples\n\n### Smoke test: made-up CONTRIBUTING.md rules (`examples/made-up/`)\n\n`vers/` holds twelve made-up projects, each starting a CONTRIBUTING.md and editing it a few times.\nLater projects write fewer style rules and more git rules, so the trend has a known shape.\n`centroids.json`, `themes.json` and `config.json` beside it name one k=4 fit. Under a minute after\nthe model download:\n\n```bash\nmkdir -p /tmp/trt && cp -r $SKILL/examples/made-up/vers /tmp/trt/\npython3 $SKILL/scripts/extract.py /tmp/trt # files 12 with items 12 items 96\npython3 $SKILL/scripts/embed.py /tmp/trt # embedded 96 items\npython3 $SKILL/scripts/cluster.py assign /tmp/trt --centroids $SKILL/examples/made-up/centroids.json\npython3 $SKILL/scripts/analyze.py --config $SKILL/examples/made-up/config.json \\\n --sample made-up=/tmp/trt --themes $SKILL/examples/made-up/themes.json --out /tmp/trt/out\n```\n\nExpected: \"Git: main branch, commits, secrets\" rises (25.0% → 37.5% of a file's rules, 2025Q2 →\n2026Q2), \"Docs and changelog\" rises (12.5% → 20.8%), \"Tests\" falls (37.5% → 29.2%) and \"Code\nstyle\" falls (25.0% → 12.5%). The split at\n2025-10-01 compares 5 files with 7. The config uses `min_files: 2` because the bins are tiny; real\ndata wants the default 20.\n\n### README section headings (`examples/readme-sections/`)\n\nThe README histories of the five repos in `github-file-history/examples/readme-package`, read as\nMarkdown headings, in snapshot mode by year:\n\n```bash\npython3 $SKILL/scripts/extract.py $D --extractor regex --regex '^#{1,3}\\s+(.+)' \\\n --kind-map '{\"(?i)^readme\": \"README\"}' # files 12 with items 7 items 151\npython3 $SKILL/scripts/embed.py $D\npython3 $SKILL/scripts/cluster.py fit $D --k 6 # then name the clusters: themes.json\npython3 $SKILL/scripts/analyze.py --config $SKILL/examples/readme-sections/config.json \\\n --sample five=$D --themes $SKILL/examples/readme-sections/themes.json --out OUT\n```\n\nOn the data collected 2026-10-11, headings per README went from 13.0 (2016) to 15.8 (2025), and\n\"API reference entries\" rose from 35.9% to 45.6% of them. `themes.json` names that one k=6 fit;\na new collection or fit needs new names. `package_deps.py` is a plugin example: each dependency\nin a package.json becomes an item (93 items from the four package.json histories).\n\n**Worked example.** These skills were first written for the agentleFS article \"CLAUDE.md best\npractices\" (https://agentlefs.com/blog/claude-md-best-practices), which studied 5,786 CLAUDE.md and\nAGENTS.md files. Its data, settings and step-by-step recipe live outside the skills, in the\n`article-claude-md-best-practices` folder (`RECIPE.md` and `data/`).\n\n## Limits and caveats\n\n- **The extractors are heuristics.** The rules extractor counts imperative lines in English and\n misses rules written as prose paragraphs or tables. Check a sample of `rules.json` by eye.\n- **Clusters are a model's grouping; themes are a person's.** Cluster numbers depend on the\n embeddings, the sklearn version and the data. A fit on new data gives new clusters, even with\n the same k and seed: in one case, a fit on a fresh collection of the same kind of files matched\n the older centroids at 0 of 40 positions. So to reuse named themes on new data, assign every\n sample to the centroids they were named for. `cluster.py fit --match-to OLD.json` renumbers a new\n fit to line up with old centroids (Hungarian matching on cosine) and reports how close they are,\n but its clusters are still new ones, and analyze.py will refuse the old themes for them. Labels\n from the fit itself and labels assigned to its centroids differ for a few items (about 1%).\n- **Small numbers.** A bin or window side with fewer than about 20 files is noise. `analyze.py`\n marks those `too few`. Look at `nfiles` before you read a bin.\n- **Dates show timing, not cause.** A before/after split on a date cannot separate that date from\n anything else that happened then.\n- **Sample bias** carries over from collection. Report samples separately before you pool them.\n- **Licences.** `rules.json` quotes the files. Only publish it, or quotes from it, for repos whose\n licence allows that.\n\n## Credits\n\n- **bge-small-en-v1.5** by BAAI (Beijing Academy of Artificial Intelligence), the default\n embedding model, https://huggingface.co/BAAI/bge-small-en-v1.5 . MIT licence per its model card\n (checked 2026-10-11). fastembed downloads the quantized ONNX port\n `Qdrant/bge-small-en-v1.5-onnx-Q`, also MIT per its model card. The model is downloaded, not\n bundled; `centroids.json` files hold k-means centres computed from its vectors.\n- **fastembed** by Qdrant (Apache-2.0, per the package metadata of 0.9.0): the embedding runtime.\n- **scikit-learn** (BSD-3-Clause): `KMeans` for `cluster.py fit`. **numpy** (BSD-3-Clause) for\n the vectors and the bootstrap; **SciPy** (BSD-3-Clause) for the optional Hungarian matching in\n `cluster.py fit --match-to`. All are installed by you, not bundled.\n\n","fields",{},"files",[50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77],"approvedBy","frdchang@gmail.com","standing",{"_46":47,"_48":47,"_49":-5},"chains",0,"ratings","meanByChain",{"_78":132,"_80":133},{"_78":131,"_80":37},{"_78":129,"_80":130},{"_78":127,"_80":128},{"_78":125,"_80":126},{"_78":123,"_80":124},{"_78":121,"_80":122},{"_78":119,"_80":120},{"_78":117,"_80":118},{"_78":115,"_80":116},{"_78":113,"_80":114},{"_78":111,"_80":112},{"_78":109,"_80":110},{"_78":107,"_80":108},{"_78":105,"_80":106},{"_78":103,"_80":104},{"_78":101,"_80":102},{"_78":99,"_80":100},{"_78":97,"_80":98},{"_78":95,"_80":96},{"_78":93,"_80":94},{"_78":91,"_80":92},{"_78":16,"_80":90},{"_78":88,"_80":89},{"_78":86,"_80":87},{"_78":84,"_80":85},{"_78":82,"_80":83},{"_78":79,"_80":81},"path","scripts/extract.py","content","#!/usr/bin/env python3\n\"\"\"Extract line-level items (by default \"rules\") from every version of every file, and follow\neach item's life: when it appeared, and when it was deleted.\n\nReads SAMPLE/vers/*.tsv.gz or *.tsv as written by github-file-history (one row per version:\nsha, author_date, claude_coauthor 0/1, base64 body; an empty body is a deletion).\n\nUsage:\n python3 extract.py SAMPLE_DIR [--extractor rules|lines|regex|plugin] [--regex RE]\n [--plugin FILE.py:FUNC] [--min-words N] [--max-words N] [--match 0.6]\n [--order datetime|string] [--out FILE]\n\nExtractors:\n rules (default) a list item, or a line starting with a capital letter, whose text (after an\n optional bold label) starts with an imperative or normative word (always, never, use,\n run, avoid, must, should, ...). Fenced code, headings and table rows are skipped.\n Default words: 4 to 45.\n lines every non-blank line outside fenced code, list markers removed. Default words: 1 to 45.\n regex lines (outside fenced code) that match --regex. The item is group 1 if the pattern has\n a group, else the whole match. Example, Markdown headings: --regex '^#{1,3}\\\\s+(.+)'\n plugin your own function: --plugin myextract.py:items. It gets the file body (str) and returns\n a list of item strings. Word limits and the identity rules below still apply.\n\nWords are counted after normalising: lower case, code spans -> \"code\", links -> their text,\ndigits and markdown removed. An item keeps its identity across a rewording when most of its\nwords survive: token Jaccard >= --match (default 0.6) against an unmatched item of the\nprevious version.\n\nVersions are replayed oldest first. --order datetime (default) sorts by the parsed author date,\nso mixed UTC offsets order correctly. --order string sorts by the date text, as some older\nruns did.\n\nOutput (default SAMPLE_DIR/rules.json):\n nfiles files with at least one version\n nhistory history files found (a file whose history came back empty counts here, not in nfiles)\n life one record per item: {kind, born, died, text, norm, repo}\n kind the file's kind (its file name unless --kind-map says otherwise)\n born author date of the version where it first appeared\n died author date of the version where it disappeared, or null\n text the line as written; norm its normalised form\n repo the history file's name (.tsv.gz or .tsv), i.e. one file in one\n repo; the key is ___. Kept as \"repo\" for compatibility\n files {key: {kind, first, deleted}}: first version date, and the date of the deletion if\n the last version deleted the file\n co_month {\"YYYY-MM\": [versions with a Claude co-author trailer, versions]}\n settings the extractor settings used\n\"\"\"\nimport argparse\nimport collections\nimport importlib.util\nimport json\nimport os\nimport re\nimport sys\nfrom datetime import datetime\n\nsys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))\nfrom common import Kinds, history_files, key_of, load_kind_map, load_versions # noqa: E402\n\nWORD = re.compile(r\"[a-z][a-z0-9_+#.-]*\")\nIMPERATIVE = re.compile(r\"^(always|never|do|don'?t|does not|avoid|prefer|use|run|keep|make sure|ensure|follow|add|write|read|check|test|update|create|remove|delete|put|place|import|call|ask|verify|commit|document|must|should|only|before|after|when|if|no |not |all |every|treat|leave|stop|start|wait|search|look|consider|default|instead|don't)\\b\")\n\n\ndef norm(line):\n t = re.sub(r\"`[^`]*`\", \" code \", line.lower())\n t = re.sub(r\"\\[([^\\]]*)\\]\$[^)]*\$\", r\"\\1\", t)\n t = re.sub(r\"[*_>#]\", \" \", t)\n t = re.sub(r\"\\d+\", \" \", t)\n return \" \".join(WORD.findall(t))\n\n\ndef rule_lines(body):\n \"\"\"The default heuristic: imperative list items and capitalised lines.\"\"\"\n out, fence = [], False\n for raw in body.splitlines():\n s = raw.strip()\n if s.startswith(\"```\"):\n fence = not fence\n continue\n if fence or not s or s.startswith(\"#\") or s.startswith(\"|\"):\n continue\n m = re.match(r\"^([-*+]|\\d+[.)])\\s+(.*)\", s)\n text = m.group(2) if m else s\n if not m and not text[:1].isupper():\n continue # a wrapped continuation line, not a rule\n head = re.sub(r\"^\\*\\*[^*]{1,60}\\*\\*\\s*[:.-]?\\s*\", \"\", text) # drop a bold label\n if IMPERATIVE.match(head.lower()):\n out.append(text)\n return out\n\n\ndef plain_lines(body, rx=None):\n out, fence = [], False\n for raw in body.splitlines():\n s = raw.strip()\n if s.startswith(\"```\"):\n fence = not fence\n continue\n if fence or not s:\n continue\n if rx is None:\n m = re.match(r\"^([-*+]|\\d+[.)])\\s+(.*)\", s)\n out.append(m.group(2) if m else s)\n else:\n m = rx.search(s)\n if m:\n out.append(m.group(1) if m.groups() else m.group(0))\n return out\n\n\ndef load_plugin(spec):\n path, _, func = spec.rpartition(\":\")\n if not path:\n raise SystemExit(\"--plugin wants FILE.py:FUNCTION\")\n mod_spec = importlib.util.spec_from_file_location(\"extract_plugin\", path)\n mod = importlib.util.module_from_spec(mod_spec)\n mod_spec.loader.exec_module(mod)\n return getattr(mod, func)\n\n\ndef make_items(a):\n if a.extractor == \"rules\":\n get = rule_lines\n elif a.extractor == \"lines\":\n get = plain_lines\n elif a.extractor == \"regex\":\n if not a.regex:\n raise SystemExit(\"--extractor regex needs --regex\")\n rx = re.compile(a.regex)\n get = lambda body: plain_lines(body, rx)\n else:\n if not a.plugin:\n raise SystemExit(\"--extractor plugin needs --plugin FILE.py:FUNC\")\n get = load_plugin(a.plugin)\n lo = a.min_words if a.min_words is not None else (4 if a.extractor == \"rules\" else 1)\n hi = a.max_words\n\n def items(body):\n seen, uniq = set(), []\n for text in get(body):\n n = norm(text)\n if lo <= len(n.split()) <= hi and n not in seen:\n seen.add(n)\n uniq.append((n, text.strip()))\n return uniq\n return items, lo\n\n\ndef jacc(a, b):\n A, B = set(a.split()), set(b.split())\n return len(A & B) / max(1, len(A | B))\n\n\ndef parse_dt(s):\n try:\n return datetime.fromisoformat(s.replace(\"Z\", \"+00:00\")).timestamp()\n except ValueError:\n return float(\"inf\")\n\n\ndef main():\n ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)\n ap.add_argument(\"sample\")\n ap.add_argument(\"--extractor\", default=\"rules\", choices=[\"rules\", \"lines\", \"regex\", \"plugin\"])\n ap.add_argument(\"--regex\")\n ap.add_argument(\"--plugin\")\n ap.add_argument(\"--min-words\", type=int)\n ap.add_argument(\"--max-words\", type=int, default=45)\n ap.add_argument(\"--match\", type=float, default=0.6, help=\"Jaccard needed to keep identity through a rewording\")\n ap.add_argument(\"--order\", default=\"datetime\", choices=[\"datetime\", \"string\"])\n ap.add_argument(\"--kind-map\", help='JSON file or string {\"regex\": \"label\"} matched against the path')\n ap.add_argument(\"--out\", help=\"default SAMPLE_DIR/rules.json\")\n a = ap.parse_args()\n\n items_of, lo = make_items(a)\n kinds = Kinds(a.sample, load_kind_map(a.kind_map))\n files = history_files(a.sample)\n if not files:\n sys.exit(f\"no {a.sample}/vers/*.tsv.gz or *.tsv\")\n life, nfiles, info = [], 0, {}\n co_month = collections.defaultdict(lambda: [0, 0])\n for path in files:\n key = key_of(path)\n kind = kinds(key)\n vers = load_versions(path)\n if a.order == \"datetime\":\n vers.sort(key=lambda r: parse_dt(r[0]))\n if not vers:\n continue\n nfiles += 1\n info[key] = {\"kind\": kind, \"first\": vers[0][0], \"deleted\": vers[-1][0] if not vers[-1][2] else None}\n alive = {} # norm -> record\n for d, co, body in vers:\n co_month[d[:7]][0] += co\n co_month[d[:7]][1] += 1\n matched, nxt = set(), {}\n for n, text in items_of(body):\n rec = alive.get(n)\n if rec is None:\n best = max(((jacc(n, k), k) for k in alive if k not in matched), default=(0, None))\n if best[0] >= a.match:\n rec = alive[best[1]]\n matched.add(best[1])\n if rec is None:\n rec = {\"kind\": kind, \"born\": d, \"died\": None, \"text\": text, \"norm\": n, \"repo\": os.path.basename(path)}\n life.append(rec)\n else:\n matched.add(rec[\"norm\"])\n rec[\"norm\"] = n\n nxt[n] = rec\n kept = {id(r) for r in nxt.values()}\n for rec in alive.values():\n if id(rec) not in kept:\n rec[\"died\"] = d\n alive = nxt\n settings = {\"extractor\": a.extractor, \"regex\": a.regex, \"plugin\": a.plugin, \"min_words\": lo,\n \"max_words\": a.max_words, \"match\": a.match, \"order\": a.order}\n out = a.out or os.path.join(a.sample, \"rules.json\")\n with open(out, \"w\") as f:\n json.dump({\"nfiles\": nfiles, \"nhistory\": len(files), \"life\": life, \"files\": info, \"co_month\": co_month, \"settings\": settings}, f)\n with_items = len({r[\"repo\"] for r in life})\n gone = sum(r[\"died\"] is not None for r in life)\n print(f\"files {nfiles} with items {with_items} items {len(life)} later deleted {gone / max(1, len(life)):.0%} -> {out}\")\n\n\nif __name__ == \"__main__\":\n main()\n","scripts/embed.py","#!/usr/bin/env python3\n\"\"\"Embed every item's text with a sentence model (bge-small by default).\n\nUsage:\n python3 embed.py SAMPLE_DIR [--model BAAI/bge-small-en-v1.5]\n [--backend fastembed|sentence-transformers] [--cache-dir DIR] [--max-chars 300]\n\nReads SAMPLE_DIR/rules.json, writes SAMPLE_DIR/emb.npy (float32, one row per item, same order).\nemb.npy is a local working file; it is not meant to be shared or bundled.\nBefore embedding: code spans become the word CODE, links keep their text, bold markers go, and\nthe text is cut at --max-chars characters.\n\nBackends:\n fastembed (default). \"BAAI/bge-small-en-v1.5\" is served as\n Qdrant's quantised ONNX export. Any model fastembed lists works. pip install fastembed\n sentence-transformers: any Hugging Face sentence model, normalised. pip install sentence-transformers\n Vectors differ slightly from fastembed's for the same model, so clusters will move.\n\"\"\"\nimport argparse\nimport json\nimport os\nimport re\n\nimport numpy as np\n\n\ndef clean(t, n):\n t = re.sub(r\"`[^`]*`\", \"CODE\", t)\n t = re.sub(r\"\\[([^\\]]*)\\]\$[^)]*\$\", r\"\\1\", t)\n t = re.sub(r\"\\*\\*([^*]+)\\*\\*\", r\"\\1\", t)\n return t.strip()[:n]\n\n\ndef main():\n ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)\n ap.add_argument(\"sample\")\n ap.add_argument(\"--model\", default=\"BAAI/bge-small-en-v1.5\")\n ap.add_argument(\"--backend\", default=\"fastembed\", choices=[\"fastembed\", \"sentence-transformers\"])\n ap.add_argument(\"--cache-dir\", default=None, help=\"where the model is downloaded\")\n ap.add_argument(\"--batch\", type=int, default=256)\n ap.add_argument(\"--max-chars\", type=int, default=300)\n a = ap.parse_args()\n\n life = json.load(open(os.path.join(a.sample, \"rules.json\")))[\"life\"]\n texts = [clean(r[\"text\"], a.max_chars) for r in life]\n if a.backend == \"fastembed\":\n from fastembed import TextEmbedding\n model = TextEmbedding(a.model, cache_dir=a.cache_dir)\n E = np.array(list(model.embed(texts, batch_size=a.batch)), dtype=np.float32)\n else:\n from sentence_transformers import SentenceTransformer\n model = SentenceTransformer(a.model, cache_folder=a.cache_dir)\n E = model.encode(texts, batch_size=a.batch, normalize_embeddings=True, show_progress_bar=False).astype(np.float32)\n np.save(os.path.join(a.sample, \"emb.npy\"), E)\n print(f\"embedded {len(texts)} items with {a.model} -> {E.shape}\")\n\n\nif __name__ == \"__main__\":\n main()\n","scripts/common.py","\"\"\"Shared pieces: reading file histories, file kinds, periods, centroids.\"\"\"\nimport base64\nimport gzip\nimport glob\nimport json\nimport os\nimport re\nfrom datetime import date, timedelta\n\n\ndef history_files(root):\n \"\"\"SAMPLE/vers/*.tsv.gz and *.tsv, sorted by key.\"\"\"\n files = glob.glob(os.path.join(root, \"vers\", \"*.tsv.gz\")) + glob.glob(os.path.join(root, \"vers\", \"*.tsv\"))\n return sorted(files, key=lambda p: os.path.basename(p))\n\n\ndef key_of(path):\n b = os.path.basename(path)\n return b[: -len(\".tsv.gz\")] if b.endswith(\".tsv.gz\") else b[: -len(\".tsv\")]\n\n\ndef open_text(path):\n return gzip.open(path, \"rt\") if path.endswith(\".gz\") else open(path, encoding=\"utf-8\")\n\n\ndef load_versions(path):\n \"\"\"[(author_date, claude_coauthor, body)], oldest first. An empty body is a deletion.\"\"\"\n rows = []\n with open_text(path) as f:\n for line in f:\n parts = line.rstrip(\"\\n\").split(\"\\t\")\n if len(parts) < 3:\n continue\n body = base64.b64decode(parts[3]).decode(\"utf-8\", \"replace\") if len(parts) > 3 and parts[3] else \"\"\n rows.append((parts[1], int(parts[2]), body))\n rows.sort(key=lambda r: r[0]) # oldest first, by author date string\n return rows\n\n\ndef load_manifest(root):\n \"\"\"{key: path in repo} from SAMPLE/manifest.tsv, or {} when there is none.\"\"\"\n p = os.path.join(root, \"manifest.tsv\")\n out = {}\n if os.path.exists(p):\n for line in open(p, encoding=\"utf-8\"):\n f = line.rstrip(\"\\n\").split(\"\\t\")\n if len(f) >= 3:\n out[f[0]] = f[2]\n return out\n\n\nclass Kinds:\n \"\"\"A file's kind. By default its file name (CLAUDE.md, README.md, package.json).\n\n kind_map is {regex: label}, tried in order against the file's path in the repo\n (or, with no manifest, the part of the key after \"__\"); the first match wins.\n \"\"\"\n\n def __init__(self, root, kind_map=None):\n self.manifest = load_manifest(root)\n self.rules = [(re.compile(k), v) for k, v in (kind_map or {}).items()]\n\n def path(self, key):\n return self.manifest.get(key) or key.split(\"__\", 1)[-1]\n\n def __call__(self, key):\n p = self.path(key)\n for rx, label in self.rules:\n if rx.search(p):\n return label\n return p.rsplit(\"/\", 1)[-1]\n\n\ndef load_kind_map(s):\n if not s:\n return None\n if isinstance(s, dict):\n return s\n return json.load(open(s)) if os.path.exists(s) else json.loads(s)\n\n\n# ---- periods: \"2026Q1\" (quarter), \"2026-03\" (month), \"2026\" (year)\n\ndef period_of(d, unit):\n if unit == \"quarter\":\n return f\"{d[:4]}Q{(int(d[5:7]) - 1) // 3 + 1}\"\n if unit == \"month\":\n return d[:7]\n if unit == \"year\":\n return d[:4]\n raise SystemExit(f\"unknown period {unit!r}: use quarter, month or year\")\n\n\ndef period_start(p):\n if \"Q\" in p:\n return date(int(p[:4]), 3 * int(p[5]) - 2, 1)\n if len(p) == 7:\n return date(int(p[:4]), int(p[5:7]), 1)\n return date(int(p[:4]), 1, 1)\n\n\ndef period_next(p):\n \"\"\"The first day after the period.\"\"\"\n s = period_start(p)\n if \"Q\" in p:\n return date(s.year + 1, 1, 1) if s.month == 10 else date(s.year, s.month + 3, 1)\n if len(p) == 7:\n return date(s.year + 1, 1, 1) if s.month == 12 else date(s.year, s.month + 1, 1)\n return date(s.year + 1, 1, 1)\n\n\ndef period_range(p0, p1, unit):\n out, p = [], p0\n while p <= p1:\n out.append(p)\n p = period_of(period_next(p).isoformat(), unit)\n return out\n\n\ndef period_end(p):\n return period_next(p) - timedelta(days=1)\n\n\n# ---- centroids: JSON (text) or .npy\n\ndef load_centroids(path):\n import numpy as np\n if path.endswith(\".npy\"):\n return np.load(path).astype(np.float32)\n d = json.load(open(path))\n C = d[\"centroids\"] if isinstance(d, dict) else d\n return np.array(C, dtype=np.float32)\n\n\ndef centroids_id(C):\n \"\"\"A short fingerprint of a set of centroids (sha256 of the float32 values), so a labels file\n and a themes file can be checked against each other.\"\"\"\n import hashlib\n import numpy as np\n return \"sha256:\" + hashlib.sha256(np.ascontiguousarray(np.asarray(C, dtype=np.float32)).tobytes()).hexdigest()[:16]\n\n\ndef save_centroids(path, C, **meta):\n \"\"\"Each value is written as the shortest decimal that reads back as the same float32.\"\"\"\n import numpy as np\n C = np.asarray(C, dtype=np.float32)\n rows = \",\\n \".join(\"[\" + \",\".join(np.format_float_positional(x, unique=True, trim=\"-\") for x in row) + \"]\" for row in C)\n head = json.dumps({**meta, \"id\": centroids_id(C), \"k\": int(C.shape[0]), \"dim\": int(C.shape[1])})[1:-1]\n with open(path, \"w\") as f:\n f.write(\"{\" + head + ',\\n \"centroids\": [\\n ' + rows + \"\\n ]\\n}\\n\")\n","scripts/cluster.py","#!/usr/bin/env python3\n\"\"\"Group items by meaning: k-means on one sample, nearest centroid for any other.\n\nFit (the reference sample):\n python3 cluster.py fit SAMPLE_DIR --k 40 [--n-init 6] [--seed 0] [--show 7] [--centroids-out FILE]\n [--match-to OLD_centroids.json]\n -> SAMPLE_DIR/labels.json {\"k\", \"labels\", \"centroids_id\"} and SAMPLE_DIR/centroids.json\n Prints each cluster with its size, reach and the items nearest its centre, so a person can\n name the clusters and group them into themes (see examples/*/themes.json).\n --match-to renumbers the new clusters to line up with older centroids (Hungarian matching on\n cosine similarity) and prints how close each pair is. Even then the new clusters are not the\n old ones: a themes file names one set of centroids, and analyze.py refuses a themes file\n whose centroids_id differs from the labels'. To reuse old themes on new data, assign.\n\nAssign (any other sample, to saved centroids):\n python3 cluster.py assign SAMPLE_DIR --centroids centroids.json\n -> SAMPLE_DIR/labels.json\n\nConvert saved centroids between .npy and JSON:\n python3 cluster.py convert IN.npy OUT.json (or IN.json OUT.npy)\n\nk-means is sklearn's KMeans with k-means++ init. The saved centroids are the mean embedding of\neach cluster's items, unit-normalised. Assignment picks the highest cosine similarity.\ncentroids.json is plain text: each value is the shortest decimal that reads back as the same\nfloat32, so a JSON round trip gives exactly the same labels as the .npy.\n\"\"\"\nimport argparse\nimport json\nimport os\nimport sys\n\nimport numpy as np\n\nsys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))\nfrom common import centroids_id, load_centroids, save_centroids # noqa: E402\n\n\ndef show(life, E, labels, centres, n):\n for c in np.argsort(-np.bincount(labels, minlength=len(centres))):\n idx = np.where(labels == c)[0]\n if not len(idx):\n continue\n files = len({life[i][\"repo\"] for i in idx})\n alive = np.mean([life[i][\"died\"] is None for i in idx])\n print(f\"\\n#{c} {len(idx)} items in {files} files, still there {alive:.0%}\")\n for i in idx[np.argsort(-(E[idx] @ centres[c]))[:n]]:\n print(\" -\", life[i][\"text\"][:110])\n\n\ndef main():\n ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)\n ap.add_argument(\"mode\", choices=[\"fit\", \"assign\", \"convert\"])\n ap.add_argument(\"sample\", help=\"SAMPLE_DIR (convert: the input file)\")\n ap.add_argument(\"target\", nargs=\"?\", help=\"convert: the output file\")\n ap.add_argument(\"--k\", type=int, default=40)\n ap.add_argument(\"--n-init\", type=int, default=6)\n ap.add_argument(\"--seed\", type=int, default=0)\n ap.add_argument(\"--centroids\", help=\"assign: centroids.json or .npy\")\n ap.add_argument(\"--centroids-out\", help=\"fit: where to write the centroids (default SAMPLE_DIR/centroids.json)\")\n ap.add_argument(\"--show\", type=int, default=7, help=\"fit: items to print per cluster (0 = none)\")\n ap.add_argument(\"--match-to\", help=\"fit: renumber clusters to match these centroids (Hungarian on cosine)\")\n a = ap.parse_args()\n\n if a.mode == \"convert\":\n C = load_centroids(a.sample)\n if a.target.endswith(\".npy\"):\n np.save(a.target, C)\n else:\n save_centroids(a.target, C)\n back = load_centroids(a.target)\n print(f\"wrote {a.target}: {C.shape}, round trip exact: {bool(np.array_equal(back, C))}\")\n return\n\n life = json.load(open(os.path.join(a.sample, \"rules.json\")))[\"life\"]\n E = np.load(os.path.join(a.sample, \"emb.npy\"))\n if len(E) != len(life):\n raise SystemExit(\"emb.npy does not match rules.json; run embed.py again\")\n\n if a.mode == \"fit\":\n from sklearn.cluster import KMeans\n if len(E) < a.k:\n raise SystemExit(f\"{len(E)} items cannot make {a.k} clusters; lower --k\")\n km = KMeans(a.k, n_init=a.n_init, random_state=a.seed).fit(E)\n labels = km.labels_\n C = np.stack([E[labels == k].mean(0) for k in range(a.k)])\n C /= np.linalg.norm(C, axis=1, keepdims=True)\n centres = km.cluster_centers_\n if a.match_to:\n from scipy.optimize import linear_sum_assignment\n old = load_centroids(a.match_to)\n if old.shape != C.shape:\n raise SystemExit(f\"--match-to has shape {old.shape}, the fit {C.shape}\")\n sim = old @ C.T # [old, new]\n rows, cols = linear_sum_assignment(-sim)\n perm = np.empty(a.k, dtype=int)\n perm[cols] = rows # new cluster cols[i] becomes old id rows[i]\n labels = perm[labels]\n order = np.argsort(perm)\n C, centres = C[order], centres[order]\n s = sim[rows, cols]\n same = int(np.sum(np.abs(old - C).max(1) == 0))\n print(f\"matched to {a.match_to}: cosine per pair min {s.min():.3f}, median {np.median(s):.3f}; \"\n f\"{same} of {a.k} centroids identical\", flush=True)\n if same < a.k:\n print(\"warning: the new clusters are not the old ones. A themes file written for the old \"\n \"centroids will be refused by analyze.py; assign to the old centroids instead, or \"\n \"rename the clusters for this fit.\", flush=True)\n out = a.centroids_out or os.path.join(a.sample, \"centroids.json\")\n if out.endswith(\".npy\"):\n np.save(out, C.astype(np.float32))\n else:\n save_centroids(out, C, n_init=a.n_init, seed=a.seed)\n k = a.k\n cid = centroids_id(C.astype(np.float32))\n else:\n if not a.centroids:\n raise SystemExit(\"assign needs --centroids\")\n C = load_centroids(a.centroids)\n labels = (E / np.linalg.norm(E, axis=1, keepdims=True) @ C.T).argmax(1)\n k = len(C)\n cid = centroids_id(C)\n json.dump({\"k\": k, \"labels\": [int(x) for x in labels], \"centroids_id\": cid}, open(os.path.join(a.sample, \"labels.json\"), \"w\"))\n print(f\"{a.mode}: {len(labels)} items in {k} clusters -> {a.sample}/labels.json\", flush=True)\n if a.mode == \"fit\" and a.show:\n show(life, E, labels, centres, a.show)\n\n\nif __name__ == \"__main__\":\n main()\n","scripts/analyze.py","#!/usr/bin/env python3\n\"\"\"Turn item lives into trends: read each file at an age, bin by period, pool samples, map\nclusters to themes, label rise/fall/steady, compare before/after a date, and count the share\nof one file kind among new files.\n\nUsage:\n python3 analyze.py --sample NAME=DIR [--sample NAME=DIR ...] --out OUT\n [--config config.json] [--themes themes.json] [options]\n\nEvery option can also be set in --config (a JSON object whose keys are the option names with\nunderscores, e.g. {\"age\": 30, \"period\": \"quarter\", \"from\": \"2025Q4\"}). The command line wins.\n\nOptions (defaults in brackets):\n --mode cohort|snapshot [cohort] cohort: bin files by the period they were created in, and read\n each file as it stood --age days after creation. snapshot: for every\n period, read every file that existed as it stood on the period's last\n day (deleted files drop out).\n --age N [30] days after creation (cohort mode)\n --created first-item|first-version [first-item] a file's creation date: the birth of its\n earliest item, or its first commit\n --period quarter|month|year [quarter]\n --periods P1,P2,... [every period with data] the bins to report, e.g. 2025Q2,2025Q3\n --from P --to P [first and last bin] the two bins compared for rise/fall\n --threshold X [0.15] a theme rises if its share of a file's items grows by more than\n X (relative), falls if it shrinks by more than X, else steady. A theme\n that starts at 0 and grows is \"rise\". If either bin holds fewer than\n --min-files files, every theme is \"too few\".\n --min-files N [20]\n --before A:B --after C:D date windows [A, B) and [C, D) by creation date for the before/after\n comparison. Or --split DATE: before = every file created earlier,\n after = every file created on DATE or later. In both modes each file in a\n window is read at creation + --age (snapshot mode too: a window compares\n files by when they started, not calendar periods).\n --kinds K1,K2 [every kind, most files first] file kinds to report separately, in this\n order (also the order of the bootstrap draws)\n --share A:B kind A's share of new files among A and B, per period\n --boot N --seed S [3000, 3] bootstrap resamples and numpy default_rng seed\n --today YYYY-MM-DD [the current date] files younger than --age on this day are left out\n of the bins. Set it to make a run repeatable.\n\nEach sample DIR holds rules.json (extract.py) and labels.json (cluster.py). The first --sample\nwins when a file is in more than one (it is counted once when pooling).\n\n --force-themes apply --themes even when its centroids_id differs from the labels'.\n Without it, analyze refuses: cluster numbers from another fit name\n different clusters, and the themes would silently be wrong.\n\nWrites to OUT (empty bins are null, never 0):\n pooled.json {Q, nfiles, tot, med, pf, files_with_rules, overlap}: all samples pooled; per\n period, files, mean and median items per file, and mean items per file in each\n cluster (pf[i][cluster]). Q is the list of periods, whatever the unit.\n themes.json {q, nfiles, tot, med, from_q, to_q, threshold, groups: [{name, kind, v,\n share_from, share_to}], rest, files_with_items, items, samples: {name:\n {history_files, files, files_with_items, items}}}. share_from/share_to are\n FRACTIONS (0.181 = 18.1%). The input to tufte-charts and watercolor-data-art.\n compare.json per sample and kind: \"periods\" (n, mean, median, pf per period) and \"window\"\n (means, medians, and a bootstrap 95% CI of the difference in means, after minus\n before; \"too_few\": true when a side has fewer than --min-files files).\n share.json per sample (with --share): new files per period by kind, counted by each file's\n first commit, whether it has items or not, leaving out files first committed after\n --today; over --periods if given, else every period. \"share\" is in PERCENT\n (87.2 = 87.2%). Also \"files\" (files with a version) and \"history_files\".\n\"\"\"\nimport argparse\nimport collections\nimport json\nimport os\nimport sys\nfrom datetime import date, timedelta\n\nimport numpy as np\n\nsys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))\nfrom common import period_end, period_of, period_range, period_start # noqa: E402\n\n\ndef load(root, k, a, today):\n R = json.load(open(os.path.join(root, \"rules.json\")))\n life = R[\"life\"]\n labels = json.load(open(os.path.join(root, \"labels.json\")))[\"labels\"]\n if len(labels) != len(life):\n raise SystemExit(f\"{root}: labels.json does not match rules.json\")\n files = R.get(\"files\", {})\n first, kind = {}, {}\n for r in life:\n first[r[\"repo\"]] = min(first.get(r[\"repo\"], \"9999\"), r[\"born\"][:10])\n kind[r[\"repo\"]] = r[\"kind\"]\n if a.created == \"first-version\":\n for f in first:\n key = f[: -len(\".tsv.gz\")] if f.endswith(\".tsv.gz\") else f[: -len(\".tsv\")]\n if key in files:\n first[f] = files[key][\"first\"][:10]\n deleted = {}\n for f in first:\n key = f[: -len(\".tsv.gz\")] if f.endswith(\".tsv.gz\") else f[: -len(\".tsv\")]\n deleted[f] = (files.get(key, {}).get(\"deleted\") or \"9999\")[:10]\n return {\"first\": first, \"kind\": kind, \"life\": life, \"labels\": labels, \"deleted\": deleted,\n \"files\": files, \"nrules\": len(life), \"k\": k, \"age\": a.age, \"today\": today}\n\n\ndef count_at(D, when):\n \"\"\"Items alive per file on a date: when(file) -> 'YYYY-MM-DD' or None (file not counted).\"\"\"\n k = D[\"k\"]\n cnt = {f: 0 for f in D[\"first\"]}\n cl = {f: np.zeros(k) for f in D[\"first\"]}\n at = {f: when(f) for f in D[\"first\"]}\n for r, l in zip(D[\"life\"], D[\"labels\"]):\n t = at[r[\"repo\"]]\n if t and r[\"born\"][:10] <= t and (r[\"died\"] or \"9999\")[:10] > t:\n cnt[r[\"repo\"]] += 1\n cl[r[\"repo\"]][l] += 1\n return cnt, cl\n\n\ndef window(s):\n a, b = s.split(\":\")\n return a, b\n\n\ndef main():\n pre = argparse.ArgumentParser(add_help=False)\n pre.add_argument(\"--config\")\n c, _ = pre.parse_known_args()\n ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)\n ap.add_argument(\"--config\")\n ap.add_argument(\"--sample\", action=\"append\", required=True, help=\"NAME=DIR, repeatable\")\n ap.add_argument(\"--themes\")\n ap.add_argument(\"--out\", required=True)\n ap.add_argument(\"--mode\", default=\"cohort\", choices=[\"cohort\", \"snapshot\"])\n ap.add_argument(\"--today\", default=date.today().isoformat())\n ap.add_argument(\"--age\", type=int, default=30)\n ap.add_argument(\"--created\", default=\"first-item\", choices=[\"first-item\", \"first-version\"])\n ap.add_argument(\"--period\", default=\"quarter\", choices=[\"quarter\", \"month\", \"year\"])\n ap.add_argument(\"--periods\")\n ap.add_argument(\"--from\", dest=\"from_\", metavar=\"P\")\n ap.add_argument(\"--to\", metavar=\"P\")\n ap.add_argument(\"--threshold\", type=float, default=0.15)\n ap.add_argument(\"--min-files\", type=int, default=20)\n ap.add_argument(\"--before\")\n ap.add_argument(\"--after\")\n ap.add_argument(\"--split\")\n ap.add_argument(\"--kinds\")\n ap.add_argument(\"--share\")\n ap.add_argument(\"--boot\", type=int, default=3000)\n ap.add_argument(\"--seed\", type=int, default=3)\n ap.add_argument(\"--force-themes\", action=\"store_true\", help=\"apply the themes file even if its centroids_id differs from the labels'\")\n if c.config:\n cfg = json.load(open(c.config))\n cfg = {(\"from_\" if k == \"from\" else k.replace(\"-\", \"_\")): (\",\".join(v) if isinstance(v, list) else v)\n for k, v in cfg.items() if not k.startswith(\"_\")}\n ap.set_defaults(**cfg)\n a = ap.parse_args()\n\n os.makedirs(a.out, exist_ok=True)\n today = date.fromisoformat(a.today)\n samples = [(s.split(\"=\", 1)[0], s.split(\"=\", 1)[1]) for s in a.sample]\n for name, root in samples:\n for f, step in ((\"rules.json\", \"extract.py\"), (\"labels.json\", \"cluster.py fit or assign\")):\n if not os.path.exists(os.path.join(root, f)):\n raise SystemExit(f\"{root}/{f} is missing: run {step} on this sample first\")\n labs = {name: json.load(open(os.path.join(root, \"labels.json\"))) for name, root in samples}\n k_lab = max(l[\"k\"] for l in labs.values())\n ids = {name: l.get(\"centroids_id\") for name, l in labs.items()}\n if len(set(ids.values())) > 1:\n msg = \"samples were labelled against different centroids: \" + \", \".join(f\"{n} {i}\" for n, i in ids.items())\n if not a.force_themes:\n raise SystemExit(msg + \". Assign every sample to the same centroids file (cluster.py assign), or pass --force-themes.\")\n print(\"warning: \" + msg)\n if a.themes:\n cfg = json.load(open(a.themes))\n K = max(k_lab, 1 + max(c for t in cfg[\"themes\"] for c in t[\"clusters\"]))\n theme_list = cfg[\"themes\"]\n want, have = cfg.get(\"centroids_id\"), next(iter(ids.values()))\n if want and have and want != have:\n msg = (f\"{a.themes} names the clusters of centroids {want}, but the labels come from {have}. \"\n \"Cluster numbers from another fit mean different things, so the themes would be wrong\")\n if not a.force_themes:\n raise SystemExit(msg + \". Assign the samples to the centroids the themes file was written for \"\n \"(cluster.py assign --centroids ...), or write a themes file for this fit. \"\n \"--force-themes overrides.\")\n print(\"warning: \" + msg)\n elif not want:\n print(f\"note: {a.themes} has no centroids_id, so it cannot be checked against the labels\")\n else:\n K = k_lab\n theme_list = [{\"name\": f\"cluster {c}\", \"clusters\": [c]} for c in range(K)]\n data = {name: load(root, K, a, today) for name, root in samples}\n per = lambda d: period_of(d, a.period)\n\n # cohort: each file at creation + age; snapshot: each file at the end of every period\n for D in data.values():\n if a.mode == \"cohort\":\n when = lambda f, D=D: (date.fromisoformat(D[\"first\"][f]) + timedelta(days=a.age)).isoformat()\n D[\"cnt\"], D[\"cl\"] = count_at(D, when)\n D[\"ok\"] = {f for f, d in D[\"first\"].items() if date.fromisoformat(d) + timedelta(days=a.age) <= today}\n\n if a.periods:\n Q = a.periods.split(\",\")\n else:\n firsts = [d for D in data.values() for d in D[\"first\"].values()]\n if a.mode == \"snapshot\": # periods that have ended by --today\n p = per(today.isoformat())\n if period_end(p) > today:\n p = per((period_start(p) - timedelta(days=1)).isoformat())\n last = p\n else:\n ok = [d for D in data.values() for f, d in D[\"first\"].items() if f in D[\"ok\"]]\n if not ok:\n raise SystemExit(f\"no file is {a.age} days old by {a.today}\")\n last = per(max(ok))\n Q = period_range(per(min(firsts)), last, a.period)\n\n def bin_files(D, q):\n \"\"\"(files, counts, cluster counts) for one sample in one period.\"\"\"\n if a.mode == \"cohort\":\n fs = [f for f in D[\"first\"] if per(D[\"first\"][f]) == q and f in D[\"ok\"]]\n return fs, D[\"cnt\"], D[\"cl\"]\n end = period_end(q).isoformat()\n fs = [f for f in D[\"first\"] if D[\"first\"][f] <= end and D[\"deleted\"][f] > end]\n cnt, cl = count_at(D, lambda f: end)\n return fs, cnt, cl\n\n # ---- per sample, per kind: periods and the before/after windows\n rng = np.random.default_rng(a.seed)\n\n def ci(x, y):\n bs = [rng.choice(y, len(y)).mean() - rng.choice(x, len(x)).mean() for _ in range(a.boot)]\n return [round(float(v), 1) for v in np.percentile(bs, [2.5, 97.5])]\n\n if a.split:\n b0, b1, a0, a1 = \"0000-00-00\", a.split, a.split, \"9999-99-99\" # open-ended; printed as \"before/from DATE\"\n elif a.before and a.after:\n (b0, b1), (a0, a1) = window(a.before), window(a.after)\n else:\n b0 = b1 = a0 = a1 = None\n if a.kinds:\n KINDS = a.kinds.split(\",\")\n else:\n kc = collections.Counter(D[\"kind\"][f] for D in data.values() for f in D[\"first\"])\n KINDS = sorted(kc, key=lambda x: (-kc[x], x))\n if a.split:\n wb, wa = f\"created before {a.split}\", f\"created {a.split} or later\"\n else:\n wb, wa = f\"{b0}..{b1}\", f\"{a0}..{a1}\"\n compare = {\"Q\": Q, \"today\": a.today, \"mode\": a.mode, \"age_days\": a.age, \"before\": [None, a.split] if a.split else [b0, b1], \"after\": [a.split, None] if a.split else [a0, a1]}\n for name, _ in samples:\n D = data[name]\n res = {\"files_with_rules\": len(D[\"first\"]), \"rules\": D[\"nrules\"]}\n print(f\"== {name}: {len(D['first'])} files with items, {D['nrules']} items\")\n for kd in KINDS:\n rows = []\n for q in Q:\n allf, cnt, cl = bin_files(D, q)\n fs = [f for f in allf if D[\"kind\"][f] == kd]\n n = len(fs)\n rows.append({\"n\": n,\n \"tot\": sum(cnt[f] for f in fs) / n if n else None,\n \"med\": float(np.median([cnt[f] for f in fs])) if n else None,\n \"pf\": (sum((cl[f] for f in fs), np.zeros(K)) / n).tolist() if n else None})\n w = {}\n if b0 is not None:\n if a.mode == \"cohort\":\n cnt = D[\"cnt\"]\n else: # snapshot mode still compares files by creation, each read at creation + age\n cnt, _ = count_at(D, lambda f: (date.fromisoformat(D[\"first\"][f]) + timedelta(days=a.age)).isoformat())\n x = np.array([cnt[f] for f in D[\"first\"] if D[\"kind\"][f] == kd and b0 <= D[\"first\"][f] < b1])\n y = np.array([cnt[f] for f in D[\"first\"] if D[\"kind\"][f] == kd and a0 <= D[\"first\"][f] < a1])\n w = {\"n_before\": len(x), \"n_after\": len(y)}\n if len(x) and len(y):\n w.update(mean_before=float(x.mean()), mean_after=float(y.mean()),\n median_before=float(np.median(x)), median_after=float(np.median(y)),\n diff=round(float(y.mean() - x.mean()), 1), ci95=ci(x, y))\n print(f\" {kd} {wb} -> {wa}: n {len(x)} -> {len(y)}, mean {x.mean():.1f} -> {y.mean():.1f}, \"\n f\"median {np.median(x)} -> {np.median(y)}, diff {w['diff']:+.1f} CI {w['ci95']}\")\n if min(len(x), len(y)) < a.min_files:\n w[\"too_few\"] = True\n print(f\" note: fewer than {a.min_files} files on one side of the {kd} window; too few to tell\")\n res[kd] = {\"periods\": rows, \"window\": w}\n compare[name] = res\n json.dump(compare, open(os.path.join(a.out, \"compare.json\"), \"w\"), indent=1)\n\n # ---- pooled across samples, a file counted once (first sample wins)\n owner = {}\n for name, _ in samples:\n for f in data[name][\"first\"]:\n owner.setdefault(f, name)\n overlap = sum(len(data[n][\"first\"]) for n, _ in samples) - len(owner)\n pooled = {\"Q\": Q, \"nfiles\": [], \"tot\": [], \"med\": [], \"pf\": [], \"files_with_rules\": len(owner), \"overlap\": overlap}\n for q in Q:\n vals, cls = [], []\n for name, _ in samples:\n D = data[name]\n fs, cnt, cl = bin_files(D, q)\n for f in fs:\n if owner[f] == name:\n vals.append(cnt[f])\n cls.append(cl[f])\n n = len(vals)\n pooled[\"nfiles\"].append(n)\n pooled[\"tot\"].append(sum(vals) / n if n else None)\n pooled[\"med\"].append(float(np.median(vals)) if n else None)\n pooled[\"pf\"].append((sum(cls, np.zeros(K)) / n).tolist() if n else None)\n json.dump(pooled, open(os.path.join(a.out, \"pooled.json\"), \"w\"))\n print(f\"== pooled ({a.mode}): {len(owner)} files with items ({overlap} in more than one sample)\")\n for i, q in enumerate(Q):\n if pooled[\"nfiles\"][i]:\n print(f\" {q}: files {pooled['nfiles'][i]:5d} mean {pooled['tot'][i]:5.2f} median {pooled['med'][i]}\")\n else:\n print(f\" {q}: no files\")\n\n # ---- themes\n qf, qt = a.from_ or Q[0], a.to or Q[-1]\n i0, i1 = Q.index(qf), Q.index(qt)\n few = min(pooled[\"nfiles\"][i0], pooled[\"nfiles\"][i1]) < a.min_files\n named, groups = set(), []\n for t in theme_list:\n v = [sum(pf[c] for c in t[\"clusters\"]) if pf is not None else None for pf in pooled[\"pf\"]]\n t0, t1 = pooled[\"tot\"][i0] or 0.0, pooled[\"tot\"][i1] or 0.0\n # an empty compared bin has no share at all: null, never 0\n s0 = v[i0] / t0 if pooled[\"nfiles\"][i0] and t0 else (0.0 if pooled[\"nfiles\"][i0] else None)\n s1 = v[i1] / t1 if pooled[\"nfiles\"][i1] and t1 else (0.0 if pooled[\"nfiles\"][i1] else None)\n if few or s0 is None or s1 is None:\n kind = \"too few\"\n elif s0 == 0:\n kind = \"rise\" if s1 > 0 else \"steady\"\n else:\n change = s1 / s0 - 1\n kind = \"rise\" if change > a.threshold else \"fall\" if change < -a.threshold else \"steady\"\n groups.append({\"name\": t[\"name\"], \"kind\": kind, \"v\": v, \"share_from\": s0, \"share_to\": s1})\n named |= set(t[\"clusters\"])\n rest = [pooled[\"tot\"][i] - sum(g[\"v\"][i] for g in groups) if pooled[\"nfiles\"][i] else None for i in range(len(Q))]\n samples_info = {}\n for name, root in samples:\n R_ = json.load(open(os.path.join(root, \"rules.json\")))\n samples_info[name] = {\"history_files\": R_.get(\"nhistory\", R_[\"nfiles\"]), \"files\": R_[\"nfiles\"],\n \"files_with_items\": len(data[name][\"first\"]), \"items\": data[name][\"nrules\"]}\n themes = {\"q\": Q, \"nfiles\": pooled[\"nfiles\"], \"tot\": pooled[\"tot\"], \"med\": pooled[\"med\"],\n \"files_with_items\": len(owner), \"items\": sum(d[\"nrules\"] for d in data.values()), \"samples\": samples_info,\n \"from_q\": qf, \"to_q\": qt, \"threshold\": a.threshold, \"groups\": groups, \"rest\": rest}\n json.dump(themes, open(os.path.join(a.out, \"themes.json\"), \"w\"), indent=1)\n print(f\"== themes, share of a file's items {qf} -> {qt}\" + (f\" (too few files: fewer than {a.min_files} in a compared bin)\" if few else \"\"))\n for g in groups[::-1]:\n pct = lambda x: \" – \" if x is None else f\"{x * 100:5.1f}%\"\n print(f\" {g['kind']:7} {g['name'][:42]:42} {pct(g['share_from'])} -> {pct(g['share_to'])}\")\n if len(named) < K:\n print(f\" ({K - len(named)} clusters in no theme; their items are in 'rest')\")\n\n # ---- share of kind A among new files of kinds A and B\n if a.share:\n A, B = a.share.split(\":\")\n share = {}\n for name, root in samples:\n files = json.load(open(os.path.join(root, \"rules.json\"))).get(\"files\")\n if files is None:\n raise SystemExit(f\"{root}/rules.json has no 'files'; run extract.py from this skill\")\n fc = {k: (v[\"kind\"], v[\"first\"]) for k, v in files.items() if v[\"first\"][:10] <= a.today}\n qs = Q if a.periods else sorted({per(d) for _, d in fc.values()})\n c = collections.Counter((kd, per(d)) for kd, d in fc.values())\n row = {\"Q\": qs, \"files\": len(fc), \"history_files\": samples_info[name][\"history_files\"]}\n for kd in dict.fromkeys(KINDS + [A, B]):\n row[kd] = [c[(kd, q)] for q in qs]\n row[\"share_of\"], row[\"share_vs\"] = A, B\n row[\"share\"] = [round(100 * c[(A, q)] / (c[(A, q)] + c[(B, q)]), 1) if c[(A, q)] + c[(B, q)] else None for q in qs]\n share[name] = row\n print(f\"== {name}: {A} share of new {A} + {B} files: \" + \", \".join(f\"{q} {s}%\" if s is not None else f\"{q} –\" for q, s in zip(qs, row[\"share\"])))\n json.dump(share, open(os.path.join(a.out, \"share.json\"), \"w\"), indent=1)\n\n\nif __name__ == \"__main__\":\n main()\n","numpy>=1.24\nscikit-learn>=1.3\nfastembed>=0.3\n# optional alternative to fastembed (vectors differ slightly):\n# sentence-transformers>=2.2\n","examples/readme-sections/themes.json","{\n \"_note\": \"Names for one k=6 fit of README headings from the five repos in github-file-history/examples/readme-package (collected 2026-10-11). The fit is in centroids.json beside this file; assign new README histories to it to reuse these names.\",\n \"centroids_id\": \"sha256:a53dc00063f14671\",\n \"clusters\": {\n \"0\": \"Usage, example, API\",\n \"1\": \"ky API entries\",\n \"2\": \"How-to sections (configuring CORS)\",\n \"3\": \"mime-types API entries\",\n \"4\": \"chalk API entries\",\n \"5\": \"Install and license\"\n },\n \"themes\": [\n {\n \"name\": \"Install and license\",\n \"clusters\": [\n 5\n ]\n },\n {\n \"name\": \"Usage and examples\",\n \"clusters\": [\n 0\n ]\n },\n {\n \"name\": \"How-to sections\",\n \"clusters\": [\n 2\n ]\n },\n {\n \"name\": \"API reference entries\",\n \"clusters\": [\n 1,\n 3,\n 4\n ]\n }\n ]\n}\n","examples/readme-sections/package_deps.py","\"\"\"An extract.py plugin: each dependency named in a package.json is one item.\n\n python3 extract.py SAMPLE --extractor plugin --plugin package_deps.py:items --kind-map '{\"package\\\\.json$\": \"package.json\"}'\n\nThe function gets one version's file body and returns a list of item strings.\nBodies that are not package.json (or not valid JSON) give no items.\n\"\"\"\nimport json\n\n\ndef items(body):\n try:\n pkg = json.loads(body)\n except ValueError:\n return []\n if not isinstance(pkg, dict):\n return []\n out = []\n for field in (\"dependencies\", \"devDependencies\", \"peerDependencies\"):\n for name in (pkg.get(field) or {}):\n out.append(f\"{field} {name}\")\n return out\n","examples/readme-sections/config.json","{\n \"_note\": \"README section headings over time, each README read as it stood at the end of every year (snapshot mode). Extract with: --extractor regex --regex '^#{1,3}\\\\s+(.+)' --kind-map '{\\\"(?i)^readme\\\": \\\"README\\\"}'\",\n \"mode\": \"snapshot\",\n \"period\": \"year\",\n \"periods\": [\"2014\", \"2016\", \"2018\", \"2020\", \"2022\", \"2024\", \"2025\"],\n \"from\": \"2016\",\n \"to\": \"2025\",\n \"threshold\": 0.15,\n \"min_files\": 3,\n \"kinds\": [\"README\"],\n \"today\": \"2026-10-11\"\n}\n","examples/readme-sections/centroids.json","{\"n_init\": 6, \"seed\": 0, \"id\": \"sha256:a53dc00063f14671\", \"k\": 6, \"dim\": 384,\n \"centroids\": [\n 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\"_note\": \"Names for the 4 clusters in centroids.json beside this file (one k=4 fit of the made-up CONTRIBUTING.md rules). Valid only for labels assigned to those centroids.\",\n \"centroids_id\": \"sha256:2e25c70679e2e79d\",\n \"clusters\": {\n \"0\": \"Git: main branch, commits, secrets\",\n \"1\": \"Tests\",\n \"2\": \"Docs and changelog\",\n \"3\": \"Code style\"\n },\n \"themes\": [\n {\n \"name\": \"Code style\",\n \"clusters\": [\n 3\n ]\n },\n {\n \"name\": \"Tests\",\n \"clusters\": [\n 1\n ]\n },\n {\n \"name\": \"Docs and changelog\",\n \"clusters\": [\n 2\n ]\n },\n {\n \"name\": \"Git: main branch, commits, secrets\",\n \"clusters\": [\n 0\n ]\n }\n ]\n}\n","examples/made-up/config.json","{\n \"_note\": \"Smoke-test settings for the made-up CONTRIBUTING.md histories. Bins are half-years' worth of quarters with very few files, so min_files is 2 here; real data wants 20.\",\n \"mode\": \"cohort\",\n \"age\": 30,\n \"period\": \"quarter\",\n \"from\": \"2025Q2\",\n \"to\": \"2026Q2\",\n \"threshold\": 0.15,\n \"min_files\": 2,\n \"split\": \"2025-10-01\",\n \"kinds\": [\n \"CONTRIBUTING.md\"\n ],\n \"today\": \"2026-10-11\"\n}\n","examples/made-up/centroids.json","{\"n_init\": 6, \"seed\": 0, \"id\": \"sha256:2e25c70679e2e79d\", \"k\": 4, \"dim\": 384,\n \"centroids\": [\n 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]\n}\n","examples/made-up/NOTICE","These histories are made up for this skill's smoke test: twelve imaginary projects, each\nstarting a CONTRIBUTING.md and editing it a few times. Later projects write fewer style rules\nand more git rules, so the trend has a known shape. Same MIT licence as the skill.\nFormat: one row per version, newest first: sha author date 0 base64 body.\n","SKILL.md","LICENSE","MIT License\n\nCopyright (c) 2026 agentleFS\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n","clerkState",{"_136":137},"__internal_clerk_state",{"_138":139,"_140":141,"_142":143,"_144":143,"_145":20,"_146":143,"_147":143,"_148":-7,"_149":-7,"_150":-7,"_151":-7,"_152":153,"_154":-7,"_155":-7,"_156":-7,"_157":-7,"_158":-7,"_159":20,"_160":20,"_161":20},"__clerk_ssr_state",{"_171":172,"_173":-5,"_174":-5,"_175":-5,"_176":-5,"_177":-5,"_178":-5,"_179":-5,"_180":-5,"_181":-5,"_182":-5,"_183":184,"_185":186,"_187":188,"_163":20},"__publishableKey","pk_live_Y2xlcmsuYWdlbnRsZWZzLmNvbSQ","__proxyUrl","","__domain","__isSatellite","__signInUrl","__signUpUrl","__signInForceRedirectUrl","__signUpForceRedirectUrl","__signInFallbackRedirectUrl","__signUpFallbackRedirectUrl","__clerk_debug",{"_162":20,"_163":20,"_164":143,"_165":166,"_167":143,"_168":141,"_169":20,"_170":143},"__clerkJSUrl","__clerkJSVersion","__clerkUIUrl","__clerkUIVersion","__prefetchUI","__telemetryDisabled","__telemetryDebug","__unsafeDisableDevelopmentModeConsoleWarning","isSignedIn","isAuthenticated","proxyUrl","reason","session-token-and-uat-missing","message","publishableKey","isSatellite","domain","tokenType","session_token","sessionClaims","sessionId","sessionStatus","userId","actor","orgId","orgRole","orgSlug","orgPermissions","factorVerificationAge","getToken",["SingleFetchFallback"],"has",["SingleFetchFallback"],"debug",["SingleFetchFallback"]]