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Ar9avAr9av/obsidian-wiki/skills/session-search/SKILL.md

session-search

Skill3.7k starsChanged 8 days agoMITView on GitHub

Search the session-brain graph for a past agent session by topic and load the winning transcript into the current conversation. Use for finding which session handled X, when Y was last worked on, or whether this was done before. Read-only; requires a session-brain graph.

14 other projects use a version of this.

Adopted by

Steady: 0 new projects took it in the last 4 weeks, none the 4 weeks before.thin data

Week of Sep 13: 0 new projectsWeek of Sep 20: 0 new projectsWeek of Sep 27: 0 new projectsWeek of Oct 4: 0 new projectsWeek of Oct 11: 0 new projectsWeek of Oct 18: 0 new projectsWeek of Oct 25: 0 new projectsWeek of Nov 1: 0 new projects
Sep 2026Oct 2026Nov 2026
New projects taking a version of this file each week, over the 8 weeks our latest run read: only 0 in all, so this is a hint, not a trend.

Most recently taken up by

Who's talking about Ar9av/obsidian-wiki

The biggest 5 of 27 mentions linking to the repository that our crawl found. Each line is their own headline.

See all 27

Talked about on GitHub and Bluesky (27 times).

On GitHub · 25

20 more on GitHub

On Bluesky · 2

Used with

Projects with it also use ml-training-recipe, replication and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • getcompanion-aiSkill

    ml-training-recipe

    Find implementable ML training recipes from papers, datasets, docs, and code. Use when the user wants to fine-tune, train, reproduce, or choose a practical ML method, dataset, hyperparameter setup, or benchmark recipe.

    Also in 3 projects that use this

  • getcompanion-aiSkill

    replication

    Plan a replication of a paper, claim, or benchmark, and execute only after an explicit environment choice. Use when the user asks to replicate results, reproduce an experiment, verify a claim empirically, or build a replication package.

    Also in 3 projects that use this

  • brainbytes-devSkill

    alpha-research

    Also in 3 projects that use this

  • JaimeParkerSkill

    paper-code-audit

    Perform three-way cross-validation of a research paper by comparing the original paper text (via Zotero), official code implementation (via Git), and personal Obsidian notes. Use this skill whenever the user wants to "audit a paper", "verify paper claims against code", "cross-check notes with implementation", "triangulate paper + code + notes", "find discrepancies between paper and code", or mentions wanting a deep, structured comparison of a paper's claims against its implementation and their own notes. Also trigger when the user asks to "analyze this paper thoroughly" with code available, or when they want to systematically reconcile what the paper says, what the code does, and what their notes claim.

    Also in 3 projects that use this

  • JimLiuSkill

    pdf-explore

    Use this skill when the user has attached a PDF, paper, report, or other document and the answer needs content from more than one place in it: summarize the methods or any other section, compare sections, find where a topic is discussed, read a value or label off a figure or chart, or find/list/extract every instance of something across the whole document (datasets, benchmarks, citations, figures, table rows, accession numbers — including appendices). Skip it only for a single lookup of 1–4 pages quoted in your very next response — `read_file(pages=[...])` attaches pages as images that are dropped from context after one turn, so multi-section answers end up re-reading the same ranges repeatedly. Parses the PDF once in the Python kernel: `pdf_pages` (pages as persistent text), `pdf_outline` (TOC), `pdf_scan` (rank pages by relevance), `pdf_map`/`pdf_extract` (per-page summary / structured fields via parallel haiku calls). For PDF creation/manipulation, use reportlab/pypdf directly.

    Also in 3 projects that use this

  • composio-communitySkill

    weekly-digest

    Generate a weekly support digest and post it to [Slack](https://composio.dev/toolkits/slack)

    Also in 3 projects that use this

  • +1 more from getcompanion-ai

The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 10 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    If this exits 1 with "run sessions-build first", tell the user and offer /session-brain.

    line 17
  2. Distinctive:

    If everything relevant is unloadable, answer from the prompt text that *is* there and say that is all that remains.

    line 44

“Common” is measured: kept by at least 6 in 10 of the 10 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Session Search

Answers "which of my past sessions was about X" and then pulls that session's context in.

Step 1: Check the graph exists and is fresh

obsidian-wiki sessions-query "<topic>" --json

If this exits 1 with "run sessions-build first", tell the user and offer /session-brain. If graph.json is more than ~7 days old, mention it and offer a rebuild — but do not silently rebuild, since that is a multi-second operation the user did not ask for.

Step 2: Rank

The scoring already combines four signals, so take the ordering as given rather than re-ranking:

  • similarity — TF-IDF cosine against the session's text
  • cluster lift — a session inside the best-matching topic scores higher even if its own words never matched. This is why a session that never said "telemetry" can still surface for it.
  • bookmark boost — a human already flagged this session as worth keeping
  • time decay — 90-day half-life, applied with a floor so an old exact match still outranks a fresh weak one

Useful filters: --project NAME, --cluster N, --since DATE, --top N.

Step 3: Present

Show the top ~5 as a compact table — title, project, date, topic, and the why string, which already explains the match. Do not dump the raw JSON at the user.

Two things must be stated honestly rather than glossed over:

  • Entries with loadable: false are history-only: the transcript has been pruned from disk and only the prompts survive. They are listed in unloadable with a reason. Say the transcript is gone; do not imply it can be retrieved.
  • If everything relevant is unloadable, answer from the prompt text that is there and say that is all that remains.

Step 4: Load — hand off, do not reimplement

should_load holds at most 3 session ids worth opening, already filtered to ones with transcripts.

Prefer an existing loader skill if the user has one installed:

  • claude-session-load — loads a Claude session by id
  • bookmark-load — use when the hit is bookmarked

⚠️ Those skills live in the user's personal skills directory (~/.claude/skills/), not in this repo. They may not exist. Do not assume them and do not error if they are absent — fall back to reading the transcript directly:

# the path is in the query result as `transcript`
obsidian-wiki sessions-show <session-id> --pretty

The transcript is JSONL and can be tens of MB. Never read one whole. Filter to the human turns first:

grep -c '' <transcript>   # size check before anything else
python3 -c "
import json,sys
for line in open(sys.argv[1], errors='replace'):
    try: r = json.loads(line)
    except ValueError: continue
    if r.get('type')=='user' and not r.get('isSidechain') and not r.get('isMeta'):
        c = (r.get('message') or {}).get('content')
        if isinstance(c,str) and not c.lstrip().startswith('<'): print('>', c[:400])
" <transcript>

Step 5: Answer

Synthesise from what you loaded and cite the session ids you used. If the answer came from a history-only session's prompts rather than a real transcript, say so — the user needs to know how much of the context you actually have.

Related

  • /session-brain — build or refresh the graph, survey and name topics
  • wiki-query — search compiled vault knowledge; this skill searches raw session history instead
  • wiki-agent — pull a topic out of another agent's history and distil it into the vault

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