skill-miner
Skill3 starsChanged 9 days agoMITView on GitHub
Explicitly selected mining of Codex/Claude/Grok/Pi sessions, memory files, and project context docs for repeated failures, workflow patterns, concrete skill improvement candidates, and memory cleanup candidates after durable repo truth extraction. Use only when the user names it or asks to mine history or memory; current-repository analysis belongs to analyze-project.
6 other projects use a version of this.
Adopted by
Most recently taken up by
- getedgehq/skillstheir own version, first seen Nov 1, 2026
- JPeetz/agent-skillstheir own version, first seen Oct 28, 2026
- nb-99/skillektiontheir own version, first seen Aug 19, 2026
- SohamBanerjee853/skill-minertheir own version, first seen Aug 9, 2026
- Sagart-cactus/skill-minertheir own version, first seen Jul 13, 2026
- AltrinaAI/skill-minertheir own version, first seen Jun 29, 2026
Other versions6 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 6
- JPeetz/agent-skills★ 14same name
- getedgehq/skills★ 5same name
- nb-99/skillektion★ 1same name
- AltrinaAI/skill-miner★ 0same name
- Sagart-cactus/skill-miner★ 0same name
- SohamBanerjee853/skill-miner★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository34 other files it gives its agents.
- AGENTS.mdAGENTS.md
- docs/AGENTS.mdAGENTS.md
- analyze-projectSkill
- api-contract-strategySkill
- architecture-patternsSkill
- close-changeSkill
- design-changeSkill
- development-standardsSkill
- docker-multiarch-buildSkill
- error-patternsSkill
- go-guidelinesSkill
- herdr-implementSkill
- implement-changeSkill
- logging-standardsSkill
- lua-guidelinesSkill
- organize-docsSkill
- output-stylesSkill
- plan-changeSkill
- powershell-guidelinesSkill
- python-guidelinesSkill
- review-changeSkill
- review-designSkill
- review-implementationSkill
- review-planSkill
- routeros-scripting-guidelinesSkill
- runtime-diagnosisSkill
- security-guardrailsSkill
- shell-guidelinesSkill
- skill-authoringSkill
- skills-routingSkill
- smart-commitSkill
- sops-age-guardrailsSkill
- sync-truthSkill
- testing-strategySkill
The file
We split this skill into the rules it gives an agent
7 rules. 0 are common, 6 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 5 edited versions we read keep these.
6
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
- Very specific: line 16
Read Parser Modes for source locations when scope is all-history or multi-home, and for Pi evidence interpretation when Pi sessions are in …
- Distinctive: line 21
Run the bundled parser for structured signals instead of raw-scanning large JSONL files.
- Distinctive: line 30
update an existing generic skill, or add a new one, when the pattern recurs across repositories or task types
- Distinctive: line 31
add or update a repo-local skill when the pattern depends on repository topology, runtime inventory, local hostnames, or domain-specific …
- Distinctive: line 32
add or update scoped repo docs or code truth for stable operational facts
- Distinctive: line 34
do not promote, which covers one-time runtime snapshots that serve only as evidence
- Distinctive: line 56
Treat search no-match exit codes as weak evidence unless followed by user correction.
“Common” is measured: kept by at least 6 in 10 of the 5 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.
Files
Skill Miner
Extract reusable skill improvements from agent history and project context. Mining returns candidates with their source evidence; the user or calling agent adjudicates promotion and memory maintenance and authorizes any separate edit. Stay read-only unless the user explicitly approves skill edits, and with an analysis-only request end with recommendations.
Apply the Promotion Rule in references/memory-boundary.md of skills-routing for how agent memory relates to durable repo truth.
Scope
Default scope is the current Git repository, or the current working directory when no Git root exists. Use all-history scope when the user explicitly asks to search all local agent homes, and include additional agent homes when the user names them. Sources are Codex, Claude, Grok, and Pi sessions and memory files, plus tracked AGENTS.md and README.md project context docs under the target repo.
Read Parser Modes for source locations when scope is all-history or multi-home, and for Pi evidence interpretation when Pi sessions are in scope. Pi scanning reads only sessions/**/*.jsonl; auth, settings, databases, other runtime stores, and subagent private state stay unread.
Workflow
- Confirm the requested scope: current repo, named repo, or all local Codex/Claude/Grok/Pi history.
- Run the bundled parser for structured signals instead of raw-scanning large JSONL files.
- Separate evidence into:
- command failures and tool errors
- interrupted, compacted, or rolled-back turns
- user corrections and scope rejections
- approval-gate mistakes
- memory-recorded failure patterns
- project docs that are large, duplicated, or workflow-heavy enough to mine
- Classify each recurring candidate, naming its source evidence, as:
- update an existing generic skill, or add a new one, when the pattern recurs across repositories or task types
- add or update a repo-local skill when the pattern depends on repository topology, runtime inventory, local hostnames, or domain-specific operational truth
- add or update scoped repo docs or code truth for stable operational facts
- mark extracted memory for cleanup
- do not promote, which covers one-time runtime snapshots that serve only as evidence
- Name a repo doc, repo-local skill, code, or generic skill as the durable owner of each recommendation. Recommend concrete target files and validation commands.
Memory
Mine existing memory to identify missing repo truth, missing skills, stale memory, and cleanup candidates. For each memory-derived finding, decide whether the target repo already owns the durable truth; if so, name the durable target file together with the source memory cleanup action, and recommend removing or shrinking the memory entry after the repo update is verified. List extracted memory entries or task groups as cleanup candidates, cite the repo files that now own the truth, and keep only short pointers when useful for historical lookup. Edit agent memory only on an explicit memory-maintenance request through the active memory workflow; cleanup entries are recommendations.
Parser
Resolve the skill directory from this file and run the bundled read-only parser for the current repository:
SKILL_DIR="$(cd "$(dirname "<path to this SKILL.md>")" && pwd)"
python3 "$SKILL_DIR/scripts/extract-session-signals.py" --scope current --repo-root "$(git rev-parse --show-toplevel)"The script accepts only named parameters, and the default scope stays current. Use Parser Modes when the task needs all-history scope, multiple homes, JSON output, or skill-usage measurement. Raw examples are off by default; set a positive --limit only when bounded session excerpts are required, and treat any excerpt as potentially sensitive because sanitization does not guarantee that arbitrary secrets are removed.
Output Rules
- Lead with counts and the strongest repeated patterns, and include project context signals by default when a repo root is available.
- Quote short user corrections only when they prove a workflow mistake.
- Treat search no-match exit codes as weak evidence unless followed by user correction.
- Report only steps that completed.
Does it work?
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

