Operon mine
Run a fresh mining pass across a user's Claude Code, Codex, and Cursor sessions to surface new recurring prompts as draft operons.From its SKILL.md
npx -y skills add Try-Operon/skills --skill operon-mineAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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Operon — Mine Sessions
operon-setup is the one-time onboarding skill. operon-mine is the
recurring loop: every few weeks (or when the user mentions a new
recurring task), run a mining pass to surface fresh patterns.
When to use this skill
- The user says "mine my sessions" or "find new patterns" or "what has recurred since I last mined."
- It's been more than a couple weeks since their last
operon mine. - The user mentions a new repeated workflow they want to capture.
Do not use this for first-run onboarding — use operon-setup instead.
How to use it
1. Confirm the user is set up
operon doctor --json
If auth.tokenPresent is false, point them at operon-setup. Do not
try to mine without auth — mine won't upload drafts to the platform
without a valid token.
2. Pick a window
Default mining window is 30 days. If the user mined recently and just wants the new stuff, that's right. If they want a broader sweep (after a sabbatical, after onboarding a new tool, etc.), widen it:
operon mine --since 90d
operon mine --since 180d
operon mine --since 365d
Wider windows take longer but surface stable patterns that didn't quite clear the threshold in a 30-day window.
3. Run the mine
For interactive review:
operon mine --since 30d
For agent-driven flow (recommended when an assistant is driving):
operon mine --since 30d --yes --json
--yes accepts every default; --json outputs the candidate list as
a single JSON object on stdout.
4. Parse the output
The JSON shape is:
{
"input": 412,
"kept": 287,
"clusters": 14,
"dryRun": false,
"candidates": [
{
"slug": "review-pr",
"uses": 12,
"spanDays": 14,
"score": 0.91,
"suggestedTools": ["Read", "Bash", "WebFetch"],
"existing": "new"
}
]
}
existing tells you how each candidate compares to what's already
published in the workspace:
new— never publishedsame— already published, no diffchanged— already published, but the draft differsunchecked— couldn't compare (offline / no workspace)
5. Direct the user to review
After mining, drafts auto-upload to
https://app.withoperon.com/<workspace>/candidates. From there the
user can edit, polish, and publish each draft.
Or pipe straight into operon-publish — call that skill next if the
user wants to ship one of the new drafts immediately.
Privacy notes
- Mining reads session files in
~/.claude/,~/.codex/,~/.cursor/. Nothing leaves the laptop without explicit consent. - After redaction, the candidate metadata (slug, score, uses, prompt fingerprint, draft prompt) uploads to the platform's private Drafts slot — visible only to the user who mined them.
- Exemplar session bodies stay on the laptop. The platform only sees opaque session IDs.
- Opt-out:
operon config set candidates.cloudReview falsekeeps the full mining flow local; no drafts upload.
Error handling
NO_SOURCES→ user doesn't have Claude Code / Codex / Cursor installed. Tell them to install at least one and retry.NO_CANDIDATES→ no patterns crossed the clustering threshold. Suggest a wider--sincewindow.RATE_LIMITED→ wait and retry. The mining itself is local — rate limiting hits the draft-upload step. The local on-disk drafts under~/.operon/candidates/are still authoritative.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.