session-learn

Solid

Use when a completed work session should yield durable concepts, corrections, decisions, reusable patterns, and a traceable next action.

AI & Automation 123 stars 18 forks Updated 3 days ago MIT

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Skill Content

# Session Learn <skill_contract> <input>Completed session objective, actions, artifacts, verification receipts, errors, decisions, and destination paths.</input> <output>Deduplicated durable deltas for concepts, entities, corrections, patterns, ideas, decisions, and gaps.</output> <done>Each retained delta is formatted, linked, logged, provenance-preserving, and paired with a traceable next action when unresolved.</done> <non_goals>Automatic-capture claims without a hook, raw transcript storage, conversational filler, or immutable-source edits.</non_goals> Close a session by extracting only reusable deltas. Invocation must be explicit or performed by a verified external hook; this skill never claims it ran automatically. ## Usage Template Provide: session objective, actions, outputs, verification receipts, errors, decisions, and destination paths. Optional: existing notes for deduplication. ## Workflow <intake> Establish the session boundary and compare intended versus observed result. Ignore conversational filler and separate execution evidence from retrospective interpretation. </intake> <unknowns_gate> If the session outcome or evidence is unavailable, return `INSUFFICIENT_EVIDENCE`. Ask for a missing artifact only when it determines whether a lesson is valid; otherwise preserve it as an unresolved gap. </unknowns_gate> <execute> Scan for seven signal types: 1. **Concept** — a stable mechanism worth linking. 2. **Entity** — a person, system, project, ...

Details

Author
Mark393295827
Repository
Mark393295827/third-brain-v5-skills
Created
2 months ago
Last Updated
3 days ago
Language
HTML
License
MIT

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