Claude Skill

continual-learning

Use when asked to mine prior chats on a scheduled or watcher tick and maintain project memory. Not for remote, credential, publish, deploy, or irreversible mutation.

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Download outlinedriven-outline-driven-development-.devin_skills_continual-learning-b0e8ce8.zip · 1 KB
Part of outlinedriven/outline-driven-development — 145 skills

Install

skills CLI npx skills add https://github.com/OutlineDriven/outline-driven-development/tree/main/.devin/skills/continual-learning
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install outlinedriven-outline-driven-development@llmmart
Git git clone https://github.com/OutlineDriven/outline-driven-development.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole outlinedriven/outline-driven-development collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Continual learning

Contract

Field Bound contract
Trigger A scheduled tick or watcher event fires to mine prior chats and maintain project memory.
Authority Reversible local: writes only AGENTS.md and the continual-learning index; rollback is version control. No remote mutation.
Side effect Updates AGENTS.md and the continual-learning index with deduplicated high-signal memory entries.
Done Deduplicated high-signal memory updates are written, or an explicit no-update result is returned.

Inputs

  • Prior chat transcripts or session logs accessible in the local workspace (required).
  • Existing AGENTS.md (required, read before mutation).
  • The continual-learning index at .continual-learning/index.json (required, read before mutation). The index schema is a JSON object with an array of entries, each carrying fact, source_session, date, and category (one of decision, convention, constraint, resolved-problem, project-knowledge).
  • Update scope or focus filter (optional).

Procedure

  1. On a scheduled tick or watcher event, enumerate accessible prior chat transcripts and session logs in the local workspace. Done when: every accessible transcript and log is enumerated.
  2. Read the current AGENTS.md and .continual-learning/index.json to establish the existing memory baseline. Done when: the existing memory baseline is read and the current set of recorded facts is known.
  3. Extract candidate memory facts from the transcripts: decisions, conventions, constraints, resolved problems, and project-specific knowledge. Done when: candidate facts are extracted from every transcript.
  4. Deduplicate each candidate against the existing baseline; drop entries that duplicate, contradict without new evidence, or restate lower-signal information already recorded. Done when: every candidate is deduplicated against the baseline.
  5. Apply the high-signal gate. A candidate passes when it meets one of: records a decision that changed project direction, establishes a convention or constraint that governs future work, resolves a problem that recurred or is likely to recur, or captures project-specific knowledge not derivable from the codebase. Drop candidates that restate obvious or one-off information. Done when: every surviving candidate is classified and only high-signal entries remain.
  6. Capture the prior state of AGENTS.md and the index before writing, so the update can be rolled back. Apply the deduplicated high-signal updates to AGENTS.md and .continual-learning/index.json as local writes only. Done when: the high-signal updates are written and the prior state is captured.
  7. If no candidate survives deduplication and the gate, record an explicit no-update result. Done when: a no-update result is recorded or updates are applied.

Failure and recovery

  • Unreadable transcript: skip that source, continue with the rest, and report the skipped source in the result.
  • Unreadable index: return a blocked result naming the missing or corrupt index; do not write updates without a baseline.
  • Conflicting evidence between a candidate and an existing entry: do not overwrite; surface the conflict and leave the existing entry unchanged.
  • Partial-result rule: write only the deduplicated subset that resolved cleanly; never write unverified or low-signal entries to meet a quota.
  • Rollback: the prior state captured in step 6 restores AGENTS.md and the index to their pre-update content. Revert by replacing the current files with the captured prior state.
  • Blocked result: if no transcripts are accessible or the index cannot be read, return a blocked result naming the missing input; do not fabricate memory.

Output

Statement of which deduplicated high-signal memory updates were applied to AGENTS.md and .continual-learning/index.json, or that no update was made and why no candidate survived the gate.

Files (outline-driven-development)
  • agents
    • openai.yaml 209 B
      interface:
        display_name: "Continual Learning"
        short_description: "Use when asked to mine prior chats on a scheduled or watcher tick and maintain project memory."
      policy:
        allow_implicit_invocation: false
      
  • SKILL.md 4.1 KB
    ---
    name: continual-learning
    description: 'Use when asked to mine prior chats on a scheduled or watcher tick and maintain project memory. Not for remote, credential, publish, deploy, or irreversible mutation.'
    disable-model-invocation: true
    ---
    
    # Continual learning
    
    ## Contract
    
    | Field | Bound contract |
    |---|---|
    | Trigger | A scheduled tick or watcher event fires to mine prior chats and maintain project memory. |
    | Authority | Reversible local: writes only AGENTS.md and the continual-learning index; rollback is version control. No remote mutation. |
    | Side effect | Updates AGENTS.md and the continual-learning index with deduplicated high-signal memory entries. |
    | Done | Deduplicated high-signal memory updates are written, or an explicit no-update result is returned. |
    
    ## Inputs
    
    - Prior chat transcripts or session logs accessible in the local workspace (required).
    - Existing AGENTS.md (required, read before mutation).
    - The continual-learning index at `.continual-learning/index.json` (required, read before mutation). The index schema is a JSON object with an array of entries, each carrying `fact`, `source_session`, `date`, and `category` (one of `decision`, `convention`, `constraint`, `resolved-problem`, `project-knowledge`).
    - Update scope or focus filter (optional).
    
    ## Procedure
    
    1. On a scheduled tick or watcher event, enumerate accessible prior chat transcripts and session logs in the local workspace. Done when: every accessible transcript and log is enumerated.
    2. Read the current AGENTS.md and `.continual-learning/index.json` to establish the existing memory baseline. Done when: the existing memory baseline is read and the current set of recorded facts is known.
    3. Extract candidate memory facts from the transcripts: decisions, conventions, constraints, resolved problems, and project-specific knowledge. Done when: candidate facts are extracted from every transcript.
    4. Deduplicate each candidate against the existing baseline; drop entries that duplicate, contradict without new evidence, or restate lower-signal information already recorded. Done when: every candidate is deduplicated against the baseline.
    5. Apply the high-signal gate. A candidate passes when it meets one of: records a decision that changed project direction, establishes a convention or constraint that governs future work, resolves a problem that recurred or is likely to recur, or captures project-specific knowledge not derivable from the codebase. Drop candidates that restate obvious or one-off information. Done when: every surviving candidate is classified and only high-signal entries remain.
    6. Capture the prior state of AGENTS.md and the index before writing, so the update can be rolled back. Apply the deduplicated high-signal updates to AGENTS.md and `.continual-learning/index.json` as local writes only. Done when: the high-signal updates are written and the prior state is captured.
    7. If no candidate survives deduplication and the gate, record an explicit no-update result. Done when: a no-update result is recorded or updates are applied.
    
    ## Failure and recovery
    
    - Unreadable transcript: skip that source, continue with the rest, and report the skipped source in the result.
    - Unreadable index: return a blocked result naming the missing or corrupt index; do not write updates without a baseline.
    - Conflicting evidence between a candidate and an existing entry: do not overwrite; surface the conflict and leave the existing entry unchanged.
    - Partial-result rule: write only the deduplicated subset that resolved cleanly; never write unverified or low-signal entries to meet a quota.
    - Rollback: the prior state captured in step 6 restores AGENTS.md and the index to their pre-update content. Revert by replacing the current files with the captured prior state.
    - Blocked result: if no transcripts are accessible or the index cannot be read, return a blocked result naming the missing input; do not fabricate memory.
    
    ## Output
    
    Statement of which deduplicated high-signal memory updates were applied to AGENTS.md and `.continual-learning/index.json`, or that no update was made and why no candidate survived the gate.
    

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