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
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 carryingfact,source_session,date, andcategory(one ofdecision,convention,constraint,resolved-problem,project-knowledge). - Update scope or focus filter (optional).
Procedure
- 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.
- Read the current AGENTS.md and
.continual-learning/index.jsonto establish the existing memory baseline. Done when: the existing memory baseline is read and the current set of recorded facts is known. - 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.
- 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.
- 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.
- 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.jsonas local writes only. Done when: the high-signal updates are written and the prior state is captured. - 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)
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agents
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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
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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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