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/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.

From plugin
odin-claude-plugin
40200 skills

Claude Code

$ npx -y skills add OutlineDriven/odin-claude-plugin --skill continual-learning --agent claude-code

SKILL.md

continual-learning.SKILL.md
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.

Read more
@outlinedriven
Ships withodin-claude-plugin

Formerly the ODIN Claude Plugin. The repository URL is unchanged. Outline-Driven Development, nicknamed ODIN, is a highly opinionated code-agent skill library: principles-first engineering, surgical editing, and workflow automation, published as installable

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Questions about continual-learning.

Which coding agents can use continual-learning?
continual-learning installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.