agentleFS
Sign inStart free
docxologydocxology/template/.cursor/skills/continual-learning/SKILL.md

continual-learning

Skill20 starsChanged 2 months agoApache-2.0View on GitHub

Orchestrate continual learning by delegating transcript mining to agents-memory-updater and writing durable memory to local JSON (never root AGENTS.md Learned sections). USE WHEN continual learning, mine prior chats, maintain agent memory, or the stop hook triggers this skill.

We split this skill into the rules it gives an agent

7 rules. 0 are common, 4 are distinctive, and 3 are very specific to this project.

  • 0

    Common

    Most of the 3 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Keep durable agent memory in local JSON, not in root AGENTS.md.

    line 9
  2. Distinctive:

    Use when the user asks to mine prior chats, maintain agent memory, or run the continual-learning loop (including stop-hook followups).

    line 13
  3. Very specific:

    Call the agents-memory-updater subagent (repo override at .cursor/agents/agents-memory-updater.md).

    line 17
  4. Distinctive:

    Keep this skill orchestration-only — do not mine transcripts or edit files in the parent flow.

    line 28
  5. Distinctive:

    Do not bypass the subagent.

    line 29
  6. Very specific:

    Never: add or edit ## Learned User Preferences or ## Learned Workspace Facts in root AGENTS.md (public-repo contract).

    line 30
  7. Very specific:

    Do not commit continual-learning-memory.json or continual-learning-index.json.

    line 31

“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Continual Learning (template repo)

Keep durable agent memory in local JSON, not in root AGENTS.md.

Trigger

Use when the user asks to mine prior chats, maintain agent memory, or run the continual-learning loop (including stop-hook followups).

Workflow

  1. Call the agents-memory-updater subagent (repo override at .cursor/agents/agents-memory-updater.md).
  2. Return the updater result verbatim.

Target file

  • Memory: .cursor/hooks/state/continual-learning-memory.json
  • Schema example: .cursor/hooks/state/continual-learning-memory.example.json
  • Optional helpers: infrastructure.core.agent_memory

Guardrails

  • Keep this skill orchestration-only — do not mine transcripts or edit files in the parent flow.
  • Do not bypass the subagent.
  • Never add or edit ## Learned User Preferences or ## Learned Workspace Facts in root AGENTS.md (public-repo contract).
  • Do not commit continual-learning-memory.json or continual-learning-index.json.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

Reviews can't be read right now.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free