Learn

Install

npx -y skills add vasu-devs/Forge --skill learn

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Distill durable "instincts" (trigger → action patterns) from this session into project-scoped memory that auto-loads at the start of future sessions in this repo. Use when the user asks you to learn or remember a workflow pattern, after solving something non-obvious worth not re-deriving, at the end of a productive session, or when invoked directly.

SKILL.md

7.9 KB, as published. Nobody here has run it

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Learn instincts from this session

forge's continuous-learning layer. It captures the non-obvious patterns of working in this specific project so future sessions start already knowing them — instead of re-discovering the same conventions, build quirks, and gotchas every time.

How the layer fits together (it runs automatically — you rarely need to invoke this by hand):

A PostToolUse hook silently logs every Edit/Write/Bash action to a project-scoped observations log (deterministic — survives compaction).
A Stop hook (stop-autolearn.js) fires when each turn ends and, once enough new work has accumulated (batched — every FORGE_LEARN_EVERY actions, default 8), spawns a detached background distiller (distill.js) that asks a small model (Haiku) to extract lessons. It never blocks you; the env-guard FORGE_DISTILLER=1 prevents recursion.
The distiller applies a strict quality gate (confidence floor, length/platitude filters, a best-effort anonymization backstop for global lessons, per-run caps) so only sharp, non-bogus knowledge is kept. The manual add/add-global CLI runs the same gate, so the hand path can't sneak past the rules.
A SessionStart hook auto-injects the highest-confidence global lessons (everywhere) + this project's instincts into future sessions (capped; nothing injected where there's nothing learned).

This skill (forge:learn) is the manual/deliberate path — use it to capture a specific lesson on demand, force a distillation now, or curate (prune/refine) what's been learned. The automatic loop handles the steady-state.

All data lives under ~/.claude/forge-data/; nothing is written into the repo.

Two tiers — choose the right one for each lesson

Project instinct (instincts/<projId>.jsonl) — specific to THIS repo: its conventions, build quirks, domain rules, file-specific gotchas. Loads only in this project.
Global lesson (global-lessons.jsonl, also rendered to ~/.claude/forge-data/LESSONS.md) — an anonymized, transferable behavioral correction that should make you better in every project. Loads into every session everywhere.

Anonymization is mandatory for global lessons. A global lesson must contain no project, company, repo, file, path, framework-version, or domain-specific identifier. Phrase it as a general rule. Litmus test: "Would this lesson help on a completely different codebase?" If yes → global. If it only makes sense with this project's specifics → project instinct. When in doubt, write it as a project instinct (cheap) rather than polluting the global layer.

Global lessons are best for mistakes and their corrections: a wrong assumption you made, a verification step you skipped, a process that went sideways — distilled into "when X → do Y instead" so you never repeat it.

Procedure

Run the CLI with the project's working directory as cwd (use $HOME, which the shell expands):

1. Gather evidence.

node "$HOME/.claude/skills/forge/scripts/forge-store.js" observations 60

Read that log, then review the conversation for signals worth keeping:

corrections the user made to your approach
non-obvious decisions and why they went that way
repeated commands/sequences (build, test, run, deploy)
gotchas, footguns, and this repo's specific conventions

2. Distill ATOMIC instincts. Each is { trigger, action, confidence, evidence }:

Good: specific, reusable, project-true, not already obvious. e.g. trigger: "running tests in this repo", action: "use 'pnpm vitest run <path>' — the bare 'test' script watches and hangs", confidence: 0.85.
Bad: vague ("write clean code"), generic model-knowledge, or one-off trivia. Don't record those.
Keep them few and sharp. A handful of high-signal instincts beats fifty platitudes.

3. Set confidence honestly:

0.8–0.9 — user stated it explicitly, or you saw it hold 3+ times
0.5–0.7 — observed clearly once
0.3–0.4 — tentative hypothesis

4. Show the candidates to the user. List the instincts you intend to save. Unless they already said "just do it," get a quick confirm — they may correct or drop some.

5. Write them:

node "$HOME/.claude/skills/forge/scripts/forge-store.js" add '{"trigger":"...","action":"...","confidence":0.8,"evidence":"..."}'

(Accepts a single JSON object or a JSON array. Re-adding appends; on read, duplicates collapse to the highest-confidence copy — to lower a confidence, remove it then re-add. The CLI rejects one-word and platitude entries, so keep them specific.)

For an anonymized global lesson (transferable, no project specifics), use add-global instead — it re-renders ~/.claude/forge-data/LESSONS.md and rejects anything that looks project-specific (a path, filename, version token, or scoped package) or platitudinous. That backstop is best-effort, not a guarantee — you still own the anonymization:

node "$HOME/.claude/skills/forge/scripts/forge-store.js" add-global '{"trigger":"<general situation>","action":"<corrected behavior>","confidence":0.8,"evidence":"<why, no project names>"}'

6. Confirm and tidy:

node "$HOME/.claude/skills/forge/scripts/forge-store.js" list          # this project's instincts
node "$HOME/.claude/skills/forge/scripts/forge-store.js" list-global   # global lessons (+ path to LESSONS.md)
node "$HOME/.claude/skills/forge/scripts/forge-store.js" clear-observations

Rules of the road

Instincts are priors, not laws. An explicit user instruction always overrides a learned instinct.
They're project-scoped — React patterns stay in the React project. They auto-load next session via the SessionStart hook (restart Claude Code for newly-written instincts to take effect).
Prune stale ones: forge-store.js remove "<substring>". Curate ruthlessly; bloat here costs context on every session start.

Tunables (env vars — set under `"env"` in `~/.claude/settings.json`)

Injection (recall):

FORGE_INSTINCT_MAX / FORGE_INSTINCT_MIN_CONF — cap & confidence floor for project instincts (defaults 12 / 0).
FORGE_GLOBAL_MAX / FORGE_GLOBAL_MIN_CONF — cap & confidence floor for global lessons (defaults 15 / 0).

Auto-learn (the automatic distiller):

FORGE_AUTOLEARN — set to off to disable automatic learning entirely (default on).
FORGE_LEARN_EVERY — distill after this many new logged actions (default 8). Lower = more continuous + more cost; higher = leaner.
FORGE_LEARN_MIN_CONF — the distiller's quality floor; lessons below this are dropped (default 0.6).
FORGE_LEARN_MODEL — model id for distillation (default Haiku).
FORGE_DATA_DIR — relocate the data dir (default ~/.claude/forge-data).

Global lessons load into every session, so keep them few and high-signal — raise FORGE_GLOBAL_MIN_CONF to 0.7+ once the list grows. The distiller never blocks you and silently no-ops on any error.