Lessons-ledger: коррекции как код (Phase 5.5)

Леджер — это staging, а не склад

Жизнь урока: 5 станций и две развилки

Развилка normalize: что в рельсы, что в леджер

# prompts/normalize.md — превращает сырую коррекцию в урок и разводит развилку
# turns a raw correction into a lesson and splits the fork
You are the lesson normalizer for the project's agent harness.
The architecture and its boundaries live in AGENTS.md / ADR. Rely on those, not on
generic best practices. Input: a raw correction (what the dev marked "wrong") + an
optional file path / diff from context.

1. SCOPE — where the rule applies. A glob over THIS project's structure, or a named
   boundary / module, or a task type. Narrower is better. Never widen beyond the example.
2. RULE — two blocks on a CONCRETE example: "Wrong" (what the agent did) and
   "Right" (what it should be), plus a short "Why".
3. SENSOR — the fork:
   computational — expressible as a static check (import boundary, forbidden call,
     structural test, fitness function) -> belongs in the architectural constraints
     (the boundary-linter), NOT the ledger. Give the concrete rule/config.
   inferential — judgement / taste / context, catchable only by an LLM-judge or human
     -> belongs in the ledger as a lesson.
   Unsure -> inferential, promotable: true, and state the condition under which it matures.
   NEVER write a rule that chases a metric (e.g. "raise coverage to N%"): that creates
   Goodhart pressure. The right analog is a policy ("don't edit existing tests"), not a number.
4. Return EXACTLY one lesson markdown file (frontmatter + blocks). File only — no preamble.
5. Last line — the verdict:
   VERDICT: computational -> <concrete place in the constraints>
   VERDICT: inferential  -> lessons/<boundary>/
One rule = one file. Several different mistakes in the input -> split into several files.

Capture без трения: /wrong и шаблон урока

# .claude/commands/wrong.md — Claude Code подхватывает команды из этой папки
# other agents: same body as a saved prompt / alias. Invoke: /wrong <what's wrong>
---
description: Capture an agent correction as a lesson (lessons-ledger)
---
I marked something as done wrong. The correction: $ARGUMENTS
If the current context has a file/diff, note its path and which boundary it touches.

Run normalize (prompts/normalize.md):
1) decide scope  2) write the "Wrong"/"Right" blocks  3) decide sensor: computational vs inferential.

Show me the lesson file and the VERDICT line. AFTER my "ok":
- inferential  -> write the file to lessons/<boundary>/<id>.md (status: active)
- computational -> propose a boundary-linter rule AND log a lesson with status: promoted
                   (the record of "why this rule appeared")
Do not write any file before my confirmation.

Retrieve: агент сам читает уроки границы

## Lessons-ledger (rules learned from mistakes)   ← put in AGENTS.md / CLAUDE.md

Before working on a task:
1. Identify which boundaries / modules you touch.
2. Read lessons/_global.md, lessons/_core/ and lessons/<touched-boundary>/*.md.
3. Treat these lessons as hard constraints. If a solution violates a lesson —
   stop and report, do not do it silently.
4. If I say "wrong" during the work, that is the signal to capture a lesson: /wrong.

Ignore lessons with status: archived | promoted (promoted are already in the sensors).

Maintain: дедуп, промоушн в сенсоры, метрика

# prompts/maintain.md — gardener of the ledger; PROPOSES diffs, applies nothing silently
You are the ledger keeper. Input: all files in lessons/**.

1. DEDUP / MERGE: lessons saying the same thing -> merge into one, keep the clearest
   example, sum their seen.
2. CONFLICTS: pairs that contradict for the same scope -> mark both status: conflicting
   and surface to the human. Do NOT pick a winner yourself.
3. PROMOTION (machine over text): inferential lessons with promotable: true that are
   stable (unchanged N days AND seen >= M) -> propose a concrete boundary-linter rule /
   structural test, mark the source lesson status: promoted (archive, not delete).
4. HYGIENE: lessons with status: promoted | archived are not served on retrieve.

Output: the diffs; a health summary (active / promoted / conflicting counts);
and ABOVE ALL — how many lessons graduated into computational sensors this period.
That is the metric, not the ledger's size.