steering-correction-promotion

name	steering-correction-promotion
description	On user steering corrections or remember-this, pick the smallest durable artifact (skill section, scoped rule, or rare always-apply rule), slim AGENTS.md, optimize agent-brain context, then prepend JRN. Use instead of creating new alwaysApply rules by default.
disable-model-invocation	true

Steering correction promotion

Run this skill in the same turn as any user steering correction — how you decide, operate, route, log, or commit. Finish with artifact + JRN + brief confirmation.

Also invoked from agent-self-improvement.

Context budget (optimize every promotion)

Layer	Cost	Use for
alwaysApply: true rules	Every turn — add sparingly; merge or scope when possible	Routing, portable paths, Python env — only what every task needs
globs rules	When matching files are in play	Script execution, docs schema, test patterns
Skills	When task matches or agent opens skill	Procedures, WSL GPU, promotion workflow itself
AGENTS.md	Router only — stay thin	One index table; no procedure prose
Self-journal	Receipt — not loaded every turn	Audit trail with Artifact path

Default: prefer skill section or scoped rule over a new alwaysApply rule.

Decision tree

Answer in order; stop at the first fit.

1. Multi-step procedure (train, debug playbook, log EXP)?
   → Extend existing skill OR new skill + skills README row
   → AGENTS.md: new index row ONLY if nothing routes there yet

2. Only matters when editing/running certain paths?
   → Scoped rule (.mdc with globs, alwaysApply: false)
   → Merge into existing scoped rule if same glob family (e.g. scripts/**)

3. Universal but <5 lines and same topic as an existing alwaysApply rule?
   → Merge into that rule — do NOT add a new file

4. Truly required on EVERY turn (safety, entry routing)?
   → alwaysApply rule — LAST RESORT
   → Keep under ~40 lines; regenerate [agent-brain.md](../../../docs/agents/agent-brain.md)

5. One-off preference for this session only?
   → Chat — no JRN unless user says remember this

6. Mechanical bug with a code fix?
   → Code/tests first; optional scoped rule if agents keep repeating it

Same-turn workflow

Step	Action
1	Run decision tree — pick smallest enforcement layer
2	Implement — edit skill/rule/AGENTS/code; dedupe while editing
3	Optimize — see checklist below
4	JRN — prepend with Artifact path(s)
5	Confirm — artifact + journal id + what was not promoted to alwaysApply
6	Quick refresh — agent-brain-refresh steps 1–3

Optimization checklist (required)

After every promotion, scan agent brain for waste:

• No new alwaysApply unless step 4 of decision tree — justify in JRN; prefer scoped rule or skill
• Merge rules that share a glob or topic (e.g. one scripts/** rule for shell output)
• Demote any alwaysApply rule that only applies to scripts, tests, or docs → set globs, alwaysApply: false
• AGENTS.md — index rows only; link agent-brain.md for rule catalog; no always-on rule table
• Skills README — one row per skill; no duplicate of rule body
• Do not duplicate the same constraint in rule + skill + AGENTS.md — one enforcement home

AGENTS.md rules

Do	Don't
Add a row to the task index when a new skill/index is the entry	Paste skill or rule content into AGENTS.md
Link agent-brain.md for rule inventory	Duplicate alwaysApply lists (Cursor loads them automatically)
Link north star / pre-submit once	Grow AGENTS.md with journal or EXP content

Rule catalog

Update docs/agents/agent-brain.md by hand during refresh. Verify with read-only python scripts/audit_agent_brain.py. Scoped summary: skills README § Agent brain.

JRN receipt

| **Artifact** | Path(s) — include layer chosen (skill / scoped rule / alwaysApply / code) |
| **Action taken** | One line: what enforces the correction + any demotion/merge done |

Signals

Steering: “remember this”, “don’t …”, “always …”, “stop doing X”, corrections to agent process (not hyperparameters or one-off task args).