steer-once

Correct an agent once; the repo remembers. An apology from the agent changes nothing next session. A merged rule does. This skill takes corrections you have typed more than once, finds the layer that would have prevented them, lands the smallest durable change there with evidence, and checks a week later whether the correction actually stopped. If it didn't, it climbs one layer; if the rule made things worse, it proposes a revert.

When to use

• Fast path (live): the user just repeated a correction mid-session ("again", "I told you", "why did you stop", "didn't I give you a skill"). Handle that one correction now.
• Batch: "mine my corrections", a weekly retro, or a prompt-mirror report listing a class with ≥N hits across ≥3 sessions.
• Team variant: recurring reviewer feedback on agent PRs in a repo (see Sources).

Not for one-off preferences, a single bad turn, or anything already handled by an open steer-once PR.

Sources

1. Your typed messages via prompt-mirror's extractor (local, redacted, source-filtered): python3 <skills-dir>/prompt-mirror/scripts/mine_sessions.py --since 21d [--repo owner/name]. Use correction_classes, repeated_correction_phrases, and each message's classes/flags. Read messages.jsonl for the instances.
2. The agent message before each correction (the cause) and after (the response). Count concession openers ("You're right", "Agreed", "Good catch") on the same class; a concession followed by a repeat is the strongest signal that a rule is missing or not loaded.
3. Team variant: changes_requested reviews and review comments on agent-authored PRs (for example gh pr list --author app/copilot-swe-agent --state all, then gh api repos/{owner}/{repo}/pulls/{n}/reviews). Same pipeline, no local history needed.

Steps

1. Read the dials (table below) and docs/autonomy.md. State the scope and autonomy level in one line.

2. Collect corrections for the window. Exclude agent, autopilot, system and template traffic (the extractor does this). Keep delivery mode: a mid-turn steer is a stronger correction than an idle follow-up.

3. Classify each correction. Start from the extractor's classes, then confirm by reading:

   Class	The agent…	Typical correction
   gate	invented approvals, locks, preflights, "exact GO" phrases	"you don't need authorization"
   over_ask	asked for what it already had	"don't ask, just do it"
   premature_stop	treated a milestone as done	"why did you stop?"
   polling	waited or slept instead of doing parallel work	"why are you waiting?"
   status_gap	made the human poll for state	"status? ETA?"
   verification_theatre	let checking replace shipping	"what did you spend 3 hours on?"
   stale_claim	reasoned from a proxy (stale clone, cache)	"that's not true, check the live source"
   shallow_fix	suppressed the symptom	"actually fix it instead of disabling lint"
   letter_of_law	shrank an ambitious ask	"I asked for the orchestrator pattern"
   scope_creep	did unrequested side quests	"why did you open a PR there?"
   process_default	lacked a team default procedure	"breaking changes are always two-phase"
   runtime_gap	hit something no prompt can fix	"rekick the stalled sessions", quota, leaked processes

4. Find recurrences: same class and same underlying expectation, ≥N hits across ≥2 sessions in the window (default N=3). Merge near-duplicates; one rule per expectation.

5. Check for an existing rule. Search .github/copilot-instructions.md, .github/instructions/*.instructions.md (applyTo scope), AGENTS.md, .github/skills/*/SKILL.md, .github/lever.yml, and personal ~/.copilot/copilot-instructions.md. If a rule exists and the class still recurs, the finding is "rule exists but is weak, misplaced or not loaded". Fix placement or wording, or escalate a layer, before adding more prose.

6. Pick the cheapest durable layer that would have prevented these specific instances:

   The expectation is…	Layer	Example
   A personal posture (tone, verbosity, how you like updates)	Personal instructions	"Report as x → y (Δz%); no preamble"
   A repo-wide default posture	.github/copilot-instructions.md (one line, with the why)	"Pre-launch service: ship and monitor; don't add approval gates"
   Authority or autonomy	A dial in .github/lever.yml, not prose	autonomy: L3 with the grant recorded
   A task-specific procedure	Scoped instruction or skill rule	expand → migrate → contract for breaking changes
   Mechanically detectable	Lint or CI check	no new eslint-disable without a linked issue
   Behavioural	A test that fails today	the service recovers from a stuck lease by itself
   Timing or liveness	Hook or automation (see automate-it)	resume idle child sessions on wake
   Outside the agent's control	Product or infra bug report	sessions don't auto-resume after sleep

   Corrections that cluster on one dial (merge authority, stop policy, status cadence, validation depth) become a dial change, not a new rule.

7. Write the rule. Positive, concrete, with the why and the scope, at most 3 lines. Name the dial it sets. No ALL CAPS, no "NEVER" walls, and never add a gate to prevent gates. Example: "Autopilot runs never idle: when the current item lands, take the next ready item. Why: 'why did you stop' recurred in 4 sessions."

8. Open the PR (or file the issue, for bugs) with the evidence block below. For personal instructions there is no PR: show the diff and apply only when the user says so, or under a recorded grant. Show the user the exact evidence text before it is published to any shared repo.

9. Land per the autonomy dial. Default L2: a ready PR the human lands. Instruction-only PRs are a common L3 grant class ("docs and instructions PRs may self-merge when green"). Record which level applied.

10. Record in the ledger: class, rule, layer, PR, baseline rate, re-measure date.

11. Re-measure after K days (and at least M typed messages in scope). Re-run the extractor for the same scope. Compare the class rate per 100 typed messages with the baseline, and with the week-to-week spread of that class over the previous 4 weeks.

    • Fell beyond noise → keep; comment the result on the PR.
    • Didn't fall → escalate one layer (instruction → skill rule → check/test → automation → bug report) and link the new PR to the old one.
    • A neighbour class rose (for example over_ask fell but premature_stop rose) → propose a revert or rewording, with numbers.

Evidence block (PR body)

### steer-once evidence
- **Class:** premature_stop: agent treated a milestone as done while ready work remained
- **Window / scope:** 2026-10-01 → 2026-10-21, this repo, typed messages only
- **Count:** 9 corrections in 4 sessions = 1.6 per 100 typed messages (baseline)
- **Agent concessions on this class:** 5 ("You're right…"), followed by repeats
- **Existing rule:** none | `path:line` exists but recurred (placement/wording issue)
- **Quotes** (redacted, dated, ≤20 words each, 3–5):
  - 2026-10-04 "why did you stop? there is more in the queue"
  - 2026-10-09 "if you're idle, take the next ready item"
- **Layer:** repo instruction. Cheaper layer insufficient because: … Costlier layer unnecessary because: …
- **Keep bar:** rate ≤ 50% of baseline and outside 4-week noise, re-measured on 2026-10-28 (K=7) or after 150 typed msgs
- **If not met:** escalate to <next layer>

Dials

Dial	Default	Options
Detection threshold	N=3 hits across ≥2 sessions in 21 days	any N / window
Mode	open PR (L2, human lands)	propose in chat; draft PR (L1); self-merge per docs/autonomy.md grant
Destination	repo for shared expectations, personal for posture	repo only; personal only
Evidence	redacted quotes + counts, shown to user before publishing	counts only; paraphrase
Re-measure	K=7 days and ≥150 typed messages in scope	any K / M
Keep bar	≤50% of baseline and outside noise	any threshold
Max open steer-once PRs	3	any
Severity weighting	off	weight mid-turn steers and heated messages higher (never quote heated ones)

Outputs

• One small PR or issue per recurring expectation, labelled steer-once, with the evidence block. The label is the team ledger.
• A personal ledger entry (default ~/.copilot/lever/steer-once.ledger.md, format per templates/ledger.md): keep, escalate or revert, with before/after rates.
• A re-measure comment on the original PR.

This skill's own feedback loop

Per rule: class rate per 100 typed messages, before and after. Across rules: share kept versus escalated versus reverted; concession openers per 100 agent replies (should fall); time from first occurrence to merged fix (should shrink). If most rules escalate, the skill is choosing layers too cheaply. If any rule raises a neighbour class, the skill is writing over-broad rules.

Failure modes it prevents

• Accepting "You're right" as a fix.
• Prose rules for things a check, test, dial or bug report should own.
• Rule sprawl: duplicate, contradictory or ALL-CAPS instructions nobody re-measures.
• One person's posture hard-coded into a shared repo; posture goes to personal instructions or a dial.
• Private text published as "evidence" without the user seeing it first.

Why this exists

• Across one heavy user's sessions, agents opened 75 replies with "You're right" or an equivalent, and the same corrections recurred for days. They stopped recurring only once they became a merged instruction or skill change.
• The fastest path, from a frustrated message to a merged operating-model PR, took 16 minutes. One correction that stayed verbal took about 18 days to become a default.
• After the user had an agent mine his last ~200 typed messages into a skill rewrite: "why did you stop" nudges 27 → 0, gate language in agent messages 31% → 3%, status asks ~24% → 7%, while his message volume rose from about 50 to 87 per day. "The delivery skill is ~200 of my own corrections, compressed."
• Some corrections are bug reports. "Restart the stalled sessions" recurred 8 times after the instruction existed, because interrupted sessions were a runtime gap no prompt could fix.