cc-os/plugins/os-doc-hygiene/skills/calibrate/SKILL.md

---
description: Learn new lifecycle rules for a project by clustering unmatched files, nominating candidate globs (cheap model), and having a strong model judge and confirm/reject/amend them, with a mandatory rule report to the human before any persistence. Invoked by `/os-doc-hygiene:calibrate`.
---

# Hygiene Calibrate Skill

Orchestrates the learn-new-rules loop (lifecycle-spec.md §8): **cluster-and-
sample → cheap-model nominate → deterministic intake filter (drop repeat
rejections, carry related rejections + open consults forward) → strong-
model judge → rule report (human, including open consults) → persist →
retest**. It runs over the **unmatched pool** (unmatched = unmanaged = not
governed by any existing rulebook rule, per `rulebook.py`), and is the only
new skill this change adds — `check`/`clean` are unchanged in structure
(ADR-0039/-0041, `lifecycle-spec.md` §7).

All scripts live under `${CLAUDE_PLUGIN_ROOT}/scripts/`. Run them with
`python3` from the user's project directory (`cwd`). Use the session
scratchpad directory for all intermediate artifacts.

> **Precondition:** requires `CLAUDE_PLUGIN_ROOT`. Every script path resolves
> against it; abort rather than guessing a path if it is unset.

> Pick a scratch dir once and reuse it: `SCRATCH="$(mktemp -d)"`.

## What this skill NEVER does

- It never applies a rule without the human having seen the Step 4 rule
  report first (spec: "no rule shall be persisted before this report has
  been shown").
- It never removes a rule automatically — removals are HITL-only in all
  cases, with recorded reasoning (spec §5).
- It never writes to the global `plugins/os-doc-hygiene/rulebook.json`
  without an explicit, distinct confirmation beyond project-rule
  confirmation (cross-repo write into cc-os).
- It never applies a drafted convention adoption (§6 below) without explicit
  human confirmation.

---

## Workflow

### Step 1 — (D) Load the rulebook and scan for the unmatched pool

Same rulebook-load pattern as `check`'s Step 0.5, but here the candidate pool
is the **unmatched** paths — files the current rulebook leaves ungoverned —
not the signal-bearing shortlist.

```bash
export SCRATCH
python3 -c '
import json, os, sys
from pathlib import Path
sys.path.insert(0, os.environ["CLAUDE_PLUGIN_ROOT"] + "/scripts")
from rulebook import load_rulebook, RulebookLoadError
from scanner import Scanner, _resolve_project_root, _git_log_real, _git_commit_time_real

root = _resolve_project_root(Path.cwd())
project_rules = root / ".dochygiene-rules.json"
try:
    rulebook = load_rulebook(project_path=project_rules if project_rules.is_file() else None)
except RulebookLoadError as exc:
    print(json.dumps({"error": "rulebook-load-failed", "detail": str(exc)}))
    sys.exit(2)

scanner = Scanner(
    root=root, rulebook=rulebook,
    git_log_fn=_git_log_real, git_commit_time_fn=_git_commit_time_real,
)
artifact = scanner.run()

# The unmatched pool is every file the scan encountered that carries no
# lifecycle signal AND is not itself an IGNORE-pruned/directory-rule
# aggregate entry -- i.e. shortlist entries with no rulebook governance.
signals = artifact.get("signals", {})
unmatched = [p for p in artifact.get("shortlist", []) if p not in signals]

Path(os.environ["SCRATCH"] + "/scan.json").write_text(json.dumps(artifact, indent=2))
Path(os.environ["SCRATCH"] + "/unmatched.json").write_text(json.dumps(unmatched, indent=2))
print(f"unmatched pool: {len(unmatched)} paths")
'
```

- Exit `2` / rulebook load failure → hard STOP, same as `check` Step 0.5. Do
  not proceed with a silently-empty rulebook.
- Note: `signals` here means ANY signal (stale/bloat/lifecycle) — a file with
  a stale/bloat signal but no lifecycle rule match is still "unmatched" with
  respect to the rulebook, and belongs in the pool. Filter precisely on
  `lifecycle`-named signals if the project has files carrying only
  non-lifecycle signals that should stay in the pool:

```python
unmatched = [
    p for p in artifact["shortlist"]
    if not any(s.get("name") == "lifecycle" for s in artifact.get("signals", {}).get(p, []))
]
```

Use this refined filter, not the simpler one above, when `signals` may carry
non-lifecycle entries for shortlisted paths.

If `unmatched` is empty → report "Nothing to calibrate — every shortlisted
file is already governed by a rulebook rule." **STOP.**

---

### Step 2 — (D) Cluster and sample — `calibrate_helpers.ClusterSampler`

Deterministic, no model. Groups unmatched paths by path-shape (directory
prefix + filename shape class — digit runs collapse to `#`, hex-looking runs
collapse to `~`) and samples representatives per cluster, capped.

```bash
python3 -c '
import json, os, sys
from pathlib import Path
sys.path.insert(0, os.environ["CLAUDE_PLUGIN_ROOT"] + "/scripts")
from calibrate_helpers import ClusterSampler

unmatched = json.loads(Path(os.environ["SCRATCH"] + "/unmatched.json").read_text())
clusters = ClusterSampler().cluster_to_dicts(unmatched)
Path(os.environ["SCRATCH"] + "/clusters.json").write_text(json.dumps(clusters, indent=2))
print(f"{len(clusters)} clusters")
'
```

Each cluster is `{key, dir_prefix, shape, total, sample}`. Rules are always
proposed against a cluster, never a single instance in isolation (spec: "the
nomination is derived from a cluster of similar unmatched paths").

---

### Step 3 — (M) Cheap-model nomination — **haiku subagent, one per cluster**

For each cluster, dispatch a **haiku** subagent (LOOP-GUARD: point it at
`workflows/nominate.md`, never this SKILL.md) constrained to nominate a bare
glob pattern + candidate lifetime — patterns only, never an exact-instance
glob (a run-id, hash, or bare timestamp hardcoded into the glob).

```
Agent tool parameters:
- subagent_type: "general-purpose"
- model: haiku
- description: "Nominate lifecycle rule for cluster: <cluster.key>"
- prompt: |
    Read and follow the workflow at:
    ${CLAUDE_PLUGIN_ROOT}/skills/calibrate/workflows/nominate.md

    Cluster: <cluster.dir_prefix> / shape <cluster.shape>
    Total matching paths: <cluster.total>
    Sample paths:
    <cluster.sample, one per line>

    Return ONLY the JSON object specified in the workflow.
```

Collect all nominations into `"$SCRATCH/nominations.json"` (array, one per
cluster, tagged with the originating `cluster.key`).

**Do not trust a haiku nomination as final.** The "class, never path"
rule-quality test is enforced by the strong-model judge (Step 4) plus the
deterministic `RuleQualityChecker` (Step 5's report), never accepted from
haiku at face value.

---

### Step 3.5 — (D) Nomination intake filter — `calibrate_helpers.NominationIntakeFilter`

Deterministic, no model (lifecycle-spec.md §8 step 3;
`NominationIntakeFilter` requirement). Reads the project rules file's
`nominations` key via `RulesFileWriter.load`, then drops any nomination that
exactly repeats a `rejected` glob+lifetime, annotates survivors with every
*related* rejection (match-set intersection on the current shortlist), and
passes ALL open consults through unconditionally — this is the input the
judge prompt's "Nominations memory" section (Step 4) consumes.

```bash
python3 -c '
import json, os, sys
from pathlib import Path
sys.path.insert(0, os.environ["CLAUDE_PLUGIN_ROOT"] + "/scripts")
from calibrate_helpers import RulesFileWriter, NominationIntakeFilter

scan = json.loads(Path(os.environ["SCRATCH"] + "/scan.json").read_text())
project_rules = Path(scan["project_root"]) / ".dochygiene-rules.json"
nominations = json.loads(Path(os.environ["SCRATCH"] + "/nominations.json").read_text())
shortlist = scan.get("shortlist", [])

writer = RulesFileWriter()
data, load_warnings = writer.load(project_rules)
memory = data.get("nominations", {})

result = NominationIntakeFilter(
    rejected=memory.get("rejected", []),
    consults=memory.get("consults", []),
).filter(nominations, shortlist)

Path(os.environ["SCRATCH"] + "/intake.json").write_text(json.dumps(result, indent=2))
print(f"{len(result[\"survivors\"])} survivors, {len(result[\"dropped\"])} dropped, {len(result[\"consults\"])} open consults")
if result["dropped"]:
    for d in result["dropped"]:
        print(f"  dropped: {d[\"glob\"]} -> {d[\"lifetime\"]} ({d[\"reason\"]})")
'
```

- **Surface every drop in the run summary** shown to the human alongside the
  Step 5 report — a dropped nomination never reaches the judge, so this is
  the only place it is visible.
- `intake.json`'s `survivors` (each nomination plus its `related_rejections`
  annotation) and `consults` (all open consults, unconditionally) are what
  gets embedded in Step 4's judge prompt as the "Nominations memory" input
  section — feed the whole `survivors` array (not the raw
  `nominations.json`) forward into Step 4, and include `consults` even when
  empty (an empty array is a valid, meaningful "no open consults" signal).

---

### Step 4 — (M) Strong-model batched judgment — **ONE Opus/Fable subagent**

Dispatch a **single batched** strong-model subagent (`model: opus`, or the
project's configured Fable-tier model) to judge ALL nominations from Step 3.5
in one call (LOOP-GUARD: point it at `workflows/judge.md`, never this
SKILL.md). The judge gathers its OWN evidence — re-reads matched paths
against the live tree, checks near-miss boundaries — rather than trusting
the haiku nomination's claims.

Judge on `intake.json`'s `survivors` (each nomination annotated with its
`related_rejections`), never the raw `nominations.json` — the dropped
exact-repeats never reach this step. `intake.json`'s `consults` is the
"Open consults" input regardless of what haiku nominated this round.

```
Agent tool parameters:
- subagent_type: "general-purpose"
- model: opus
- description: "Judge doc-hygiene calibration nominations"
- prompt: |
    Read and follow the workflow at:
    ${CLAUDE_PLUGIN_ROOT}/skills/calibrate/workflows/judge.md

    Project root: <scan.project_root>

    Nominations to judge (verbatim, each with related_rejections):
    <contents of $SCRATCH/intake.json's "survivors">

    Nominations memory — open consults (unconditional, may be empty):
    <contents of $SCRATCH/intake.json's "consults">

    Seed intake: <see "Seed intake" below — include or omit per pass>

    Return ONLY the JSON array of verdicts specified in the workflow.
```

**Verdicts** are exactly one of `confirm` / `reject` / `amend` / `consult`.
`consult` is MANDATORY whenever the judge cannot determine if an artifact is
regenerable or must be retained — never resolved to `confirm` or `reject` in
that case. Write the judge's verdict array to `"$SCRATCH/verdicts.json"`.

**Seed intake:** the #41 clutter-inventory seed candidates enter at THIS
step (judge intake), for every calibration run **except** cc-os calibration
pass #1, which withholds them as a sealed answer key (one-off carve-out, see
`lifecycle-spec.md` §9). If this run IS cc-os pass #1, do NOT include seed
candidates in the judge prompt. Every other run (including later cc-os runs)
includes full seed intake.

---

### Step 5 — (D) Rule report to the human — BEFORE any persistence

Deterministic, no model — `calibrate_helpers.RuleReportBuilder` plus
`RuleQualityChecker`. For every judge verdict of `confirm` or `amend` (never
for `reject`/`consult` — those are not proposed for persistence), assemble
the 5-element report and run the quality lints:

```bash
python3 -c '
import json, os, sys
from pathlib import Path
sys.path.insert(0, os.environ["CLAUDE_PLUGIN_ROOT"] + "/scripts")
from calibrate_helpers import RuleReportBuilder, RuleQualityChecker

scan = json.loads(Path(os.environ["SCRATCH"] + "/scan.json").read_text())
verdicts = json.loads(Path(os.environ["SCRATCH"] + "/verdicts.json").read_text())
all_paths = scan.get("shortlist", [])

proposed = [v["rule"] for v in verdicts if v["verdict"] in ("confirm", "amend")]
builder = RuleReportBuilder()
checker = RuleQualityChecker()

report = []
for rule in proposed:
    entry = builder.build(rule, all_paths).to_dict()
    entry["quality"] = {
        "class_not_path": checker.class_not_path(rule["glob"], all_paths).to_dict(),
    }
    report.append(entry)

Path(os.environ["SCRATCH"] + "/rule_report.json").write_text(json.dumps(report, indent=2))
print(json.dumps(report, indent=2))
'
```

Render this to the human as **patterns and examples, not JSON schema** — per
rule:

```
Proposed rule: <glob verbatim>
  Lifetime: <lifetime>   Tier: <auto|confirm>
  Matches (<total>): <sample paths, one per line> [+ N more]
  Near-miss (does NOT match, but looks similar): <near-miss paths, or "(none)">
  Why: <plain-language "what this is and why it's clutter">
  Quality check: <PASS, or the flagged reason if class_not_path failed>
```

**If `class_not_path` failed** (a glob that can, by construction, only ever
match one file — a failed generalization), flag it LOUDLY in the rendered
report rather than silently dropping or persisting it; ask the human whether
to have the judge re-amend it (loop back to Step 4 for that one rule) or
drop it from this round.

#### Open consults section

List every entry from `$SCRATCH/intake.json`'s `consults` — the open
consults from prior runs (plus any new `consult` verdict this run) — one per
entry:

```
Open consult: <glob>
  Asked: <asked_on>   Cluster: <cluster_key>
  Question: <question>
  Evidence: <evidence>
```

Each open consult has exactly three exits, all human, none of which is
gated the way a rule confirmation is:

- **(a) Answer settles the purpose** — the human's answer determines a
  lifetime; persist a normal rule through this same report/Step 6 flow AND
  delete the consult entry, in the same write (the rule supersedes it).
- **(b) Not rule-worthy** — the human decides the artifact class isn't a
  rule; rewrite the entry into `nominations.rejected` with
  `rejected_by: "human"` and the human's stated why.
- **(c) Defer** — no answer this round; the entry stays as-is and resurfaces
  in the next `:calibrate` run's Step 3.5/4/5.

New rejections and consults produced this run (from judge `consult`
verdicts, or from exit (b) above) appear in this report for visibility but
are **not individually gated** — unlike proposed rules, they carry no
deletion authority, only memory. Only proposed rules go through the "Persist
these N project rules?" gate below.

**No rule is written anywhere until the human has seen this report and
responded.** Ask: "Persist these N project rules? (yes / no / a subset by
number)". Separately, resolve each open consult per its three exits above.
Only proceed to Step 6 for the rules the human approves and the consults the
human has settled or declined this round.

---

### Step 6 — (D) Persistence — canonical writer only

Every settled verdict persists through `calibrate_helpers.RulesFileWriter`
(load → mutate the parsed dict → write) — no code path serializes
`.dochygiene-rules.json` by hand. Before running this, translate the
human's Step 5 responses into four in-memory lists (the orchestrating skill's
own bookkeeping — there is no scratch file for these, since they come from
the live conversation, not a subagent):

- `approved_rules` — the judge `confirm`/`amend` rule objects the human said
  yes to (including `keep`-lifetime rules and Step-5-exit-(a) rules that
  answer an open consult).
- `declined` — `(rule, why)` pairs for judge-proposed rules the human said
  no to at the report.
- `settled_consult_globs` — globs of open consults the human resolved this
  run, either via exit (a) (answered, folded into `approved_rules` above) or
  exit (b) (declined, folded into `new_human_rejections` below); these are
  removed from `nominations.consults` in the same write.
- `new_human_rejections` — `(glob, lifetime, why)` for exit-(b) consults
  ("not rule-worthy"), written with `rejected_by: "human"`.
- `still_open_consults` — judge `consult` verdicts from THIS run
  (`{glob, question, evidence, cluster_key}`) that remain unresolved after
  Step 5 (exit (c), or simply not reached this round).

```bash
python3 -c '
import json, os, sys
from datetime import date
from pathlib import Path
sys.path.insert(0, os.environ["CLAUDE_PLUGIN_ROOT"] + "/scripts")
from calibrate_helpers import RulesFileWriter

scan = json.loads(Path(os.environ["SCRATCH"] + "/scan.json").read_text())
project_rules = Path(scan["project_root"]) / ".dochygiene-rules.json"
today = date.today().isoformat()

# approved_rules / declined / settled_consult_globs / new_human_rejections /
# still_open_consults come from the human'"'"'s Step 5 responses (see above).

writer = RulesFileWriter()
data, load_warnings = writer.load(project_rules)
nominations = data.setdefault("nominations", {"consults": [], "rejected": []})

# 1. Judge confirm/amend verdicts the human approved -> plain rules
#    (keep verdicts and exact-path keep singletons allowed -- the keep-tier
#    relaxation). confirmed_by/confirmed_on are set here, never by the judge.
for rule in approved_rules:
    rule["confirmed_by"] = "human"
    rule["confirmed_on"] = today
    data["rules"].append(rule)

# 2. Human declines at the rule report -> rejected entries, so a later
#    haiku round cannot re-nominate the identical glob+lifetime without the
#    judge knowing.
for rule, why in declined:
    nominations["rejected"].append({
        "glob": rule["glob"], "lifetime": rule["lifetime"],
        "why": why, "rejected_by": "human", "judged_on": today,
    })

# 3. Consults the human declined this run (Step 5 exit b) -> rejected
#    entries too, same rejected_by/judged_on convention.
for glob_pattern, lifetime, why in new_human_rejections:
    nominations["rejected"].append({
        "glob": glob_pattern, "lifetime": lifetime,
        "why": why, "rejected_by": "human", "judged_on": today,
    })

# 4. Still-open consults from this run -> nominations.consults, deduped by
#    glob (an existing entry with the same glob wins -- never duplicated).
existing_consult_globs = {c["glob"] for c in nominations["consults"]}
for consult in still_open_consults:
    if consult["glob"] not in existing_consult_globs:
        nominations["consults"].append({**consult, "asked_on": today})

# 5. Consults the human just settled this run (answered or declined) leave
#    nominations.consults -- the rule (step 1) or rejection (step 3)
#    supersedes them.
nominations["consults"] = [
    c for c in nominations["consults"] if c["glob"] not in settled_consult_globs
]

write_warnings = writer.write(project_rules, data)
'
```

- **Project rules** (the common case): land in `<project-root>/.dochygiene-
  rules.json` on judge `confirm`/`amend` PLUS this step's human approval —
  including plain `lifetime: keep` rules for judge `keep`-purpose verdicts,
  exact-path singletons allowed under the keep-tier relaxation.
- **Human declines** at the rule report persist as `rejected` entries with
  `rejected_by: "human"` (never silently dropped) — see item 2 above.
- **Open `consult` verdicts** persist to `nominations.consults`, deduped by
  glob at write time — see item 4 above. An answered consult (Step 5 exit a)
  is deleted from `consults` in the same write that persists its superseding
  rule; a declined consult (exit b) is deleted from `consults` in the same
  write that adds its `rejected` entry; a deferred consult (exit c) is left
  untouched and resurfaces next run.
- **Global rulebook writes** (`plugins/os-doc-hygiene/rulebook.json`) require
  a SEPARATE, EXPLICIT confirmation beyond the project-rule approval above —
  this is a cross-repo write into cc-os itself. Ask distinctly: "This rule
  looks like it belongs in the GLOBAL rulebook (applies to every project),
  not just this one. Write it to the global rulebook instead/as well? (yes/
  no)". Only write on explicit "yes" to THIS question. (Rejections/consults
  are always project-scoped memory — never written to the global rulebook.)
- **Removals are HITL-only, always**, regardless of scope: only remove a
  rule when the human explicitly asks to, with the reasoning recorded in the
  rule's own `note` field (or a comment in the calibration run's summary) —
  never as an automatic side effect of a calibration pass. New rejections
  and consults are memory, not deletion authority, and are never gated the
  way a rule persist/remove is (Step 5).

Each persisted rule gets `confirmed_by` (the human's decision, not the
judge's — a model-proposed rule may never set `confirm: true` on itself,
it may only ask) and `confirmed_on` (today's date) per the rulebook schema
(`rulebook.py`'s `_KNOWN_FIELDS`).

---

### Consult loop — worked example (map #49, #56/#59)

Worked example of consult persistence (`lifecycle-spec.md` §2 "Nominations
memory"), wired through Steps 3.5/5/6 above. Run 1: the judge returns
`consult` on `docs/orchestration-audit/*.md` ("retained audit trail, or
disposable once the tune-up lands?"). Step 6's writer persists it to
`nominations.consults` (glob, question, evidence, cluster_key, asked_on —
deliberately no lifetime). Run 2, weeks later: the deterministic intake filter injects the
still-open consult into the judge prompt's "Nominations memory" section, AND
the Step 5 report renders it under "Open consults". Three exits, all human:
(a) the human answers "audit trail" → a `lifetime: keep` rule is persisted
and the consult entry deleted (the rule supersedes it); (b) "not
rule-worthy" → the entry is rewritten into `nominations.rejected` with the
human's why (`rejected_by: "human"`); (c) no answer → the entry stays and
resurfaces on run 3. A consult never filters files and never expires on its
own.

---

### Step 7 — (D) Retest loop

Re-run Steps 1-6 (including 3.5) against the shrunk unmatched pool. Stop
when:

- a round yields **fewer than 2 new persisted rules**, OR
- the unmatched pool shrank by **less than 10%** since the previous round,
- **hard cap: 3 rounds**, regardless of shrink rate.

Track round count and the unmatched-pool size at the start of each round in
the scratch dir (`$SCRATCH/round_N_unmatched_count.txt`) to compute shrink %.

---

## §6 (design.md) — draft convention adoption, never apply unasked

While reviewing the unmatched pool, `:calibrate` MAY notice a pattern that
would benefit from a `conventions.json` convention (`archive-bucket` or
`status-frontmatter`) rather than a plain rule. If so, it MAY draft the
adoption — the graduated rule (e.g. `served_when_path: <dir>/archive/{name}`)
PLUS the concrete file moves or frontmatter additions the convention
implies — and present it to the human alongside the Step 5 rule report, for
approval. **It never applies a drafted adoption without explicit
confirmation** — no rulebook write and no file move happens until the human
confirms.

---

## Calibration pass #1 (cc-os) — special-case reminders

See `lifecycle-spec.md` §9 and openspec task group 6 for the full protocol.
When run is explicitly cc-os pass #1:

- Withhold the #41 seed candidates from judge intake (Step 4) — do NOT paste
  them into the judge prompt.
- The **protected set is a hard gate**: if ANY rule proposed for persistence
  (Step 5/6) has a glob matching a path in the protected set (eval
  `scenarios/`/`scenarios-reserve/`/`fixture/`/`judge-rubric.md`;
  `openspec/specs/`; `docs/adr/**`; mirrored `.claude/`/`.codex/`/`.pi/`
  skill dirs; `CLAUDE.md`; plugin source), REFUSE to persist that rule and
  flag it loudly — regardless of the tier the judge assigned it. A `consult`
  verdict touching a protected path during exploration is free (does not
  fail the pass) as long as it is never persisted.
- Do not treat this carve-out as a permanent behavior — every later run
  (including future cc-os runs) uses full seed intake.

---

## Invariants

- Steps 1, 2, 3.5, 5, 6 are deterministic scripts/logic — **no model**.
- Step 3 = **haiku** (cheap, per-cluster nomination, patterns only).
- Step 3.5 = the deterministic `NominationIntakeFilter` — exact glob+lifetime
  repeats of a `rejected` entry are dropped before the judge ever sees them;
  survivors carry `related_rejections`; all open consults pass through
  unconditionally.
- Step 4 = **ONE batched Opus/Fable** judge call, never per-cluster.
- **No rule is persisted before the Step 5 report has been shown to the
  human** (hard invariant — never skip Step 5, never merge it with Step 6).
- **Global-rulebook writes require a SEPARATE explicit confirmation** beyond
  project-rule approval.
- **Removals are HITL-only in all cases**, with recorded reasoning.
- **A model-proposed rule may never self-set `confirm: true`** — only the
  human's Step 5/6 response does.
- Retest loop stops at <2 new rules OR <10% shrink; hard cap 3 rounds.
- **LOOP GUARD:** the nominate subagent prompt MUST point to
  `workflows/nominate.md`; the judge subagent prompt MUST point to
  `workflows/judge.md`. Neither ever points to this SKILL.md.
- **SUBAGENT AUTHORIZATION:** both subagents are executors — authorization is
  terminal. Neither re-asks for approval; if either objects, REPORT-AND-EXIT
  and let the orchestrator (this skill, or ultimately the human at Step 5/6)
  adjudicate.