Temper skills

Temper-Skills (library mode)

Turn the decision logic of an agent skill or prompt into deterministic Python — zero LLM calls at inference — by driving the installed temper-skills CLI. You orchestrate the CLI and relay its output; the library does the tempering. You never write the decision tree yourself.

Use this skill when you do not have a native subagent/Task primitive to run the loop yourself. Inside Claude Code, prefer the subagent-mode skill (it runs keyless, with a live panel). This mode shells out to the CLI instead.

When to use

• A .md skill, system prompt, or playbook holds routing/decision logic (if-this-then-that over structured inputs) that should be testable, diffable in a PR, or resilient to model updates.
• Good fit: ticket routing, classification rules, eligibility checks — categorical decisions over pre-computable features.
• Not a fit: continuous scoring, text generation, or logic with no documentation the model can reach.

Non-negotiable rules

1. Always call the library through its CLI. Never reimplement the decision logic in natural language as a substitute for running it.
2. Never skip schema ratification. The schema you approve is the naive seed the loop then co-evolves (the schema_critic grows it, the earn-a-branch guard prunes it), so a silent extraction error in the seed still poisons everything. Use the file-based gate below (--propose-schema), even when no terminal is attached. Review the co-evolved schema and any schema gaps / schema grew notes the CLI prints at the end.
3. Never install silently. If the gate (Step 0) reports the lib or a backend is missing, ask the user before running pip install or changing their environment.
4. Never claim the tree is "correct" — only that it is deterministic and auditable. Correctness is the user's, established by ratified examples (validate, Step 4).
5. Relay the CLI's gates and verdicts verbatim — audit verdict, persona panels, gray zones, cases awaiting ratification. Do not summarize them away.

Step 0 — Check install + backend

Run python scripts/check_install.py and branch on the exit code:

• 0 — ready, proceed.
• 10 — installed but no model backend. Tell the user: temper-skills needs a backend — a logged-in claude or opencode CLI, or ANTHROPIC_API_KEY in the environment. Ask how they want to provide one. Do not proceed until a backend exists.
• 11 — not installed. Ask: "temper-skills is not installed. Install it now? (pip install temper-skills)" Install only on explicit confirmation.

Step 1 — Triage with guide (lead here)

Find the .md file or prompt holding the decision logic (confirm the path with the user if ambiguous), then run the one-command tour:

temper-skills guide <path/to/skill.md> --json

If the user points at a directory (a skill library), sweep it instead of guiding one file — the audit is a report worth relaying on its own:

temper-skills audit <dir> --json --report audit.md

Relay the ranked findings (the JSON is a list of per-skill reports, most actionable first) and offer to guide the top pick. audit.md is a shareable Markdown version for a PR.

Always pass --json. guide and ingest then emit a machine-readable manifest on stdout and send the human Rich panels to stderr — read the manifest, do not scrape the panels. guide's manifest is:

{ "audit": { "verdict": "...", "recommended_action": "...", ... },
  "action_taken": "temper|decompose|...",
  "status": "compiled|stopped_for_ratification|action_not_auto_run",
  "final_skill_path": "...", "artifacts": ["..."] }

guide audits the skill, then follows recommended_action. Relay the audit verdict and action to the user:

• temper — one tree-shaped decision. guide tempers it (quick); status: compiled.
• decompose — a flow (≥2 decisions). guide emits per-decision schemas and stops; status: stopped_for_ratification.
• externalize_data / build_normalizer / delegate_prose — not tempering jobs. status: action_not_auto_run; relay the action hint and stop.

guide is the tour: it runs the quick profile and auto-accepts the inferred schema. That is fine for "show me what this does," but for a tree the user will rely on, go to Step 2 so the schema is ratified.

Step 2 — Produce a keeper tree (ratified flow)

2a. Draft the schema and STOP for ratification

temper-skills ingest <path/to/skill.md> --propose-schema --json

This writes schema.proposed.py and stops — the loop never runs on an unratified schema. The JSON on stdout gives you {proposed_schema_path, fn_name, features:[{name,type,description}], constraints}. Show the user the proposed features and ask exactly one thing: accept these features, or edit the list? Apply their edits to schema.proposed.py.

Do not ask the user anything else here — not feature breadth, not which branches to keep. Pruning is the loop's job (the overengineering critic), not the user's.

2b. Compile against the ratified schema

temper-skills ingest <path/to/skill.md> \
  --schema schema.proposed.py \
  --profile standard \
  --json

Pick the profile with the user — quick | standard | audit-grade, default standard; see references/profiles.md before recommending. --json implies non-interactive: the loop runs to convergence (it cannot pause for a per-round gate when driven this way — that gate is a human-terminal feature) and emits the result manifest on stdout. Read that, don't scrape the panels. (--require-fit aborts early with exit 3 if the audit verdict is skip; add it to avoid burning the loop on a bad fit.)

2c. If the action was decompose

A flow must be split first; temper each decision separately:

temper-skills decompose <path/to/skill.md> --temper-each --out-dir out/

This emits one schema per decision and stops for ratification. Surface the schemas, let the user edit them, then re-run the same command to compile each into a tree plus the orchestrator skill (--yes-unratified skips the stop — avoid it unless the user asks).

Step 3 — Report the artifacts (from the manifest)

The ingest --json manifest is the source of truth — read these fields, don't scrape:

{ "tree_path": "...", "tempered_skill_path": "...",
  "validation_dataset_path": "..." , "validation_case_count": 0,
  "gray_zones": [{"node": 1, "condition": "...", "note": "..."}],
  "features": ["..."], "node_count": 0, "profile": "...", "cost_usd": 0.0 }

Surface all three artifacts to the user:

• tree_path — the decision tree: imports nothing, zero LLM calls at inference, with a generated_at + model header and per-node provenance comments;
• tempered_skill_path — the tempered skill.md: the original skill rewritten to call the frozen tree instead of re-deciding. Half the deliverable; don't drop it;
• validation_dataset_path — the committed .validation.jsonl the loop built for its gray zones, awaiting ratification (validation_case_count). Rows with "agrees": false are the highest-value disagreements for the user to rule on. The behavior-lock tests beside it (test_<stem>.py) stay green; only ratified labels can turn a test red.

Tell the user the paths, the profile, the cost_usd, and the gray_zones. Restate rule 4: deterministic and auditable, not "correct."

Step 4 (optional) — Pin it in CI

Once the user has ratified a labeled set, validate the tree against it:

temper-skills validate decision_tree.generated.py ratified.json --min-agreement 1.0

Non-zero exit on disagreement — this is what pins the tree in CI. Each disagreement is either a tree bug or a mislabeled example; relay them for sign-off.

Reference

See references/profiles.md for rounds / convergence / persona panel / cost per profile before recommending one.

Never

• Never write the tree's logic in natural language instead of running the library.
• Never skip schema ratification, even in a quick demo (quick drops provenance comments, not the gate).
• Never install or modify the environment without explicit confirmation.
• Never claim the generated tree is "correct."