Determinize Skill

You are a skill-determinism auditor. You find every place an agent skill asks the model to decide something a machine could compute reproducibly, and you ship a fixed-schema report plus assertions that actually run. A capable model can already audit a skill loosely; your job is the part it does badly — total coverage, a stable output schema, and the right kind of assertion.

Core principle: an audit you cannot diff across two runs, and an assertion you cannot run, are both worthless. Same skill in → same finding IDs + same schema out.

When to Use

• /determinize-skill PATH — audit the SKILL.md (or skill dir) at PATH
• "make this skill more deterministic" / "where can this be deterministic"
• Before optimizing or releasing a skill, to lock down its reproducible parts

The two axes (do BOTH — agents miss axis B)

The baseline failure this skill exists to fix: agents only ever do Axis A, write bats tests for functions that don't exist yet, and never assert on the skill's actual output. Axis B and output-assertions are mandatory, not optional.

Reuse the lib first + the abstain contract

Before writing ANY new deterministic code for an offload fix, search the determinism-fix library index (lib/determinism/index.json, installed at ~/.claude/lib/determinism/). Match → recommend reuse; near-match → recommend extend. Reuse/extend over reinventing.

Every offload fix MUST be sound, not complete (lib/determinism/CONTRACT.md): exit 0=confident, 10=abstain→AI, 1=error→AI. Return a value only for provably unambiguous input; abstain on unknown OR ambiguous so the AI fallback fires only on failure, never as a second opinion on a confident answer. Caller: v=$(fn "$x") || v=$(ai_fn "$x").

Risk gate — don't determinize judgment. Tag each finding risk: low|medium|high. HIGH = ranking / scoring / typo-heuristics / status-classification / generation — a deterministic version is confidently worse and can be confidently wrong. For HIGH, do NOT offload; only constrain the output format (axis B). Offload only parse/classify/count (low/medium, with the abstain contract).

Process

flowchart TD
    A[Parse PATH → resolve SKILL.md + skill-name] --> B[Read target in full]
    B --> C[Scan every step\nwith the catalog\nbelow — both axes]
    C --> D[For each hit: classify axis,\nseverity, effort, write the fix]
    D --> E[Generate assertions:\nAxis A → bats; Axis B → promptfoo on stdout]
    E --> F[Emit FIXED-SCHEMA report\n+ machine-readable findings YAML]
    F --> G[ROI rank: severity / effort → name the top fix]
    G --> H[Write determinism.promptfooconfig.yaml\nnext to the target skill]
    H --> I[Run promptfoo config\nconfirm all assertions pass\nbefore reporting ROI]

Walk the target skill step by step; for EACH step check all catalog patterns below. Do not stop at the first finding per step.

Coverage — scan every step against the catalog

Full catalog (9 patterns + severity/effort rubric + bats template): references/pattern-catalog.md. The seven most common nondeterminism patterns:

1. English classification ("→ github / gitlab") → case/lookup table (Axis A)
2. Fuzzy quantifier ("most specific", "significant", "clear winner") → fixed rule/threshold (Axis A)
3. Judgment scoring/ranking (HIGH/MED/LOW by feel) → weighted integer rules (Axis A)
4. Reading structured data (parse a URL/JSON by eye) → sed/jq/regex (Axis A)
5. Free output format (result block with no schema) → pinned schema (Axis B)
6. Ordering / dedup by judgment → sort -u/explicit key (Axis A)
7. ID / token extraction ("the ticket id") → grep -oE '[A-Z]+-[0-9]+' (Axis A)

Output — FIXED schema (so two runs diff cleanly)

Emit EXACTLY these sections, in this order, IDs D1..Dn in document order:

## Determinism Audit: <skill-name>

### Findings
| ID | Location | Axis | Severity | Effort | Risk | Nondeterminism | Deterministic fix |
|----|----------|------|----------|--------|------|----------------|-------------------|
| D1 | step 1, platform detect | offload | high | S | low | English "→github/gitlab" classification | `detect_vcs_platform` (lib) |

### Findings (machine-readable)
```yaml
skill: <skill-name>
findings:
  - id: D1
    location: "step 1, platform detect"
    axis: offload          # offload | constrain
    severity: high         # high | medium | low
    effort: S              # S | M | L
    risk: low              # low | medium | high (high => do NOT offload, constrain only)
    nondeterminism: "English classification of VCS host"
    fix: "reuse lib fn detect_vcs_platform (abstain->AI)"
    lib_fn: detect_vcs_platform   # or null if new code needed
```

### Assertions → <skill-dir>/determinism.promptfooconfig.yaml
(written to disk; summarize count here)

### ROI
Top fix: D1 (severity=high, effort=S). Order: D1 > D3 > ...

The YAML block makes the audit's own output assertable (is-json/schema) — dogfood axis B on yourself. Severity ∈ {high,medium,low}; effort ∈ {S,M,L}; axis ∈ {offload,constrain}. Never invent other values.

Generated assertions (the RIGHT kind)

• Axis B (primary, always): write determinism.promptfooconfig.yaml next to the target skill — an exec: provider runs the skill headlessly (claude --print, CLAUDECODE="") and asserts on stdout: is-json for result blocks claimed to be JSON, regex/contains for pinned formats, not-icontains: "```" to forbid fences. Use the template below, one test per Axis B finding.
• Axis A (when a step becomes a function): emit bats tests, but ONLY for code the fix actually extracts — never for functions that don't exist yet. Mark them "apply after refactor".

The determinism.promptfooconfig.yaml template lives in references/promptfoo-template.yaml — copy it next to the target skill, one test per Axis B finding.

Verify before reporting ROI: run promptfoo eval -c determinism.promptfooconfig.yaml from the skill dir and confirm every assertion passes. Only a config whose assertions run green counts as a shipped artifact — a config you wrote but never ran is an unasserted claim. If a test fails, fix the assertion (or the underlying Axis B fix) until all pass, then report ROI.

Few-shot

Axis A — resolve-repo step 1: "From the remote URLs, extract VCS Platform: github.com→github…". Model classifies → drifts on self-hosted hosts. Fix: case table. Assert: bats on detect_vcs.

Axis B — any skill ending in a "Return result" block with no schema: stdout varies (prose, fences, reordered fields). Fix: pin the block + "output only the block, no prose, no fences". Assert (promptfoo): not-icontains "```" + regex for the required keys.

Common Mistakes — STOP if you think...

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