Skill Harness

An evaluation harness for reusable AI agent skills — a skill disposition engine. First-class Claude Code support, built to extend to other agent ecosystems.

You installed a skill that's supposed to make your AI assistant better. Is it actually doing anything?

Skills ride along in every conversation and take up context space, so a skill that does nothing isn't free — it's dead weight. This tool measures whether a skill earns its slot: it runs the same task with the skill and without it, and compares. You get one of three honest answers: it helped, it didn't, or this can't be measured yet — and that third answer comes back labeled UNMEASURED instead of a made-up score.

Most benchmarks hand you a number no matter what. This tool refuses. That refusal is the product.

Status: v0.2.0 — the keep/cut verdict layer is live; zero measured KEEPs to date. The tool now answers the slot question with one of three verdicts: KEEP (measurably earns its slot), CUT — either subsumed (the model already does the task without the skill) or no-lift (measured on a task the model needed help with, and it didn't deliver) — or CAN'T-TELL-YET (the evidence doesn't support a verdict). A made-up score is never emitted in place of CAN'T-TELL-YET.

Run live, end-to-end (Inspect + Docker + deterministic oracle, direct Anthropic frontier model, 3 epochs each, store-backed): two real skills — append-only-evidence-design and a hardened git-pull-rebase-trap — both returned CUT (subsumed) at a bare-arm pass rate of 1.00: the model passed every no-skill epoch, so there was nothing for the skill to add. That is the tool doing its job, not a failure.

Honest maturity. Zero measured KEEPs exist in the program to date. The paired Full-vs-Null path that would produce one is coded and cost-free-validated but has never fired — it launches only on the first task whose no-skill screen returns a pass rate below 1, and so far every screened skill ceilings at 1. Store-backed coverage is partial: only a handful of the ~26 screen verdicts derive from an append-only evidence store (the two live runs plus a curated batch-1 backfill); the rest are prose-backed pending backfill. A tool that has never said KEEP, and says so plainly, is more trustworthy than one that manufactures a KEEP to look useful. The v0.1→v0.2 re-aim that got us here is pre-registered and published, not papered over: docs/findings/v0.2-reaim-gate.md.

The question other skill evals don't answer

Most skill-evaluation tools score the skill file itself — lint it, have an LLM judge grade it, simulate runs against it. That answers "is this artifact well-made?" It's a reasonable question. It is not the question that decides an install.

The question that decides an install is "is my agent measurably better with this than without it?" — and the only way to answer it is a paired comparison against the no-skill baseline. That's what this harness does, with three properties the certification-style tools don't have:

1. A control arm. Every measurement is with-vs-without, not a score in a vacuum. Frontier models often ace the task with no skill at all — in our own testing, "the model already does this fine" has been the most common truthful result. A score-in-a-vacuum can't tell you that.
2. An honest failure mode. When the evidence doesn't support a verdict — too noisy, task too easy, judge uncalibrated — the answer is UNMEASURED, stored and first-class, never an estimate that launders noise into a finding.
3. A paper trail. Evidence admissibility is checked and snapshotted at write time in an append-only store. Every number can show its work; inadmissible data is kept but never aggregated.

This harness is also the instrument behind the evidence records in MrBinnacle/skills — a living skill collection where skills are re-screened on each major model release and publicly retired when models stop needing them.

60-second start — no API key, no cost

pip install git+https://github.com/MrBinnacle/skill-harness
skill-harness skill audit path/to/your/SKILL.md

Two commands to your first verdict. skill audit is fully offline: a structural check against Anthropic's authoring spec, plus a preview of what a paid run could measure about this skill today and which claims would come back UNMEASURED. Real output:

OFFLINE AUDIT — no API calls, no cost
  skill:  caveman
  body:   41 lines / 233 words

  PASS  name            name 'caveman' meets spec
  PASS  body-length     body 41 lines (budget 500)
  INFO  description-unparsed-block-scalar
        description uses a multi-line YAML block scalar, which this audit's
        minimal frontmatter parser cannot read — checks skipped
        (UNMEASURED, not passed)

Evaluability preflight — what a paid run could measure today:
  Tier-1 mechanical axes: citation_presence_per_flag, compliance_proxy,
  hedge_index, structure_score, verbosity (style-shaped only).
  Behavior-shaped claims (correctness, tool use, outcomes): no mechanical
  instrument in v0.1 → verdict would be UNMEASURED, not an estimate.

Summary: 3 pass · 0 warn — UNMEASURED is a verdict, not a failure.

Note the third line of that report: when the tool can't read something, it says so and skips the check — it does not pass what it did not measure. That's the whole design, applied at every layer. --strict exits 1 on warnings for CI use. On Windows terminals, set PYTHONUTF8=1 first.

Measuring for real (API key required)

skill-harness skill init path/to/SKILL.md --execute   # extract testable claims
skill-harness run ablation <skill_id> --execute        # run the with/without comparison
skill-harness run evaluate-skill <skill_id>            # aggregate verdicts

Both skill init and run ablation accept either ANTHROPIC_API_KEY (direct) or OPENROUTER_API_KEY (auto-routed). Every run command is dry-run by default; --execute is required to spend money, and per-run/daily budget caps are enforced. Reproduction script and details: examples/.

What it measures today — and what it refuses to

v0.1's honest scope: directional effects on style (verbosity, hedging, structure, citation presence), measured claim by claim with a single-turn subject. Behavior-shaped claims — correctness, tool use, outcomes — have no mechanical instrument in v0.1 and return UNMEASURED. LLM-judge verdicts count only from a calibrated (judge, axis) pair: position-swapped, length-controlled, injection-defended, with human agreement measured before a single judged verdict is admitted (docs/concepts/why-unmeasured.md).

The v0.2 re-aim (gate doc) landed in v0.2.0: the whole-skill Stage-0 screen (does the model pass without the skill?) is the primary, dominant contrast, run against an agentic multi-turn subject with deterministic outcome oracles (file_contains, command_succeeds), and the harness configuration is captured as an admissibility field — because published agentic-benchmark experience puts harness-induced variance at 10–20 points on identical model weights, larger than most skill effects. The claim-level style path above still exists as the offline/audit surface. What has not yet fired is the paired Full-vs-Null run — see the status note at the top: it launches only when a screen returns a sub-1 pass rate, and none has yet.

How it compares

	skill-harness	skill-eval-harness	promptfoo	Inspect
Primary question	is this claim about a skill backed by admissible evidence?	did this skill improve outcomes on my cases?	which prompt/config is better/safer?	how does this model/agent score?
Unit	claim (v0.1) → whole skill (v0.2)	whole skill, paired with/without + component ablations	prompt/config matrix	task/eval
When evidence is weak	refuses: UNMEASURED verdict; inadmissible rows are stored but never aggregate	flags: oracle tiers, critical-severity veto, audit warnings	—	—
LLM judges	admissible only from a calibrated (judge, axis) pair	user-supplied judge command, uncalibrated by design	judge/rubric assertions	model-graded scorers
Maturity	v0.2.0, 0.x (keep/cut layer live, 0 measured KEEPs)	active v0.5.x	mature, 23k+ stars	mature, institutional

Honest guidance: if you want the most featureful skill benchmarking today, use adewale's skill-eval-harness. Use this harness when what you care about is whether the number deserves to exist.

Why this exists

With-vs-without skill benchmarking at 3 runs apiece is now common practice, and it is trap-laden: run-to-run noise (we measured ±17.6% on agentic tasks) swallows all but huge effects; hand-matched tasks quietly bias the result; pass/fail test banks price a skill's cost while structurally missing its benefit; and synthetic test oracles leak their own answers through docstrings. The measured findings, with evidence grades: docs/findings/why-naive-skill-benchmarks-mislead.md. The independent literature agrees on the stakes: low-quality skills don't just fail to help — they actively degrade performance. A tool that can honestly say "no measurable effect" is the missing instrument.

Dig deeper

• docs/case-studies/ai-slop-sentinel-under-ablation.md — the discipline catching its own author three times before a contaminated result could ship. The deliverable is the chain of refusals, not a number.
• docs/case-studies/double-ceiling-structurally-unmeasured.md — a frontier agent passed 14/14 no-skill runs on two deliberately hardened tasks: on that task class there is nothing for a skill to improve, and any benchmark claiming otherwise owes you its no-skill pass rate first.
• docs/case-studies/displaced-enforcement-skill-ablation-blind-spot.md — a scope boundary of skill-ablation: when a discipline's real firing lives in a hook, not in the model reading the skill, ablating the text measures the wrong layer — so a null there is not the discipline's verdict, and to measure the discipline you ablate the hook instead.
• docs/PRD.md — full specification: evidence model, oracle tiers, admissibility rules, CLI surface.
• Evidence store: src/skill_harness/storage/migrations_sql/evidence/ (append-only, trigger-enforced) + migrations_sql/runtime/ (mutable operational state).

MIT licensed. Issues and PRs welcome — see CONTRIBUTING.md.