run-benchmark

A procedure for running repeatable quality tests on a coding-agent skill. It compares results with the skill loaded against results without it, using development and held-back test scenarios.

In plain words
What is it for?
Use it to run benchmark scenarios, calculate pass@k results, compare with-and-without-skill performance, and produce machine-readable benchmark reports.
Why use it?
It shows whether a skill actually improves results and catches regressions. Held-back scenarios help reveal when a skill was tuned only for the examples used during development.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/danielvm-git/bigpowers/run-benchmark
Any agent
npx skills add danielvm-git/bigpowers --skill run-benchmark
Clone the repo
git clone --depth 1 https://github.com/danielvm-git/bigpowers

Made for: Claude Code, Codex.

Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,108 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00057 $0.01108
Opus 5 $0.00028 $0.00554
Sonnet 5 $0.00011 $0.00222
Haiku 4.5 $0.00006 $0.00111

Measured 2d ago against content hash eabe6f59c9da, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

run-benchmark scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.cline/skills/run-benchmark/SKILL.md · 86 lines

How it starts

The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Run Benchmark

HARD GATE — Do NOT use benchmark scores to declare a skill "good" or "bad" in isolation. Benchmarks measure relative quality vs. a baseline — they catch regressions, they do not certify correctness.

Reads benchmark definitions from specs/benchmarks/, executes each scenario's grader with and without the skill loaded, and writes a structured pass@k report with delta grading that evolve-skill consumes.

With/Without-Skill Delta Grading

Every scenario runs N times (default 3) in two modes: with the skill loaded and without (bare agent with only CLAUDE.md). The delta Δ = pass@k_with − pass@k_without isolates the skill's causal contribution. A negative delta is a regression flag.

Train/Validation Split

Benchmark definitions partition scenarios into two sets:

Set Tag Purpose
Train split: train Development scenarios — used while iterating. Hitting 100% on train is expected.
Validation split: validation Held-out scenarios — the real quality signal. Overfitting train while validation stagnates is a design smell.

pass@k is reported separately for train and validation. Validation score is authoritative; train score is iteration guidance only.

Usage

bash scripts/run-benchmark.sh <skill-name>           # benchmark single skill
bash scripts/run-benchmark.sh --all                  # benchmark all with definitions
bash scripts/run-benchmark.sh <skill-name> --baseline # pin results as baseline

Process

  1. Locate definition — Read specs/benchmarks/<skill>.yaml. If absent, stop with message.

  2. Partition scenarios — Split by split field (train → iteration, validation → authoritative, default: validation).

  3. Run each scenario (N-run delta) — For each scenario, run grader N times (default 3, configurable via runs:):

    • Without skill: Agent with only CLAUDE.md/CONVENTIONS.md
    • With skill: Agent with the skill under test active
    • Code grader: bash -c <command>, exit 0 → PASS. Timeout: 15s.
    • Rubric grader: yes/no per criterion, ≥ 80% yes → PASS.
    • Record: {scenario_id: {with: [P/F,...], without: [P/F,...]}}

Read the full file on GitHub · 86 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 86 lines · 57 tokens per session scan A eabe6f59c9da

Subscribe to this mod's changes

run-benchmark is a skill published in the GitHub repository danielvm-git/bigpowers (156 stars, last pushed 25d ago), licensed MIT. It adds 57 tokens to every session and 1,108 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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