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.
npx agentmods add skills/danielvm-git/bigpowers/run-benchmarknpx skills add danielvm-git/bigpowers --skill run-benchmarkgit clone --depth 1 https://github.com/danielvm-git/bigpowersWhat 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.
| Model | Per session | Once 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 |
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.
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
-
Locate definition — Read
specs/benchmarks/<skill>.yaml. If absent, stop with message. -
Partition scenarios — Split by
splitfield (train→ iteration,validation→ authoritative, default:validation). -
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,...]}}
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.
- 2d ago First seen · 86 lines · 57 tokens per session scan A eabe6f59c9da
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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