SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.
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 skills add benchflow-ai/skillsbench --skill contract-playbook-reviewgit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/benchflow-ai/skillsbench/contract-playbook-review)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/contract-playbook-review"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/contract-playbook-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/contract-playbook-review"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/contract-playbook-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00118 | $0.01894 |
| Opus 5 | $0.00059 | $0.00947 |
| Sonnet 5 | $0.00024 | $0.00379 |
| Haiku 4.5 | $0.00012 | $0.00189 |
Grade A, and why
contract-playbook-review 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 11d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Contract playbook review
How a contract reviewer applies a structured deviation policy to a contract, clause by clause, and emits a structured review.
The general workflow
A playbook is a list of clause rules. Each rule names a clause, describes the policy in prose, and encodes the policy as machine-checkable fields (e.g. max_years, acceptable_jurisdictions, must_be_present). Review is a four-step loop, repeated once per clause:
- Locate the corresponding provision in the contract (it may not be present at all).
- Apply the rule — check the contract's position against the policy fields.
- Classify the outcome as
ok,risk, orreject. - Record the finding: a verbatim excerpt that grounds it, a rationale, and the playbook's prescribed action.
Walk the playbook in order. Don't skip clauses just because the contract is silent on them — silence is itself a finding (found = false) and may trigger an action (e.g. "request_addition").
Rule types you will see
A handful of rule shapes cover almost every playbook entry. Recognise them and you can apply them mechanically:
| Rule shape | Field example | "ok" means |
|---|---|---|
| Numeric ceiling | max_years: 3, max_months: 24 |
Contract value ≤ ceiling |
| Numeric floor | min_years: 1 |
Contract value ≥ floor |
| Must be present | must_be_present: true |
Provision exists |
| Must be absent | must_be_absent: true |
Provision (or specific phrasing) does NOT exist |
| Acceptable set | acceptable_jurisdictions: [...] |
Contract value ∈ set |
| Required feature | must_allow_destruction: true |
Provision contains the feature |
| Conjunction of features | must_require_notice && must_require_cooperation |
All features present |
| Conditional structural | must_be_bilateral_if_present |
Provision absent OR present-and-symmetric |
A playbook entry may also carry a source field naming the published authority the rule is anchored to (a survey, statute, treatise, or firm-published practice guide). When the entry has a source, treat it as load-bearing — the rule's threshold is not arbitrary; it is the cited source's stated value.
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.
- 11d ago First seen · 96 lines · 118 tokens per session scan A fa1cca330d8d
contract-playbook-review is a skill published in the GitHub repository benchflow-ai/skillsbench (1,760 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 118 tokens to every session and 1,894 once invoked, about $0.0006 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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