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 LegalQuants/lq-ai --skill playbook-easy-extractgit clone --depth 1 https://github.com/LegalQuants/lq-aiWrote 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/legalquants/lq-ai/playbook-easy-extract)<a href="https://agentmods.dev/skills/legalquants/lq-ai/playbook-easy-extract"><img src="https://agentmods.dev/badge/skills/legalquants/lq-ai/playbook-easy-extract/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/legalquants/lq-ai/playbook-easy-extract"><img src="https://agentmods.dev/badge/skills/legalquants/lq-ai/playbook-easy-extract.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 | $0.00103 | $0.01613 |
| Opus 5.5 | $0.00041 | $0.00645 |
| Sonnet 5.5 | $0.00021 | $0.00323 |
| Haiku 4.5 | $0.00010 | $0.00161 |
Grade A, and why
playbook-easy-extract 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 1mo 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Playbook Easy Extract
Extract the negotiated positions from a single contract — the clauses where the parties take a substantive stance on a recurring contract issue — and emit a structured list the Easy Playbook generation pipeline can cluster.
When this skill applies
This skill is internal to the Easy Playbook generation pipeline (M3-A6) — it is not a user-facing skill. The pipeline calls it once per uploaded corpus document; the per-document output is then clustered across the corpus to detect modal positions, and the result is assembled into a draft playbook the user-attorney reviews and edits.
Apply when given a single contract text and asked to enumerate the negotiated positions it takes. Do not apply for contract review, redlining, or any user-facing task — those are the job of the per-contract-type skills (nda-review, msa-review-saas, etc.).
Workflow
Read the contract end-to-end before emitting any output. Identify every clause that:
- Takes a substantive position on a recurring contract issue. Recurring issues are those that appear across most contracts of the same family: confidentiality definition, term, limitation of liability, indemnification, payment terms, governing law, etc. A clause that imposes an obligation, defines a key term, allocates risk, or sets a threshold is "substantive."
- Is recognizable as a position (not boilerplate). Skip pure mechanical clauses (notices, severability, integration) — they don't represent a negotiated stance worth clustering. Include them only when the contract takes an unusual position on them.
- Is contained in identifiable, contiguous text. Don't emit overlapping or fragmentary spans — pick the section/sentence that captures the position.
For each identified clause, emit one entry. The same contract typically yields 5–20 entries depending on length and complexity.
Issue labeling
The issue field is a short, descriptive label the downstream clustering step uses to group like clauses across the corpus. Use common contract-issue vocabulary. Examples:
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 121 lines · 103 tokens per session scan A 2f5d1949f2d6
playbook-easy-extract is a skill published in the GitHub repository LegalQuants/lq-ai (156 stars, last pushed today), licensed Apache-2.0. It adds 103 tokens to every session and 1,613 once invoked, about $0.0004 per session on Opus 5.5. 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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