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 zgbrenner/agentcounsel --skill diligence-issue-extractiongit clone --depth 1 https://github.com/zgbrenner/agentcounselWrote 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/zgbrenner/agentcounsel/diligence-issue-extraction)<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/diligence-issue-extraction"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/diligence-issue-extraction/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/zgbrenner/agentcounsel/diligence-issue-extraction"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/diligence-issue-extraction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00039 | $0.03027 |
| Opus 5 | $0.00019 | $0.01514 |
| Sonnet 5 | $0.00008 | $0.00605 |
| Haiku 4.5 | $0.00004 | $0.00303 |
Grade A, and why
Diligence Issue Extraction 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diligence Issue Extraction
Purpose
Extract and organize material issues from a target's due-diligence documents into a structured issues memo, ready for attorney review. The skill inventories the provided documents, maps them to diligence categories, applies a stated materiality threshold, extracts findings per category, flags successor-liability exposure, and identifies gaps in the document set. It produces draft legal work product for attorney review — not legal advice and not a final due-diligence opinion.
Use When
- A user asks to "pull issues from these diligence documents," "give me a first-pass issues list," or "flag the red flags in the data room."
- The user has uploaded or pasted due-diligence documents and needs a structured issues memo organized by category and severity.
- The transaction is an M&A deal or an investment round and a first-pass extraction of material concerns is needed before attorney analysis.
- The user needs a gap analysis identifying missing diligence categories or document types.
- The user needs a successor-liability sweep surfaced from the provided materials.
Required Inputs
- The target documents — uploaded or pasted in full. This skill works only from documents provided in the conversation; it does not query a data room or external source.
- Deal context — deal name or working title; the user's side (buy-side or sell-side); and the diligence category or categories under review (e.g., corporate, material contracts, IP, employment, litigation).
- Materiality threshold — the dollar amount, percentage, or qualitative standard that defines a material issue for this deal. If not supplied, stop and request it before proceeding.
- Optional: the firm's or client's preferred diligence categories, severity scheme, and house issues-memo format. Where provided, apply them; where not provided, use the default category and severity framework in this skill.
If the target documents are not provided, stop and request them. Do not reconstruct or assume document contents, defined terms, or diligence findings from background knowledge.
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.
- 11d ago First seen · 159 lines · 39 tokens per session scan A 24dc1e7b46f9
Diligence Issue Extraction is a skill published in the GitHub repository zgbrenner/agentcounsel (19 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 3,027 once invoked, about $0.0002 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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