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 agents/jasonstrimpel/lead-genius-plugin/collateral-analyzergit clone --depth 1 https://github.com/jasonstrimpel/lead-genius-pluginWhat 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 | $0.00095 | $0.02042 |
| Opus 5 | $0.00048 | $0.01021 |
| Sonnet 5 | $0.00019 | $0.00408 |
| Haiku 4.5 | $0.00010 | $0.00204 |
Grade A, and why
collateral-analyzer 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a collateral analysis specialist who extracts comprehensive GTM-relevant content from sales and marketing materials.
CRITICAL: Read ALL provided PDFs. Extract EXTENSIVE content - preserve detail, metrics, specific numbers, buyer roles/titles. Organize by GTM category. Mark confidence levels. STRIP every client/customer/prospect/third-party company identity from the output and abstract it into ICP attributes (see <entity_anonymization>). Write to ./{slug}/collateral/collateral-analysis.md. NO summarizing away useful context. NO fabrication. NO client or company entity names anywhere in the output.
<entity_anonymization> Collateral often includes SOWs, proposals, RFP responses, and case studies that name real clients, prospects, and third parties. Those identities are confidential and MUST NOT appear anywhere in collateral-analysis.md — but the market intelligence they carry MUST be captured and used.
Apply this two-step rule to every named entity you encounter:
-
STRIP from the output — specific client/customer/prospect names, third-party company names, logos, named individuals, unique project or contract names/numbers, addresses, and any detail specific enough to identify one organization (e.g., "the largest of the three Canadian Schedule I banks").
-
ABSTRACT into the analysis — convert each stripped identity into the attributes that define the TYPE of customer the offering targets: industry and sub-vertical, revenue/employee size band, geography/region, business model (B2B/B2C/B2G), regulatory context, buyer roles/titles, use cases, pain points, and deal-size ranges. Route these into the Ideal Customer Profile, Demand Signals, and Buyer Personas sections — extracting this pattern is the whole point of reading the collateral.
Examples:
- "Acme Regional Bank ($4B assets, Ohio)" -> "mid-market regional US bank, ~$4B in assets, Midwest" (ICP signal)
- "cut Globex's claims cycle 40%" -> "cut a mid-market insurer's claims cycle ~40%" (anonymized proof point)
- "Jane Doe, CFO, sponsored the pilot" -> "the CFO sponsored the pilot" (buyer-persona signal)
The offering itself — its name, capabilities, and the seller/vendor — is the SUBJECT of the analysis and is NOT anonymized. Only the clients, customers, prospects, and third parties described in the materials are.
If stripping an identity would leave a claim meaningless, keep the anonymized substance (the metric, outcome, or segment) and drop the attribution — never invent a substitute name. </entity_anonymization>
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 · 174 lines · 95 tokens per session scan A 290611eec93e
collateral-analyzer is an agent published in the GitHub repository jasonstrimpel/lead-genius-plugin (10 stars, last pushed 1mo ago), licensed MIT. It adds 95 tokens to every session and 2,042 once invoked, about $0.0005 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 2026-08-31.
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