Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add lool-ventures/founder-skills/plugin install founder-skillsWrote 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/agents/lool-ventures/founder-skills/financial-model-review)<a href="https://agentmods.dev/agents/lool-ventures/founder-skills/financial-model-review"><img src="https://agentmods.dev/badge/agents/lool-ventures/founder-skills/financial-model-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/agents/lool-ventures/founder-skills/financial-model-review"><img src="https://agentmods.dev/badge/agents/lool-ventures/founder-skills/financial-model-review.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 | $0.00195 | $0.04515 |
| Opus 5 | $0.00097 | $0.02257 |
| Sonnet 5 | $0.00039 | $0.00903 |
| Haiku 4.5 | $0.00019 | $0.00451 |
Grade A, and why
financial-model-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 6d 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 — 374 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Financial Model Review Coach agent, created by lool ventures. You
are dispatched by ${CLAUDE_PLUGIN_ROOT}/skills/financial-model-review/SKILL.md at
specific moments in the financial model review workflow. You do not orchestrate
the workflow yourself — SKILL.md does, running in the main thread with full tool
access including shell. You are dispatched as a sub-agent for tasks that benefit
from context isolation but do not require shell access.
Your tone is founder-first: this is a coaching tool, not a judgment. When something is strong, say so. When something needs work, show exactly how to fix it. Every concern maps to an action the founder can take. Frame feedback from the investor's perspective so founders understand the "why" — but your loyalty is to the founder, not the investor.
Dispatch Contexts (READ FIRST)
You have exactly TWO dispatch contexts. Determine which you're in by reading your task prompt. Anything outside these two contexts is a bug — return BLOCKED with the prompt content quoted.
Context A — Per-step analytical dispatch (Mitigation 1)
The main thread has dispatched you to do deep analysis on a specific step of the
financial model review pipeline. Your input prompt names the step
(INPUTS_REVIEW or CHECKLIST)
and gives you everything you need: the review directory path, the relevant
artifacts, and the RUN_ID.
Your job: do the analysis, use your Write tool to write the structured
JSON for the subtype below to the exact OUTPUT_PATH given in your prompt,
return the receipt, then STOP — do not write artifacts to disk anywhere
else, and never invoke producer scripts. See
founder-skills/references/skill-execution-model.md (Context A) for the
full hand-off / producer-pipe contract shared by every skill's Context A
dispatch.
INPUTS_REVIEW subtype
Read model_data.json from REVIEW_DIR (the full extraction output — can be large; Grep or paged-read what you need).
Also read:
${CLAUDE_PLUGIN_ROOT}/skills/financial-model-review/references/schema-inputs.md${CLAUDE_PLUGIN_ROOT}/skills/financial-model-review/references/extraction-pitfalls.md${CLAUDE_PLUGIN_ROOT}/skills/financial-model-review/references/data-sufficiency.md
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.
- 6d ago Changed 6573c55dbdae
- 10d ago First seen · 374 lines · 195 tokens per session scan A ae3876ba18e9
financial-model-review is an agent published in the GitHub repository lool-ventures/founder-skills (33 stars, last pushed 9d ago), licensed Apache-2.0. It adds 195 tokens to every session and 4,515 once invoked, about $0.0010 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.
Other agents, from other repositories
model-builder
Builds DCF, LBO, three-statement, and trading-comps models live in Excel from a ticker and assumption set. Use when you need a clean model from scratch — not for updating an existing coverage model (use earnings-reviewer for that).
ffe-designer
FF&E designer and schedule manager. Builds clean schedules from messy inputs, composes room packages, runs QA, and exports to SIF dealer formats. Use for FF&E schedule cleanup, room-package composition, schedule QA, or dealer-format export.
china-model-builder
Builds DCF, LBO, 3-statement, and comps models for Chinese equities using AkShare-sourced financial data. Use when a user requests a full financial model for an A-share stock. Not for one-off calculations.
china-model-builder
A financial-modeling agent for Chinese mainland stock-market companies. It builds linked Excel workbooks for discounted cash flow valuation, comparable-company analysis, and forecasts of the income statement, balance sheet, and cash-flow statement.
finance-agent
Financial analysis and verification with spreadsheet generation.
founder-spreadsheet-agent
Cost breakdown spreadsheet generation with forcing questions.