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 Mark393295827/third-brain-v7-skills --skill startup-evaluationgit clone --depth 1 https://github.com/Mark393295827/third-brain-v7-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/skills/mark393295827/third-brain-v7-skills/startup-evaluation)<a href="https://agentmods.dev/skills/mark393295827/third-brain-v7-skills/startup-evaluation"><img src="https://agentmods.dev/badge/skills/mark393295827/third-brain-v7-skills/startup-evaluation/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/mark393295827/third-brain-v7-skills/startup-evaluation"><img src="https://agentmods.dev/badge/skills/mark393295827/third-brain-v7-skills/startup-evaluation.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 | $0.00031 | $0.01196 |
| Opus 5 | $0.00015 | $0.00598 |
| Sonnet 5 | $0.00006 | $0.00239 |
| Haiku 4.5 | $0.00003 | $0.00120 |
Grade A, and why
startup-evaluation 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 12d 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Startup Evaluation
<skill_contract> Company, stage, evaluation decision, customer evidence, traction, economics, team, runway, terms, and risks. An evidence-weighted health, venture-suitability, and financing assessment with one top constraint and cheapest test. Every score and verdict traces to evidence, fatal risks remain visible, and the next test has owner, threshold, budget, and stop. <non_goals>Investment advice by narrative, averaging away fatal risk, or treating market size, conviction, or pitch quality as demand.</non_goals>
Evaluate the company the evidence supports, not the story it tells. Distinguish business health, venture suitability, and financing readiness; they are different decisions.
Usage Template
Provide: company, stage, startup type, evaluation decision, customer, problem, product, traction, team, economics, runway, round terms, and known risks. Use the rubric in references/evaluation-rubric.md when scoring is requested.
Workflow
Classify stage, type (SME, innovation-driven, venture-scale, hard-tech, AI-native), lens (founder, diligence, fundraising, pivot), and evidence state. Define the decision and time horizon before calculating a score.
<unknowns_gate>
Separate facts, assumptions, self-reported claims, and missing evidence. If the target decision or company identity is unclear, return NEEDS_INPUT. Continue with missing metrics only when the output is explicitly provisional and each gap has a probe.
</unknowns_gate>
- Rank demand evidence from belief and interviews through behavior, payment, retention, expansion, and referral.
- Score eight dimensions using stage-adjusted weights: pain/beachhead, market/timing, value step-change, PMF/traction, business model/economics, team/governance, capital/runway, and moat/risk.
- For investor work, cross-check 5T: Team, Target Market, Tech/Product, Traction, Terms.
- For AI-native or hard-tech cases, test what remains defensible as components cheapen and identify physical, regulatory, deployment, or supply-chain bottlenecks. For AI value capture, separate usage, productivity, customer ROI, and vendor profit; do not infer durable economics from token volume or revenue growth alone.
- Separate the spending engine (CapEx, inference, integration, service labor, energy, and deployment cost) from the earning engine (retention, expansion, pricing power, gross margin, and free cash flow). Test who owns institutional learning: workflow exceptions, context, permissions, and feedback write-back.
- Diagnose runway and whether spend buys evidence for the next milestone.
- Name the single constraint most likely to invalidate or unlock the company.
- Specify the cheapest test, threshold, owner, budget, and stop condition.
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.
- 12d ago First seen · 99 lines · 31 tokens per session scan A cfc47388f48d
startup-evaluation is a skill published in the GitHub repository Mark393295827/third-brain-v7-skills (138 stars, last pushed 21d ago), licensed MIT. It adds 31 tokens to every session and 1,196 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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