startup-evaluation — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited startup-evaluation (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
<skill_contract> <input>Company, stage, evaluation decision, customer evidence, traction, economics, team, runway, terms, and risks.</input> <output>An evidence-weighted health, venture-suitability, and financing assessment with one top constraint and cheapest test.</output> <done>Every score and verdict traces to evidence, fatal risks remain visible, and the next test has owner, threshold, budget, and stop.</done> <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.
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.
<intake>
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.
</intake>
<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>
<execute>
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.
Do not average away fatal risk. A healthy cash-flow business may still be a poor venture investment; a large market cannot rescue absent demand evidence.
</execute>
<evaluate>
Trace every score and verdict to the evidence ledger. Stress-test the conclusion against churn, paid acquisition dependence, founder conflict, financing timing, platform dependency, falling model prices, rising inference/service cost, and customer ROI that fails to become vendor margin. Calibrate confidence to the weakest decision-critical claim.
</evaluate>
NEEDS_INPUT: the evaluation decision, stage, or company boundary is unclear.INSUFFICIENT_EVIDENCE: the requested verdict depends on unavailable demand, retention, economics, or terms data.VERIFY_FAILED: a score lacks evidence or contradicts the ledger; rescore or mark unknown.BUDGET_STOP: return the provisional constraint and highest-value evidence request.Return status, result (verdict, scorecard, top constraint, fatal risks, and next test), evidence (fact/assumption ledger), unknowns, and next_action with owner and threshold.
without calling it value capture; test pricing, service labor, and inference economics separately.
</skill_contract>
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.