Ent diligence

Skill kalyvask/entrepreneurship-lessons/.claude/skills/ent-diligence

PMF framework spine + supporting methodologies (Lean Startup, Customer Development, RDI, Mom Test, Disruption, Market Type) and 24 Claude Code skills, from curious mind to PMF.

Install

npx -y skills add kalyvask/entrepreneurship-lessons --skill ent-diligence

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Run verification-first diligence on a set of factual claims — verify / flag / discard with sources, a what-must-be-true frame, and a gap list. Two directions; use when the founder is preparing their own memo or pitch for sharp scrutiny ("diligence my memo", "am I ready for investors"), or when they're evaluating someone else's claims (a company, a deal, a partnership) — the discipline that feeds the investment-style memo in the thesis ledger.

SKILL.md

5.3 KB, as published. Nobody here has run it

Paths: file references like frameworks/pmf.md are repo-root-relative. When this skill runs from an installed plugin, the same files ship with the plugin — resolve them under the plugin root (the CLAUDE_PLUGIN_ROOT environment variable).

Diligence (verification-first)

You pressure-test factual claims before a decision rides on them. Full playbook in playbooks/diligence.md. The one rule everything follows from: every unverified statement — yours, theirs, or an AI's — is a hypothesis, not a fact. Your job is to sort the claim set into verified / flagged / discarded, say what must be true for the decision to hold, and name the gaps.

This skill checks facts. It does not replace behavioural evidence: for PMF claims, a verified document never outranks a desperate customer (playbooks/validation_sequence.md).

What you ask the user

Which direction? Their own claims before sharing (founder self-diligence), or someone else's claims they're evaluating (a company, a deal, a partner)?
What decision does this feed? (Share the memo? Invest? Partner? Walk away?) The decision defines which claims are load-bearing.
The materials. The memo / deck / data; for self-diligence with a venture workspace, you read pmf_dashboard.md, experiment_log.md, and interviews.csv yourself rather than asking.

The loop you run

Enumerate the claim set. Pull out the load-bearing factual claims — team backgrounds, funding amounts (and their actual structure: grant vs. loan vs. intent), market numbers, named partnerships, traction and retention figures. Make vague claims concrete before judging them.
Verify / flag / discard, claim by claim. For each: what was found, the source (insist on a primary source or URL — the request catches fabricated citations), and a status. Default to unverified when uncertain; accuracy beats impressiveness. Work section by section.
What must be true. For the decision to hold, lay out the assumptions (market, execution, competitive, team) and rate each: likelihood / verifiability-before-deciding / impact if wrong. Spend the remaining effort on low-verifiability deal-breakers — Pareto, not completeness (frameworks/judgment_and_pareto.md).
Gaps. What still matters but couldn't be confirmed: why it matters, how to fill it (data room, management call, expert network, a behavioural test), what to assume if it can't be filled.
Bear case — route it. Send the surviving thesis to /ent-red-team for the adversarial pass. Don't soft-run it inline; the separation is the point.

Output format

DILIGENCE — [target] — feeding [decision]

CLAIM VERIFICATION
| # | Claim | Found | Source | Status (verified / partial / unverified) |

WHAT MUST BE TRUE (for [decision] to hold)
| Assumption | Category | Likelihood | Verifiable before deciding? | Impact if wrong |

GAPS THAT MATTER
- [gap] — why it matters / how to fill / what we assume if unfillable

READ
[What the verified record actually supports — distinct from what the materials claim]
CONFIDENCE: [low / medium / high] — and the single finding that would flip it

NEXT
→ /ent-red-team on the surviving thesis (mandatory before a high-stakes yes)
→ if evaluating a company: log the durable lesson to thesis_ledger.md via /ent-thesis
→ if self-diligence: fix or label every flag in the memo before sharing — don't polish flags away

Discipline you enforce

No source, no fact. A claim that can't be traced is flagged or discarded — never carried forward because it sounds right.
Vague numbers get decomposed. "Backed by $X of government money" → instrument, agency, terms, disbursed-or-announced.
AI structures, humans own the math. Models and scenarios: skeleton from the assistant, arithmetic checked by the user (frameworks/unit_economics.md).
Flags stay visible. In self-diligence, an honest "unverified — here's how we'll know" beats a confident line that collapses under one question.
Behaviour outranks documents for any PMF claim. Verified facts about a market are not evidence of desperation.

What you DON'T do

Don't run the bear case yourself — route to /ent-red-team.
Don't accept "trust me" or the target's own deck as verification.
Don't verify everything equally — the claims that carry the decision get the effort.
Don't write to the venture workspace; this is an assessment. (Lessons from an evaluation go to the thesis ledger via /ent-thesis; memo fixes are the founder's edit.)
Don't let a clean fact-check stand in for behavioural validation.

Source

Synthesized in this repo's own words from Diego Oppenheimer's GSB Applied-AI session on AI for startup investing. Full method in playbooks/diligence.md; provenance in SOURCES.md.