Hard Screening Startup

What it does

When your agent uses it

• You need a reproducible
• Weighted-score verdict on a startup — not just a qualitative opinion

Example prompts

• “/venture-capital-intelligence:hard-screening-startup”
• “hard screen this startup”
• “run a hard screen on X”
• “/hard-screening-startup”

Requirements

• Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

1. 1GATHER COMPANY INFORMATION
2. 2CLAUDE: EXTRACT AND SCORE DIMENSIONS
3. 3PYTHON: COMPUTE WEIGHTED SCORE AND VERDICT
4. 4CLAUDE: INTERPRET SCORES
5. 5PYTHON: FORMAT FINAL REPORT

What it can do on your machine

Read from SKILL.md and the folder at commit 616deb5. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 2 files in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • python

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Hard Screening Startup loads about 1.1k tokens when it runs. Until then it costs about 191 tokens; SKILL.md has 357 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~191

When it runs · the whole SKILL.md, loaded when a task matches

~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from davepoon/buildwithclaude at commit 616deb5, republished under its MIT licence (© davepoon). 357 words, ~1,098 tokens.

name

hard-screening-startup

description

Deterministic Python-scored startup screening with full audit trail. Use when you need a reproducible, weighted-score verdict on a startup — not just a qualitative opinion. Triggered by: "/venture-capital-intelligence:hard-screening-startup", "hard screen this startup", "run a hard screen on X", "score this startup with Python", "give me an auditable screen", "run a scored evaluation on X", "give me a weighted score for this startup", "screen with numbers", "objective startup score", "reproducible screen", "investment scorecard for X", "score this company out of 100", "run the full screen on X". Claude Code only. Requires Python 3.x. For conversational soft-mode screening, use /venture-capital-intelligence:soft-screening-startup.

category

business-finance

platform

claude-code

requires

python3

{
  "company": "Company Name",
  "sector": "B2B SaaS",
  "stage": "Seed",
  "geography": "US",
  "scores": {
    "team": {"score": 0, "rationale": ""},
    "market": {"score": 0, "rationale": ""},
    "product": {"score": 0, "rationale": ""},
    "traction": {"score": 0, "rationale": ""},
    "business_model": {"score": 0, "rationale": ""},
    "competition": {"score": 0, "rationale": ""},
    "financials": {"score": 0, "rationale": ""},
    "risk_profile": {"score": 0, "rationale": ""}
  },
  "investment_thesis": "",
  "why_now": "",
  "key_risks": ["", "", ""],
  "dd_priorities": ["", "", ""],
  "comparables": ["", ""]
}

© davepoon, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts) in plugins/venture-capital-intelligence/skills/hard-screening-startup of davepoon/buildwithclaude.

• SKILL.md
• scripts/report_formatter.py
• scripts/verdict_calc.py

Open the folder on GitHubat commit 616deb5