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BB Fundraising Advisor

Professional fundraising advisory skill for startups - AI-powered project assessment, pitch deck generation, valuation analysis, investor matching, and PDF processing (financial statements, OCR, reports)

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 61 files body ≈ 4 942 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Professional fundraising advisory skill for startups - AI-powered project assessment, pitch deck generation, valuation analysis, investor matching, and PDF…

As a process B 66/100 · Nearly there — weak spots: inputs and preconditions, consistency, running it twice

AnalyzerFinanceData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
98
Quality 40%
68
Run on models
none yet
Process rating
B
66/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token examples/basic-usage.ts:237
    High-entropy token-like string (may be an id, hash or a credential) (placeholder value)
    async function exam…ons() {
    placeholder
  • low Secrets in code secret-high-entropy-token examples/basic-usage.ts:279
    High-entropy token-like string (may be an id, hash or a credential) (placeholder value)
    await exam…ons();
    placeholder

Files scanned: 61. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 66/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (Fundraising Advisor) differs from the folder (fundraising-advisor)
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 4942 tokens
  • 85Steps. 125 steps, 2 vague phrases
  • 100Tools and files. No external tools needed
  • 100Failures and branches. 3 branches, has a failure section
  • low 12 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • -216 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 203: enough signal without eating the budget
  • +4Structure: 32 headings
  • +3Step-by-step instructions: 125 items
  • +3Output format is stated explicitly
  • +4Has examples (23 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.