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AF startup-financial-modeling

This skill should be used when the user asks to "create financial projections", "build a financial model", "forecast revenue", "calculate burn rate", "estimate runway", "model cash flow", or requests 3-5 year financial planning for a startup.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 2 802 tokens Open the sourcegithub.com↗ analyzed 2 d ago

This skill should be used when the user asks to "create financial projections", "build a financial model", "forecast revenue", "calculate burn rate"…

As a process F 50/100 · Will not run — References files that are not bundled: references/model-templates.md, references/unit-economics.md, references/fundraising-scenarios.md

GeneratorFinanceData 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
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
F
50/100
Will not run
References files that are not bundled: references/model-templates.md, references/unit-economics.md, references/fundraising-scenarios.md
Tools and files w 18
0
Result and completion w 14
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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 · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/model-templates.md
  • warning missing-ref reference to a missing file: references/unit-economics.md
  • warning missing-ref reference to a missing file: references/fundraising-scenarios.md
  • warning missing-ref reference to a missing file: examples/saas-financial-model.md
  • warning missing-ref reference to a missing file: examples/marketplace-model.md
  • warning missing-ref reference to a missing file: examples/scenario-analysis.md

Process rating: all ten parameters 50/100

Will not run. References files that are not bundled: references/model-templates.md, references/unit-economics.md, references/fundraising-scenarios.md
  • 0Tools and files. 6 referenced file(s) missing: references/model-templates.md, references/unit-economics.md, references/fundraising-scenarios.md
  • 0Result and completion. Does not say what the result is
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 5 mutating operations with no state check
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 179 steps
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2802 tokens
  • low 13 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 6 example trigger phrases
  • +3Description length 242: enough signal without eating the budget
  • +4Structure: 35 headings
  • +3Step-by-step instructions: 179 items
  • +4Has examples (9 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.