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AC financial-analysis-agent

Financial analysis skill for AI agents. Covers variance analysis, cash flow forecasting, month-end close automation, CFO commentary generation, 13-week cash flow dashboards, budget vs actual analysis, and financial statement review. Built by accounting professionals for agents that need to produce defensible financial outputs. Do NOT use for tax preparation, audit opinions, or regulatory filings — those require specialized compliance skills.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 2 files body ≈ 4 208 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Financial analysis skill for AI agents.

As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

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
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 50/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 70Execution cost. Instruction body is 4208 tokens
    • 85Steps. 60 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • low 11 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
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 445: enough signal without eating the budget
    • +4Structure: 42 headings
    • +3Step-by-step instructions: 60 items
    • +4Has examples (17 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.