SKILLEMALL.ai
GitHub ↗ RU Sign in

AD personal-finance-beancount

Professional personal finance advisor specializing in plain-text accounting with Beancount and Fava. Use when users need help with: (1) Analyzing spending habits and financial patterns from Beancount files, (2) Creating or understanding Beancount transactions and syntax, (3) Financial planning, budgeting, and investment advice, (4) Interpreting Fava reports and creating custom queries, (5) Organizing chart of accounts, (6) Double-entry bookkeeping principles, (7) Personal finance optimization and wealth building strategies. Provides analysis, education, and personalized recommendations while maintaining professional standards.

modbender/skill-library-mcp Agent Skills author: modbender MIT 8 files body ≈ 3 028 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Professional personal finance advisor specializing in plain-text accounting with Beancount and Fava.

As a process D 42/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureFinanceSoftware developmentData 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%
91
Run on models
none yet
Process rating
D
42/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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: 8. 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 42/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 40Consistency. Frontmatter name (personal-finance-beancount) differs from the folder (beancount-skill)
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (read, python) that frontmatter does not declare
    • 85Steps. 162 steps, 2 vague phrases
    • 100Execution cost. Instruction body is 3028 tokens
    • 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
    • +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
    • +3Description length 634: enough signal without eating the budget
    • +4Structure: 32 headings
    • +3Step-by-step instructions: 162 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (5 of 5)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.