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AC stock-evaluator-v3

Comprehensive evaluation of potential stock investments combining valuation analysis, fundamental research, technical assessment, and clear buy/hold/sell recommendations. Use when the user asks about buying a stock, evaluating investment opportunities, analyzing watchlist candidates, or requests stock recommendations. Provides specific entry prices, position sizing, and conviction ratings.

modbender/skill-library-mcp Agent Skills author: modbender MIT 5 files body ≈ 24 324 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Comprehensive evaluation of potential stock investments combining valuation analysis, fundamental research, technical assessment, and clear buy/hold/sell…

As a process C 60/100 · Has gaps — weak spots: inputs and preconditions, consistency, execution cost

AnalyzerData and analyticsFinancetype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
C
60/100
Has gaps
Inputs and preconditions w 11
0
Execution cost w 6
10
Consistency w 8
40
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 24324 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 60/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 10Execution cost. Instruction body is 24324 tokens: crowds the task out of the window
  • 40Consistency. Frontmatter name (stock-evaluator-v3) differs from the folder (stock-evaluator)
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 60Failures and branches. 2 branches
  • 85Steps. 601 steps, 1 vague phrases
  • 100When it triggers. States when to use and when not to
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 28 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
  • -246 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 392: enough signal without eating the budget
  • +4Structure: 95 headings
  • +3Step-by-step instructions: 601 items
  • +3Output format is stated explicitly
  • +4Has examples (5 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.