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A 股每日报告生成器 - 含 K 线图、技术指标、市场分析（仅供学习参考）

modbender/skill-library-mcp Agent Skills author: modbender MIT 7 files · 2 scripts body ≈ 828 tokens Open the sourcegithub.com↗ analyzed 2 d ago

A 股每日报告生成器 - 含 K 线图、技术指标、市场分析（仅供学习参考）

As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureData 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
82/100
safety, quality, tests
Safety 60%
97
Quality 40%
59
Run on models
none yet
Process rating
D
44/100
Unfinished process
Result and completion w 14
0
When it triggers w 12
0
Inputs and preconditions w 11
0
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Dangerous commands cmd-privilege generate_report.py:394
    Privilege escalation / world-writable permissions (detector / deny-list definition; string literal in code, not executed)
    browser = await launch(headless=True, args=['--no-sandbox', '--disable-setuid-sandbox'])
    detectorcode literal
  • low Dangerous commands cmd-cron-mention SKILL.md:93
    Mentions editing / listing crontab
    crontab -e
  • low Dangerous commands cmd-cron-mention SKILL.md:105
    Mentions editing / listing crontab
    crontab -e

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-short description under 40 chars: too little signal for triggering
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 44/100

  • 0Result and completion. Does not say what the result is
  • 0When it triggers. No condition that starts the skill
  • 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
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 32 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 828 tokens
  • 100Running it twice. No mutating operations
  • 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)
  • +3Description length 37: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -215 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 32 items
  • +4Has examples (11 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.