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AF equity-analyst

전문 투자 분석가 AI로, 한국 주식 종목의 재무제표, 뉴스, 차트를 분석하여 Investment Attractiveness Score (0-100)와 BUY/HOLD/AVOID 추천을 제공합니다. 네이버 금융 데이터를 사용하며, 프롬프트에 명시된 엄격한 우선순위(Financial > News > Chart)와 가중치를 따릅니다. trigger: 사용자가 "xxx 주식 분석해줘", "xxx 투자 매력도 알려줘", "xxx 리포트 써줘" 등으로 분석 요청을 했을 때.

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files body ≈ 1 709 tokens Open the sourcegithub.com↗ analyzed 2 d ago

전문 투자 분석가 AI로, 한국 주식 종목의 재무제표, 뉴스, 차트를 분석하여 Investment Attractiveness Score (0-100)와 BUY/HOLD/AVOID 추천을 제공합니다.

As a process F 45/100 · Will not run — References files that are not bundled: scripts/scrape_naver.py

AnalyzerOutlookWriting and documentsData 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
99/100
safety, quality, tests
Safety 60%
100
Quality 40%
97
Run on models
none yet
Process rating
F
45/100
Will not run
References files that are not bundled: scripts/scrape_naver.py
Tools and files w 18
0
Inputs and preconditions w 11
0
Progress reporting w 2
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/scrape_naver.py

Process rating: all ten parameters 45/100

Will not run. References files that are not bundled: scripts/scrape_naver.py
  • 0Tools and files. 1 referenced file(s) missing: scripts/scrape_naver.py
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 40Consistency. Frontmatter name (equity-analyst) differs from the folder (equity-analyst-test)
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 85Steps. 62 steps, 1 vague phrases
  • 100Execution cost. Instruction body is 1709 tokens
  • 100Running it twice. No mutating operations
  • low 10 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

  • +1No license
  • +2Single-language instructions
  • +5Description quotes 3 example trigger phrases
  • +4Description says when NOT to use the skill
  • +3Description length 262: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 62 items
  • +3Output format is stated explicitly
  • +4Has examples (3 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 97.