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BC kungfu_finance

Mainland China A-share stock and sector analysis tool (中国A股个股与板块分析). Current repo build focuses on stable deterministic products for stock snapshots, basic finance indicators, chip/price levels, sector detail lookup, plus bucket, phase-2 strategy, researcher, bayesian monitor, and preview stock/sector deep research flows.

ClawHub Agent Skills author: jickchen34 v0.4.1 MIT-0 80 files body ≈ 6 148 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 58/100 · Has gaps — weak spots: result and completion, consistency, running it twice

AnalyzerGitHubData 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
81/100
safety, quality, tests
Safety 60%
97
Quality 40%
57
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
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.
  2. 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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Risky intent intent-offensive-security references/research-flows/sector-analysis/modules/step-4-thesis.md:50
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    ### 4.8 反面审查 (Red Team Review) ⭐⭐ 独立步骤 — 防止确认偏误的最后防线
  • low Risky intent intent-offensive-security references/research-flows/sector-analysis/modules/step-4-thesis.md:59
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    ### 反面审查 (Red Team Review)
  • low Risky intent intent-offensive-security references/research-flows/stock-analysis/modules/step-5-thesis.md:121
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    ## 5.3 反面审查 (Red Team Review)

Files scanned: 80. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 6148 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 58/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 12 mutating operations with no state check
  • 40Consistency. Frontmatter name (kungfu_finance) differs from the folder (kungfu-finance)
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 6148 tokens
  • 100Steps. 174 steps
  • 100Failures and branches. 10 branches, has a failure section
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • 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 323: enough signal without eating the budget
  • +4Structure: 63 headings
  • +3Step-by-step instructions: 174 items
  • +4Has examples (31 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 57.

External checks

ClawHub: suspicious
This finance skill is mostly coherent, but it deserves review because it can change authenticated account data, store an API key locally, use a configurable external search endpoint, and ask the agent to apply updates via shell command.
LLM: suspicious (high) · VirusTotal: · 29 May 2026