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BF financial-event-impact-analyzer

Analyze historical impact of financial events on related assets. Kensho-style event-driven analysis. Use when: asking about asset reactions to events (oil surge, gold rise, rate hikes), historical precedent analysis, causal indicator relationships. Outputs Chinese reports and charts.

ClawHub Agent Skills author: 赖根 v1.0.5 MIT-0 13 files body ≈ 3 171 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process F 35/100 · Will not run — References files that are not bundled: charts/图表文件.png

AnalyzerData and analyticsInfrastructuretype 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
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
75
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: charts/图表文件.png
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Dangerous commands cmd-shell-rc SKILL.md:42
    Writes to a shell startup file (documentation table row)
    | **TUSHARE_TOKEN** | `echo $TUSHARE_TOKEN` | 在 ~/.bashrc 中设置 |
    table

Files scanned: 13. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: charts/图表文件.png
  • note frontmatter-key unknown frontmatter key "dependencies"
  • note frontmatter-key unknown frontmatter key "env"

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: charts/图表文件.png
  • 0Tools and files. 1 referenced file(s) missing: charts/图表文件.png
  • 0Result and completion. Does not say what the result is
  • 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
  • 20When it triggers. No condition that starts the skill
  • 100Steps. 57 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3171 tokens
  • 100Running it twice. No mutating operations
  • low The response is described with custom markup (3 tags): a typed call is more reliable

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
  • -239 emoji in the instructions: noise for the model
  • -32 of 8 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 284: enough signal without eating the budget
  • +4Structure: 33 headings
  • +3Step-by-step instructions: 57 items
  • +4Has examples (16 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: clean
This is a disclosed financial analysis skill that fetches market data and writes local reports, with notable quality and setup cautions but no evidence of hidden, destructive, or exfiltrating behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026