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CF finance-news-analyzer

财经新闻深度分析技能。从多个新闻源抓取内容，进行情感分析（利好/利空/中性）、影响评估（行业/公司/市场）、关键信息提取，生成专业投资简报。支持 A 股/港股/美股、行业板块、个股分析。Use when user asks to "分析财经新闻", "评估新闻影响", "今日财经简报", "这条新闻是利好还是利空", "总结本周行业动态", or provides financial news URLs for analysis.

Not recommendedcritical or high security findings
ClawHub Agent Skills author: Criss_Su v1.0.0 MIT-0 25 files body ≈ 2 083 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 35/100 · Will not run — References files that are not bundled: references/impact-patterns.md

AnalyzerAI and agentstype 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
C
71/100
safety, quality, tests
Safety 60%
59
Quality 40%
88
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: references/impact-patterns.md
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

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. 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 · 3

  • high Dangerous commands cmd-pipe-to-shell QUICKSTART.md:161
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    curl -fsSL https://ollama.com/install.sh | sh
  • high Dangerous commands cmd-pipe-to-shell references/supported-models.md:153
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    curl -fsSL https://ollama.com/install.sh | sh
Medium and low: 1
  • medium Exfiltration net-redirectable-api-key scripts/analyze_sentiment.py:112
    Helper sends the API key to a host configured by an environment variable — the key can be redirected to another server
    API key + configurable base URL from environment

Files scanned: 25. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/impact-patterns.md

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: references/impact-patterns.md
  • 0Tools and files. 1 referenced file(s) missing: references/impact-patterns.md
  • 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. 47 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2083 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -230 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 217: enough signal without eating the budget
  • +4Structure: 49 headings
  • +3Step-by-step instructions: 47 items
  • +4Has examples (22 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)
  • +3All 5 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

External checks

ClawHub: suspicious
The skill is not clearly malicious, but its main finance workflow can produce authoritative-looking reports from hardcoded articles and random sentiment labels.
LLM: suspicious (high) · VirusTotal: · 29 May 2026