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CD marketsensor

AI 驱动的股票多因子分析——查自选、看报告、触发分析、查额度。支持美股、加密货币、A 股。

ClawHub Agent Skills author: xmhu v1.0.0 MIT-0 13 files · 2 scripts body ≈ 1 284 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype 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
67/100
safety, quality, tests
Safety 60%
70
Quality 40%
62
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

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. 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 · 10

✓ No critical or high findings

Medium and low: 10
  • medium Exfiltration net-credential-use SKILL.md:53
    Credential used in a network call (verify the destination is the intended service)
    curl -s -H "Authorization: Bearer $MARKETSENSOR_API_KEY" \
  • medium Exfiltration net-credential-use SKILL.md:71
    Credential used in a network call (verify the destination is the intended service)
    curl -s -X POST -H "Authorization: Bearer $MARKETSENSOR_API_KEY" \
  • medium Exfiltration net-credential-use SKILL.md:90
    Credential used in a network call (verify the destination is the intended service)
    curl -s -X POST -H "Authorization: Bearer $MARKETSENSOR_API_KEY" \
  • medium Exfiltration net-credential-use SKILL.md:101
    Credential used in a network call (verify the destination is the intended service)
    curl -s -H "Authorization: Bearer $MARKETSENSOR_API_KEY" \
  • medium Exfiltration net-credential-use SKILL.md:119
    Credential used in a network call (verify the destination is the intended service)
    curl -s -X POST -H "Authorization: Bearer $MARKETSENSOR_API_KEY" \
  • low Exfiltration net-credential-use references/examples.md:9
    Credential used in a network call (verify the destination is the intended service) (test fixture / example file)
    curl -s -H "Authorization: Bearer $MARKETSENSOR_API_KEY" \
    fixture
  • low Exfiltration net-credential-use references/examples.md:15
    Credential used in a network call (verify the destination is the intended service) (test fixture / example file)
    curl -s -X POST -H "Authorization: Bearer $MARKETSENSOR_API_KEY" \
    fixture
  • low Exfiltration net-credential-use references/examples.md:24
    Credential used in a network call (verify the destination is the intended service) (test fixture / example file)
    curl -s -H "Authorization: Bearer $MARKETSENSOR_API_KEY" \
    fixture
  • low Exfiltration net-credential-use references/examples.md:30
    Credential used in a network call (verify the destination is the intended service) (test fixture / example file)
    curl -s -H "Authorization: Bearer $MARKETSENSOR_API_KEY" \
    fixture
  • low Exfiltration net-credential-use references/examples.md:38
    Credential used in a network call (verify the destination is the intended service) (test fixture / example file)
    curl -s -H "Authorization: Bearer $MARKETSENSOR_API_KEY" \
    fixture

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

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 49/100

  • 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
  • 40Consistency. Frontmatter name (marketsensor) differs from the folder (market-sensor)
  • 100Tools and files. No external tools needed
  • 100Steps. 11 steps
  • 100Execution cost. Instruction body is 1284 tokens
  • 100Running it twice. No mutating operations

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 46: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -43 reference files, but SKILL.md never points to them: the model will not open them
  • -32 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 11 items
  • +4Has examples (12 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: clean
MarketSensor is a disclosed finance API helper that uses a user-provided API key to read reports, manage a watchlist, trigger analysis, and check quota.
LLM: benign (high) · VirusTotal: · 29 May 2026