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BF Privora · 数据驱动投资工作流平台 for AI Agents

Privora · AI Agent 投资工作流平台 — Bearer Token 即接入 /agent/skills/execute 通用 dispatcher，覆盖 A 股/港股/美股/黄金/基金/财报数据 + Python 回测（含 sandbox）+ 模拟交易 + 组合归因（α/β TWR）+ 云端告警 + 流程编排。Hermes / Claude / GPT / OpenClaw 全兼容。

ClawHub Agent Skills author: GuangfuWu v1.0.51 MIT-0 4 files · 2 scripts body ≈ 33 332 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 49/100 · Will not run — References files that are not bundled: lg-data-demo.gif, scripts/lg_agent_approval.sh

ProcedureAI 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
B
77/100
safety, quality, tests
Safety 60%
97
Quality 40%
47
Run on models
none yet
Process rating
F
49/100
Will not run
References files that are not bundled: lg-data-demo.gif, scripts/lg_agent_approval.sh
Tools and files w 18
0
Result and completion w 14
0
Execution cost w 6
10
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.
  3. 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

✓ No critical or high findings

Medium and low: 3
  • low Exfiltration net-credential-use SKILL.md:240
    Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
    curl -H "Authorization: Bearer $LG_AGENT_TOKEN" \
    security skill
  • low Exfiltration net-credential-use SKILL.md:248
    Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
    curl -H "Authorization: Bearer $LG_AGENT_TOKEN" \
    security skill
  • low Exfiltration net-credential-use SKILL.md:255
    Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
    curl -H "Authorization: Bearer $LG_AGENT_TOKEN" \
    security skill

Files scanned: 4. 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 ≈ 33332 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: lg-data-demo.gif
  • warning missing-ref reference to a missing file: scripts/lg_agent_approval.sh
  • note frontmatter-key unknown frontmatter key "title"
  • note frontmatter-key unknown frontmatter key "updatedAt"
  • note frontmatter-key unknown frontmatter key "keywords"

Process rating: all ten parameters 49/100

Will not run. References files that are not bundled: lg-data-demo.gif, scripts/lg_agent_approval.sh
  • 0Tools and files. 2 referenced file(s) missing: lg-data-demo.gif, scripts/lg_agent_approval.sh
  • 0Result and completion. Does not say what the result is
  • 10Execution cost. Instruction body is 33332 tokens: crowds the task out of the window
  • 40Consistency. Frontmatter name (Privora · 数据驱动投资工作流平台 for AI Agents) differs from the folder (privora-cn-quant)
  • 50When it triggers. No condition that starts the skill
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 219 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 15 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (12 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
  • -2264 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 201: enough signal without eating the budget
  • +4Structure: 108 headings
  • +3Step-by-step instructions: 219 items
  • +4Has examples (51 code blocks)
  • +3All 2 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 47.

External checks

ClawHub: clean
The skill is a broad but clearly documented Privora API wrapper for investment data workflows, with sensitive token-scoped capabilities disclosed and bounded by platform scopes/confirmations.
LLM: benign (high) · VirusTotal: · 31 Aug 2026