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AC hua-personal-strategy

以 HuahuaDaily 真实基金持仓和 quant_strategy_context.v2 为数据底座，为每个用户先建立版本化投资目标、风险、现金流和资产限制，再联合东方财富研究与 serenity-skill 形成有证据和反证的 AI 投资观点，最后由确定性资金内核输出单一、果断、可审计的场外基金持有、加仓、减仓、再平衡或现金等待建议。用户提出“接管我的基金仓位”“分析我的持仓”“今天买卖多少”“基金组合复盘”“动态调仓”“策略自进化”“回测或复盘策略”等请求时使用。不得用于股票/场内 ETF 自动交易、缺少授权持仓的公共荐基、承诺准确无误或绕过用户对真实交易的最终决定。

ClawHub Agent Skills author: baiye v4.3.3 MIT-0 28 files body ≈ 1 845 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

以 HuahuaDaily 真实基金持仓和 quantstrategycontext.v2 为数据底座，为每个用户先建立版本化投资目标、风险、现金流和资产限制，再联合东方财富研究与 serenity-skill 形成有证据和反证的 AI…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

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
A
91/100
safety, quality, tests
Safety 60%
99
Quality 40%
78
Run on models
none yet
Process rating
C
53/100
Has gaps
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

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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Obfuscation obf-base64-blob references/input-schema.md:29
    Long base64-looking blob (quoted — discussed, not commanded)
    - `holdings[].met…Pct/r60Pct/r120Pct/r250Pct/ma20/ma60/bias20Pct/maxDrawdownPct/annualizedVolatilityPct/navPoints` → `funds[].metric_overrides`
    quoted

Files scanned: 28. 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 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 61 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1845 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -33 of 8 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 292: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 61 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (5 of 13)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

External checks

ClawHub: suspicious
This is a coherent mutual-fund advisory skill, but it needs review because it handles sensitive portfolio data, persists local state, and has unclear archival/permission boundaries.
LLM: suspicious (medium) · 24 Jul 2026