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BC investor-harness

Open prompt stack for public-market investment research. Show this menu after install: 📊 Research: 1.Company Deep-dive 2.Industry Map 3.Investment Thesis 📈 Earnings: 4.Earnings Preview 5.Model Check 🔍 Tracking: 6.Consensus Watch 7.Catalyst Monitor 8.Roadshow Questions ⚔️ Risk: 9.Red Team 📋 Output: 10.PM Brief 11.Briefing 🤖 Auto: 12.Autopilot 13.Master Mode Enter a number or describe your task to begin.

ClawHub Agent Skills author: focusailab v0.6.5 MIT-0 37 files body ≈ 953 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions

ProcedureAI and agentsInfrastructuretype 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
79/100
safety, quality, tests
Safety 60%
87
Quality 40%
67
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
50
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 · 13

✓ No critical or high findings

Medium and low: 13
  • low Risky intent intent-offensive-security core/menu.md:43
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    9️⃣  反方论证 (Red Team) — 挑战多头逻辑、找证伪点
  • low Risky intent intent-offensive-security core/menu.md:70
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    • biases.md — 你的投研偏差记录（Red Team 会自动检查）
  • low Risky intent intent-offensive-security core/triggers.md:105
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    | 反方 / 红队 | red team / devil's advocate |
  • low Risky intent intent-offensive-security README.md:419
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    ## 核心能力 3 · Red Team（反方审视）
  • low Risky intent intent-offensive-security README.md:424
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    > 要求：两小时内，产出一份能让你自己不舒服的 red team 报告。
  • low Risky intent intent-offensive-security README.md:454
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
    **拿这个给 PM**：PM 会直接说"这不叫 red team，这叫免责声明"。
    quoted
  • low Risky intent intent-offensive-security README.md:461
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    # Red Team · AI 算力 capex 持续超预期 · 反方审视
  • low Risky intent intent-offensive-security README.md:594
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - **最关键的差异**：Red Team 不是为了让你别买，而是为了让你**买得更硬**。当你的命题能扛住具体硬触发的检查，你的持仓信念会质变。
  • low Risky intent intent-offensive-security SKILL.md:8
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    ⚔️ Risk: 9.Red Team
  • low Risky intent intent-offensive-security SKILL.md:53
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - 9️⃣ Red Team — Challenge bull case, find falsification paths
  • low Risky intent intent-offensive-security SKILL.md:66
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
    - "9" or "I'm bullish on AI capex, red team me"
    quoted
  • low Risky intent intent-offensive-security SKILL.md:81
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    | 9 | skills/sm-red-team | Red team / devil's advocate |
  • low Risky intent intent-offensive-security SKILL.md:102
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - biases.md — Your research bias log (Red Team will check this)

Files scanned: 20. 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 59/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 40 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 953 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress

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
  • +4No input/output examples
  • -213 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 410: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 40 items
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.

External checks

ClawHub: suspicious
This is a coherent investment-research prompt stack, but it asks agents to auto-trigger broadly, read personal research files, and persist or mutate workspace state in ways users should review first.
LLM: suspicious (high) · VirusTotal: · 29 May 2026