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AD npc-distill

构建特定人物(上级/合作伙伴/客户)的本地数字分身,辅助汇报演练、风格化起草、决策预判。当用户粘贴某人物的发言、邮件、聊天记录、会议纪要、批注,或提供包含其内容的网页URL,或说出"投喂语料""练习汇报""模拟 NPC 反应""按X总风格起草""预演一下""数字分身""人格画像""压力测试我的方案"等意图时,自动启用本Skill。所有数据存储在本地单一memory文件中,人物使用代号(L001/L002)而非真名,适合开源场景与高隐私要求。兼容Claude Skills/Hermes Agent/LobsterAI/OpenClaw等所有遵循 agentskills.io 标准的Agent。

ClawHub Agent Skills author: bangbangmao v1.0.0 MIT-0 15 files body ≈ 561 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

构建特定人物(上级/合作伙伴/客户)的本地数字分身,辅助汇报演练、风格化起草、决策预判。当用户粘贴某人物的发言、邮件、聊天记录、会议纪要、批注,或提供包含其内容的网页URL,或说出"投喂语料""练习汇报""模拟 NPC…

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

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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    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 · 0

    ✓ No critical or high findings

    Files scanned: 15. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"
    • note frontmatter-key unknown frontmatter key "platform"

    Process rating: all ten parameters 49/100

    • 0Result and completion. Does not say what the result is
    • 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 (npc-distill) differs from the folder (npc-distill-main)
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 11 steps
    • 100Execution cost. Instruction body is 561 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
    • -42 reference files, but SKILL.md never points to them: the model will not open them
    • -32 of 3 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +5Description quotes 3 example trigger phrases
    • +3Description length 299: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 11 items
    • +4Has examples (3 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

    External checks

    ClawHub: suspicious
    This skill appears purpose-built rather than malicious, but it creates durable local profiles from sensitive workplace communications and has retention/scoping gaps users should review carefully.
    LLM: suspicious (medium) · 17 Jun 2026