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BF creator-intel-v5

创造者情报 V5 - 工程师视角技术情报聚合器 严禁 VC 商业化话术，只关注底层技术实现。 服务对象：硬科技工程师、极客产品经理、技术研究者。 核心选品标准（按优先级）： 1. 🥇 GitHub 开源项目与霸榜（新模型、新硬件图纸、开发工具库） 2. 🥈 硬核技术原理解析（MoE、稀疏注意力、流匹配、新架构） 3. 🥉 极客硬件与创新交互（ESP32、树莓派、Kickstarter 创意硬件） 摘要必须包含： - 至少 2 个技术名词或具体参数 - 架构/算法/材料/性能指标 - 严禁："拓展商业化"、"规模化部署"、"生态布局" 等公关套话 信源分布： - 国际：GitHub、IEEE、arXiv、Kickstarter、Hackaday（Tavily API 搜索） - 国内：机器之心、量子位、开源中国 RSS 使用场景： - "生成今日技术情报" - "有什么硬核技术新闻" - "GitHub 上有啥新东西" 输出格式： ``` [YYYY-MM-DD] 创造者情报 🌍 📦 [技术实体 + 动作 + 性能参数](URL) 摘要：架构细节；关键性能指标；应用场景 ⚛️ [技术实体 + 动作 + 性能参数](URL) 摘要：... ```

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 446 tokens Open the sourcegithub.com↗ analyzed 2 d ago

创造者情报 V5 - 工程师视角技术情报聚合器 严禁 VC 商业化话术，只关注底层技术实现。 服务对象：硬科技工程师、极客产品经理、技术研究者。 核心选品标准（按优先级）： 1.

As a process F 35/100 · Will not run — References files that are not bundled: scripts/generate_intel.py

IntegrationGitHubResearchSoftware developmenttype 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
86/100
safety, quality, tests
Safety 60%
99
Quality 40%
66
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: scripts/generate_intel.py
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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.
  2. 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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token scripts/generate_brief.py:17
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    TAVILY_API_KEY = "tvly…1DJ"
    quoted

Files scanned: 2. 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")
  • warning missing-ref reference to a missing file: scripts/generate_intel.py

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: scripts/generate_intel.py
  • 0Tools and files. 1 referenced file(s) missing: scripts/generate_intel.py
  • 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
  • 100Steps. 31 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 446 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
  • -218 emoji in the instructions: noise for the model
  • -31 of 1 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 3 example trigger phrases
  • +3Description length 542: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 31 items
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.