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BF Signal-Search

答案质量层搜索/增强检索 skill：搜信息、做调研、对比、查资料、信源比对、事实核查时启用。不做链接列表——返回带加权打分(SBA)、事实级来源锚定(M51)、分层深度(L0–L3)与 token 预算封顶的干净答案；无广告、零 key、可嵌入，被其它 skill 当检索原语调用（由调用方注入 LLM 与抓取能力）。

ClawHub Agent Skills author: Sabre v0.1.1 MIT-0 80 files body ≈ 1 854 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

答案质量层搜索/增强检索 skill：搜信息、做调研、对比、查资料、信源比对、事实核查时启用。不做链接列表——返回带加权打分(SBA)、事实级来源锚定(M51)、分层深度(L0–L3)与 token 预算封顶的干净答案；无广告、零 key、可嵌入，被其它 skill 当检索原语调用（由调用方注入 LLM 与抓取能力）。

As a process F 33/100 · Will not run — References files that are not bundled: examples/agent_dispatch.py, references/tier-policy.md, references/intent-decomposition.md

ProcedureAI and agentsCustomer supporttype 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%
97
Quality 40%
53
Run on models
none yet
Process rating
F
33/100
Will not run
References files that are not bundled: examples/agent_dispatch.py, references/tier-policy.md, references/intent-decomposition.md
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Secrets in code secret-high-entropy-token config.json:602
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "model": "para…-v2"
    quoted
  • low Secrets in code secret-high-entropy-token scripts/embed.py:6
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    模型默认 para…-v2（中文友好，多语语义）；可在 config.embed.model
    quoted
  • low Secrets in code secret-high-entropy-token scripts/embed.py:21
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    _DEFAULT_MODEL = "para…-v2"
    quoted

Files scanned: 39. 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 missing-ref reference to a missing file: examples/agent_dispatch.py
  • warning missing-ref reference to a missing file: references/tier-policy.md
  • warning missing-ref reference to a missing file: references/intent-decomposition.md
  • warning missing-ref reference to a missing file: references/token-optimization.md
  • warning missing-ref reference to a missing file: examples/conflict_llm.py
  • warning missing-ref reference to a missing file: references/*.md
  • warning missing-ref reference to a missing file: scripts/*.py

Process rating: all ten parameters 33/100

Will not run. References files that are not bundled: examples/agent_dispatch.py, references/tier-policy.md, references/intent-decomposition.md
  • 0Tools and files. 7 referenced file(s) missing: examples/agent_dispatch.py, references/tier-policy.md, references/intent-decomposition.md
  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 100Steps. 40 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1854 tokens
  • low 14 top-level sections: this looks like several domains in one skill
  • low The skill ranks results itself: that belongs to the system behind the tool, not the model

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
  • -327 of 30 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 160: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 40 items
  • +4Reference files are cited in the instructions (1 of 3)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 53.

External checks

ClawHub: suspicious
This appears to be a real search and research skill, but it includes anti-bot evasion, automatic environment-token use, and local caching behavior that should be reviewed before installation.
LLM: suspicious (high) · 21 Aug 2026