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BD agentic-ai-research

检索顶会顶刊的 Agentic AI 最新研究成果，产出一份给人看的文献综述。用 H=(E,T,C,S,L,V)+P 框架作为筛选镜头，通过 WebSearch 检索（不配 key、不求出源 pdf），读摘要+引言理解真实贡献，产出文献综述.md，可选经确认后接入内置 wiki-creator 组件完成 wiki 化。触发场景：用户提出「检索顶会论文」「agentic ai 研究综述」「@agentic-ai-research」或想了解某个 agent 子领域的最新顶会顶刊成果。

ClawHub Agent Skills author: hanli v1.1.1 MIT-0 14 files body ≈ 1 258 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

检索顶会顶刊的 Agentic AI 最新研究成果，产出一份给人看的文献综述。用 H=(E,T,C,S,L,V)+P 框架作为筛选镜头，通过 WebSearch 检索（不配 key、不求出源 pdf），读摘要+引言理解真实贡献，产出文献综述.md，可选经确认后接入内置 wiki-creator 组件完成 wiki…

As a process D 46/100 · Unfinished process — 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
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
D
46/100
Unfinished process
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 · 0

✓ No critical or high findings

Files scanned: 14. 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")
  • note frontmatter-key unknown frontmatter key "display_name"
  • note frontmatter-key unknown frontmatter key "display_name_en"
  • note frontmatter-key unknown frontmatter key "description_en"

Process rating: all ten parameters 46/100

  • 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
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 100Steps. 44 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1258 tokens
  • 100Running it twice. No mutating operations
  • low 10 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (7 tags): a typed call is more reliable

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
  • -32 of 6 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 242: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 44 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: suspicious
The skill is mostly coherent and disclosed, but its local wiki index script can be driven by page metadata to write Markdown files outside the intended wiki topics directory.
LLM: suspicious (high) · 9 Sept 2026