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BD 科研技能鉴赏

科研技能总入口与路由——不知道用哪个科研技能时先来找它。精选 13 个科研技能（含本技能），覆盖文献检索与下载、个人知识库、CAJ 转 PDF、引用核查、学术诚信法医、组会把关、新颖性校验、组会领航、答辩演练、科研方法论谋士、技能锻造与 AI 易读化。每个技能都标注了「适用边界 / 能做什么 / 不能做什么 / 该不该装」，帮 AI 和用户一眼判断装哪个。触发词：科研技能、找论文、查文献、下载PDF、引用核查、文献真假、撤稿、论文工厂、知识库、组会、答辩、新颖性、研究方法论、卡点、造技能、技能易读。

ClawHub Agent Skills author: J-levee v1.2.0 MIT-0 2 files body ≈ 2 220 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

科研技能总入口与路由——不知道用哪个科研技能时先来找它。精选 13 个科研技能（含本技能），覆盖文献检索与下载、个人知识库、CAJ 转 PDF、引用核查、学术诚信法医、组会把关、新颖性校验、组会领航、答辩演练、科研方法论谋士、技能锻造与 AI 易读化。每个技能都标注了「适用边界 / 能做什么 / 不能做什么 /…

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

ProcedureResearchAI 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
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
60
Run on models
none yet
Process rating
D
49/100
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: 2. 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")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 49/100

  • 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
  • 40Consistency. Frontmatter name (科研技能鉴赏) differs from the folder (keji-skill-showcase)
  • 100Tools and files. No external tools needed
  • 100Steps. 146 steps
  • 100Execution cost. Instruction body is 2220 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

  • +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
  • +1No license
  • +2Single-language instructions
  • +3Description length 252: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 146 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.

External checks

ClawHub: clean
This is a lightweight research-skill directory that routes users to other skills, with broad triggers that may be noisy but no evidence of hidden execution, data access, or persistence.
LLM: benign (high) · VirusTotal: · 31 Jul 2026