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CD research-lit

Search and analyze research papers, find related work, summarize key ideas. Use when user says "find papers", "related work", "literature review", "what does this paper say", or needs to understand academic papers.

wanshuiyin/Auto-claude-code-research-in-sleep Agent Skills author: wanshuiyin MIT 1 file body ≈ 7 383 tokens Open the sourcegithub.com↗ analyzed 5 h ago

Search and analyze research papers, find related work, summarize key ideas.

As a process D 49/100 · Unfinished process — References files that are not bundled: ../shared-references/fan-out-pattern.md, ../shared-references/integration-contract.md, ../shared-references/output-composition.md

AnalyzerPDFObsidianResearchtype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
Run on models
none yet
Process rating
D
49/100
Unfinished process
References files that are not bundled: ../shared-references/fan-out-pattern.md, ../shared-references/integration-contract.md, ../shared-references/output-composition.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

The same skill appears in 1 more place: Auto-claude-code-research-in-sleep

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  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 · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 7383 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: ../shared-references/fan-out-pattern.md
  • warning missing-ref reference to a missing file: ../shared-references/integration-contract.md
  • warning missing-ref reference to a missing file: ../shared-references/output-composition.md

Process rating: all ten parameters 49/100

Will not run. References files that are not bundled: ../shared-references/fan-out-pattern.md, ../shared-references/integration-contract.md, ../shared-references/output-composition.md
  • 0Tools and files. 3 referenced file(s) missing: ../shared-references/fan-out-pattern.md, ../shared-references/integration-contract.md, ../shared-references/output-composition.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 50When it triggers. No condition that starts the skill
  • 70Execution cost. Instruction body is 7383 tokens
  • 100Steps. 86 steps
  • 100Failures and branches. 8 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress

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
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 214: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 86 items
  • +4Has examples (10 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.