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AB litreview

Searches academic literature through free, keyless PubMed and OpenAlex APIs, optionally augments results with Consensus MCP when connected, builds a PICO, SPIDER, decomposition, or hybrid search plan, and produces a formatted Word research-orientation guide. Uses brief intake and post-reconnaissance checkpoints to confirm the framework, sub-areas, and depth (5, 10, or 20 queries) before spending the search budget. The output is a launching pad, not a finished review. Use when the user requests a literature review, starts a paper or literature-oriented research project, or needs structured research on a topic. Do NOT use for a single paper lookup or a quick paper list; use a plain PubMed, OpenAlex, or Consensus query instead.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI 8 files · 4 scripts body ≈ 4 262 tokens Open the sourcegithub.com↗ analyzed 9 d ago

Searches academic literature through free, keyless PubMed and OpenAlex APIs, optionally augments results with Consensus MCP when connected, builds a PICO…

As a process B 72/100 · Nearly there — weak spots: inputs and preconditions, running it twice

AnalyzerWordResearchAI 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
A
100/100
safety, quality, tests
Safety 60%
100
Quality 40%
99
Run on models
none yet
Process rating
B
72/100
Nearly there
Inputs and preconditions w 11
0
Running it twice w 4
30
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

    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: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 72/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 4 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Execution cost. Instruction body is 4262 tokens
    • 100Steps. 82 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Progress reporting. Reports progress
    • low 14 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (4 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
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 734: enough signal without eating the budget
    • +4Structure: 28 headings
    • +3Step-by-step instructions: 82 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 4 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 99.