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BC fetch-pubmed-recent

Fetches PubMed articles posted in a given date window matching a keyword set, returning normalized records (PMID, title, authors, abstract, journal, date, DOI, URL). Uses NCBI E-utilities (`esearch` + `esummary` + `efetch`) via WebFetch for portability, but prefers a PubMed MCP server's `search_articles` + `get_article_metadata` tools when one is connected (faster, paginated, parsed). Builds date-bounded queries with optional MeSH expansion. Domain-neutral - usable for any PubMed scan, not just one project. Use when user mentions PubMed, NCBI, last-N-days PubMed, weekly PubMed scan, MeSH search, or when a literature-scan agent needs structured PubMed records.

lyndonkl/claude Agent Skills author: lyndonkl 1 file body ≈ 2 053 tokens Open the sourcegithub.com↗ analyzed 16 h ago

Fetches PubMed articles posted in a given date window matching a keyword set, returning normalized records (PMID, title, authors, abstract, journal, date…

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentsWriting and documentsPersonal productivitytype 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
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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: 1. 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 54/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 85Steps. 21 steps, 1 vague phrases
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2053 tokens
    • 100Running it twice. Mutating operations check current state
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 667: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 21 items
    • +4Has examples (5 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.