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BC methods-reporting

Audits a methods section against a 45-item checklist synthesized from the APSA Experimental Section rubric, JARS-Quant, CONSORT, and DA-RT — pre-registration and design documentation, subjects and recruitment, randomization and treatment detail, CONSORT-style sample flow with attrition, statistical analysis with sample-size justification and three-tier results labeling, conjoint-specific reporting, the four validity types, and open-science infrastructure. Use when the user asks whether a methods section reports enough, is preparing for submission or a replication archive, asks what CONSORT, JARS, or DA-RT require, or asks how to document a deviation from the pre-analysis plan. Writing that plan beforehand goes to pre-registration-writing.

scdenney/open-science-skills Agent Skills author: scdenney NOASSERTION 5 files body ≈ 4 672 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Audits a methods section against a 45-item checklist synthesized from the APSA Experimental Section rubric, JARS-Quant, CONSORT, and DA-RT — pre-registration…

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerData and analyticstype 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
55/100
Has gaps
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

    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: 5. 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 55/100

    • 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
    • 30Running it twice. 10 mutating operations with no state check
    • 70When it triggers. States when to use, but not when not to
    • 70Execution cost. Instruction body is 4672 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 49 steps
    • 100Consistency. Name and required fields are in place

    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 748: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 49 items
    • +4Reference files are cited in the instructions (3 of 3)

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