BC replication-package
Scaffold or audit a social-science replication package, and audit the manuscript and its archived research objects against the FAIR principles. Scaffold mode writes the folder structure, README, master.R, figure/table crosswalk, codebook template, LICENSE placeholder, .gitignore, and pre-release checklist. Audit mode grades an existing package against that checklist and runs the FAIR block over data, code, materials, prompts, preregistrations, DOIs, metadata, licenses, access restrictions, and availability statements. Use when setting up or repairing a replication package, checking one before submission, auditing research objects against FAIR (Findable, Accessible, Interoperable, Reusable), or drafting and verifying data-, code-, and materials-availability statements. Adapted from Yusaku Horiuchi's replication-package-guide; platform-neutral (Harvard Dataverse, OSF, Zenodo, GitHub releases, institutional archives).
Scaffold or audit a social-science replication package, and audit the manuscript and its archived research objects against the FAIR principles.
As a process C 57/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5860 tokens (recommended < 5000); move details to references/ - note
edit-residuethe text marks something as outdated (lines 472): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 57/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 11 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Execution cost. Instruction body is 5860 tokens
- 85Steps. 77 steps, 2 vague phrases
- 100Failures and branches. 3 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 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
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 928: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 31 headings
- +3Step-by-step instructions: 77 items
- +4Has examples (10 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.