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BC deliverable-lint

Reviews a whole deliverable, such as a talk, course module, paper, chapter, or referee report, against its manifest without rewriting. Runs a deterministic gate for build, citations, numbers, facts, and leaks, then stops on failure. It assigns a cheap finder to each section, runs a strong-model whole-work pass, and adjudicates into checks/<date>/findings.json and report.md with anchors, severities, and honest NOT-CHECKED coverage within a dollar budget. Use when the user says "lint the deliverable", "review the whole talk/module/paper", "night shift", "pre-ship review", or before release. Revision remains serial and author-led.

scdenney/open-science-skills Claude Code author: scdenney NOASSERTION 2 files body ≈ 2 113 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Reviews a whole deliverable, such as a talk, course module, paper, chapter, or referee report, against its manifest without rewriting.

As a process C 58/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, running it twice

AnalyzerData and analyticsSoftware developmenttype 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%
85
Run on models
none yet
Process rating
C
58/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: open-science-skills

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

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 8): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 58/100

    • 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. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 7 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2113 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low The response is described with custom markup (15 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

    • +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
    • +5Description quotes 4 example trigger phrases
    • +3Description length 635: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 7 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.