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ariaxhanariaxhan/kernel-claude/skills/quality/SKILL.md

quality

Skill15 starsChanged 4 months agoMITView on GitHub

AI code quality checks. The Big 5: input validation, edge cases, error handling, duplication, complexity. Triggers: quality, big 5, ai code, review, validate.

12 other projects use a version of this.

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Used with

Projects with it also use tasks, analyze and 2 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • genomewalkerSkill

    tasks

    Browse and resume tasks, threads, and background jobs across sessions

    Also in 3 projects that use this

  • AnthropicSkill

    analyze

    Answer data questions -- from quick lookups to full analyses. Use when looking up a single metric, investigating what's driving a trend or drop, comparing segments over time, or preparing a formal data report for stakeholders.

    Also in 3 projects that use this

  • MicrosoftSkill

    report

    Turn an exploration (threads, findings, charts) into a single Markdown report — note, blog post, executive summary, KPI dashboard, slide brief, or multi-section analytical report, with embedded charts.

    Also in 3 projects that use this

  • BlockSkill

    plan

    Draft a TDD, PRD, or lightweight plan in the conversation without committing it.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 10 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Every endpoint has Zod/Pydantic schema.

    line 25
  2. Very specific:

    No empty catch. Errors logged with context.

    line 33

“Common” is measured: kept by at least 6 in 10 of the 10 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md
<skill id="quality"><purpose> AI code is 1.7x buggier. These 5 checks catch 80% of issues. Load this skill for any review, validation, or implementation work. </purpose><reference> Verbose research: skills/quality/reference/quality-research.md </reference><big5> <check id="1" name="input_validation" detection="grep -r 'req\.body' | grep -v 'parse\|validate\|z\.'"> Every endpoint has Zod/Pydantic schema. Parameterized queries only. </check><check id="2" name="edge_cases" detection="search for array access without length check"> Handle: null, empty array, zero-length string, timeout, unicode. </check><check id="3" name="error_handling" detection="grep -r 'catch.*{}'"> No empty catch. Errors logged with context. User messages generic. Silent swallowing is the worst variant: a catch/onError that returns a masked or generic body without first logging method/path/cause hides the root failure behind a 500 and costs a full re-diagnosis per incident. Every handler logs the cause before it masks. Missing config/dependency is a NAMED condition in the response (which var, which service), never a generic error. </check><check id="4" name="duplication" detection="jscpd or manual review"> Same logic in 3+ places = extract to utility. </check><check id="5" name="complexity" detection="eslint complexity rule"> Functions under 30 lines. No nested ternaries > 2 levels. </check><check id="6" name="gate_integrity" detection="seed a known violation and confirm the gate goes red"> A gate nobody has seen fail is not a gate. Before trusting any check you ship or run: - Prove it red. Seed the exact violation it claims to catch, watch it fail, then unseed. A gate that has only ever printed PASS is measuring nothing. - The checker re-derives its evidence. A hash, a "PASS" string, a file that merely exists, or a count supplied by the thing being checked is an assertion, not evidence. Read the bytes, run the command, fetch the remote. - Exit 0 with empty or blank output is a failure, not a pass. Never pipe a gate to `tail`/`head`; the pipe reports the pager's exit code. - A gate that stays green after you delete the code it guards is blind. Delete-and-rerun is the cheapest mutation test there is. - Randomness in a gate makes it a coin flip. Pin the seed or pin the input. - Absence of a run is red, not pending. </check> </big5>

<quick_checks>

# 1. Missing validation
grep -r "req\.body" --include="*.ts" --include="*.js" | grep -v "parse\|validate\|z\." | head -5

# 2. Empty catch blocks
grep -r "catch.*{}" --include="*.ts" --include="*.js" | head -5

# 3. String concat in queries (SQL injection)
grep -rE "SELECT.*\$\{|INSERT.*\$\{" --include="*.ts" --include="*.js" | head -5

</quick_checks>

<data_correctness> For any pipeline that extracts or transforms figures (financial, metrics, counts):

  • Parse deterministically (a real parser, regex, typed loader). The LLM never generates, transforms, or "fixes" numeric values.
  • Units are explicit at parse time (percent vs fraction, counts vs currency); a value never crosses unit categories through arithmetic.
  • Tie-out gate: derived aggregates must reproduce the source's own totals before any output is shown downstream. A delta between your output and the source is assumed to be YOUR normalization bug until proven otherwise.
  • Silent-empty guard: "no findings" produced from an empty parse is a failure of the parse, not a finding. </data_correctness>
<verdict> Any Big 5 violation = NOT READY Fix before commit. No exceptions. </verdict>

<on_complete> agentdb write-end '{"skill":"quality","big5_checked":true,"violations":N}' </on_complete>

</skill>

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