create-skill

name	create-skill
description	Use when creating or updating a skill file in `.claude/skills/`. Produces compact, tag-based instruction files that follow the project ruleset.

Principles

PURPOSE: a skill is a self-contained, reusable SKILL.md that shifts the model's output toward a desired pattern — otherwise it is prose absorbed once, not an instruction the model can reapply LIFETIME_COST: optimize tokens across every future load plus the downstream cost of misapplying a rule, not the file's raw length — otherwise a rule that misfires downstream costs more than the tokens it saved SIGNAL: a rule earns its load cost only by changing what the model produces — otherwise it restates the model's default, shifting nothing at pure cost REASONING: a divergent rule's why lets the model extend it to unnamed cases — otherwise the bare rule reverts to the model's default at the first case it does not name INVERSION: put each rule's failure mode in its why, not in a negative directive — otherwise a positive-only rule hides the cost it guards against and the reader cannot separate the pattern from its rationale SEGMENTATION: one skill per responsibility, one section per concept, one tag per rule — otherwise a rule mixing concerns cannot be reused or removed without collateral damage

Method

SCOPE_PATH: write to .claude/skills/<skill-name>/SKILL.md FRONTMATTER: name (kebab-case), description (one-line trigger sentence) SECTIONS: frontmatter, principles, method, validation TAG_FORMAT: UPPER_SNAKE_CASE: value, one rule per line, noun-led TAG_STYLE: name the desired pattern as a noun tag, positive framing, stable vocabulary KEEP_TEST: delete each candidate rule and keep it only if the model's output would change WHY_CLAUSE: write each divergent rule as a positive directive, then — otherwise <the failure that omitting it causes>; leave arbitrary conventions bare FRAMING: express limits as orderings and comparisons, not arithmetic thresholds the model self-counts; prefer domain-loaded words over coined abstractions FLOW_STYLE: express a sequence as an arrow chain or named phase tags, never a numbered list — the model tracks named stages and transitions, not ordinals DEDUP: fold any rule that restates another rule or a section heading REVERSE_BRAINSTORM: add tags that block known failures — trivial signals, content restated across skills, narrative prose, markdown tables, numbered steps, generic names and values, mixed tag responsibilities CODE_EXAMPLE: fenced snippet only where it resolves ambiguity a tag cannot, never as decoration

Validation

TAG_CHECK: UPPER_SNAKE_CASE, one rule per line, noun-led SIGNAL_CHECK: every rule passes KEEP_TEST; each divergent rule carries its why, each convention stays bare FRAMING_CHECK: positive directives throughout, each divergent rule's why introduced by — otherwise and naming the failure it prevents TOKEN_CHECK: no filler phrases, no narrative prose, no markdown tables, no numbered lists, no decorative code fences SCOPE_CHECK: frontmatter description matches the skill's actual trigger PATH_CHECK: declared paths exist in the workspace SECTION_CHECK: mandatory sections present — Principles, Method, Validation