Catalog
yeachan-heo/skillify

yeachan-heo

skillify

Turn a repeatable workflow from the current session into a reusable OMC skill draft

v1.0LATEST
NewUpdated Sep 9, 2026

Skillify

Use this skill when the current session uncovered a repeatable workflow that should become a reusable OMC skill.

Goal

Capture a successful multi-step workflow as a concrete skill draft instead of rediscovering it later.

Quality Gate

Before extracting a skill, all three should be true:

  • "Could someone Google this in 5 minutes?" → No.
  • "Is this specific to this codebase, project, or workflow?" → Yes.
  • "Did this take real debugging, design, or operational effort to discover?" → Yes.

Prefer skills that encode decision-making heuristics, constraints, pitfalls, and verification steps. Avoid generic snippets, boilerplate, or library usage examples that belong in normal documentation.

Workflow

  1. Identify the repeatable task the session accomplished.
  2. Extract:
    • inputs
    • ordered steps
    • success criteria
    • constraints / pitfalls
    • verification evidence
    • best target location for the skill
  3. Decide whether the workflow belongs as:
    • a repo built-in skill
    • a user/project learned skill
    • documentation only
  4. When drafting a learned skill file, output a complete skill file that starts with YAML frontmatter.
    • Never emit plain markdown-only skill files.
    • Do not write plain markdown without frontmatter.
    • Minimum frontmatter:
      ---
      name: <skill-name>
      description: <one-line description>
      triggers:
        - <trigger-1>
        - <trigger-2>
      ---
      
    • Write learned/user/project skills to flat file-backed paths:
      • ${CLAUDE_CONFIG_DIR:-~/.claude}/skills/omc-learned/<skill-name>.md
      • .omc/skills/<skill-name>.md
    • Remember that uncommitted skills are still worktree-local until committed or copied to a user-level directory.
  5. Draft the rest of the skill file with clear triggers, steps, success criteria, and pitfalls.
  6. Point out anything still too fuzzy to encode safely.

Rules

  • Only capture workflows that are actually repeatable.
  • Keep the skill practical and scoped.
  • Prefer explicit success criteria over vague prose.
  • If the workflow still has unresolved branching decisions, note them before drafting.
  • Keep omc-learned as the storage directory name for compatibility; do not present it as the public invocation name.

Output

  • Proposed skill name
  • Target location
  • Draft workflow structure or complete skill file
  • Verification or quality-gate notes
  • Open questions, if any
Files1
1 files · 1.0 KB

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Overall Score

78/100

Grade

B

Good

Grades are signals, not a certification. Always review a skill yourself before use.

Safety

85

Quality

75

Clarity

82

Completeness

72

Summary

Skillify is a meta-skill that teaches an AI agent how to capture and extract repeatable workflows from a session into reusable OMC skill drafts. It provides a structured quality gate, workflow steps, and formatting requirements to ensure that only workflows worthy of becoming skills are formalized.

Detected Capabilities

file writeskill file generationworkflow documentation

Trigger Keywords

Phrases that agents use to match this skill to user intent.

extract workflow to skillconvert session to skillcapture repeatable processformalize debugging patterncreate learned skill

Use Cases

  • Extract a complex debugging workflow into a reusable skill after solving a rare production issue
  • Formalize a multi-step database migration pattern discovered during development into a learned skill
  • Convert a specialized testing or deployment routine into a skill to prevent knowledge loss
  • Capture domain-specific code generation or refactoring heuristics that emerge during a session into a skill
  • Document constraints and decision-making for a complex operational task as a skill for future sessions

Quality Notes

  • Strong quality gate that prevents generic or trivial workflows from becoming skills
  • Clear rules about scope and specificity prevent over-broad skill creation
  • Explicit guidance on frontmatter format and file locations ensures consistency
  • Workflow structure is logical and pedagogically sound
  • Helpfully distinguishes between repo-built-in skills, learned skills, and documentation-only knowledge
  • Includes verification step to catch unresolved branching or unsafe-to-encode workflows
  • Minor: Could benefit from concrete end-to-end example showing before/after of skill extraction
  • Minor: Could provide more detail on what makes a workflow 'fuzzy' or unsafe to encode
Model: claude-haiku-4-5-20251001Analyzed: Sep 9, 2026

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