aio-skillify

name	aio-skillify
description	Capture this session's repeatable process into a reusable SKILL.md file via guided interview.
when_to_use	skillify, capture this workflow, save this as a skill, turn this into a skill, make a skill from this session
effort	medium
allowed-tools	["Read","Write","Edit","Glob","Grep","AskUserQuestion","Bash(mkdir:*)"]
argument-hint	[optional description of the process to capture]

Skillify - Capture Session Workflows as Reusable Skills

Turn what you just did into a skill that works every time.

Goal

Analyze the current session, interview the user about their intent, and produce a well-structured SKILL.md file that captures the repeatable process.

Steps

1. Load Session Memory

Session memory is stored at ~/.claude/projects/{sanitized-cwd}/{sessionId}/session-memory/summary.md where sanitized-cwd replaces all non-alphanumeric characters with -.

Use Glob to find and Read the most recent session memory for the current project. This gives you a compact summary of the entire session including compacted context. If no session memory exists, skip this step and rely on the current conversation context.

2. Analyze the Session

Combine the session memory with the current conversation to identify:

• What repeatable process was performed
• What the inputs/parameters were
• The distinct steps (in order)
• The success artifacts/criteria for each step (e.g. not just "writing code" but "an open PR with CI fully passing")
• Where the user corrected or steered you - these are gold for capturing preferences
• What tools and permissions were needed
• What agents were used (if any)

3. Interview the User

Use AskUserQuestion for ALL questions. Never ask via plain text. For each round, iterate until the user is happy. Always include a freeform "Other" option - do NOT add your own "Needs tweaking" option.

Round 1: High-level confirmation

• Suggest a name and description for the skill based on your analysis
• Suggest high-level goal(s) and specific success criteria

Round 2: Details

• Present the high-level steps as a numbered list. Tell the user you will dig into details next round.
• If the skill needs arguments, suggest them based on what you observed.
• Ask if this skill should run inline (in current conversation, user can steer mid-process) or fork (sub-agent with own context, better for self-contained tasks).
• Ask where to save. Suggest a default based on context:
  • This repo (.claude/skills/<name>/SKILL.md) - for workflows specific to this project
  • Personal (~/.claude/skills/<name>/SKILL.md) - follows you across all repos
  • Plugin (<plugin-path>/skills/<name>/SKILL.md) - if part of a plugin

Round 3: Breaking down each step For each major step, if not glaringly obvious, ask:

• What does this step produce that later steps need? (data, artifacts, IDs)
• What proves this step succeeded, and that we can move on?
• Should the user confirm before proceeding? (especially irreversible actions: merging, sending messages, destructive ops)
• Are any steps independent and could run in parallel?
• How should the skill execute? (direct, Task agent, agent team for concurrent steps)
• What are hard constraints or preferences? Things that must or must not happen?

Do multiple rounds here if needed - one per step for complex workflows. Iterate as much as needed.

IMPORTANT: Pay special attention to places where the user corrected you during the session.

Round 4: Final questions

• Confirm when this skill should be invoked, and suggest/confirm trigger phrases (e.g. "cherry-pick to release", "CP this PR", "hotfix")
• Ask for any gotchas or things to watch out for

Stop interviewing once you have enough information. Don't over-ask for simple processes!

4. Write the SKILL.md

Use this format:

---
name: {{skill-name}}
description: {{one-line description including trigger phrases}}
allowed-tools:
  {{list of tool permission patterns observed during session}}
when_to_use: {{detailed description of when to auto-invoke, starting with "Use when..."}}
argument-hint: "{{hint showing argument placeholders}}"
arguments:
  {{list of argument names}}
context: {{inline or fork -- omit for inline}}
---

# {{Skill Title}}

Description of skill

## Inputs
- `$arg_name`: Description of this input

## Goal
Clearly stated goal for this workflow. Best if you have clearly defined artifacts or criteria for completion.

## Steps

### 1. Step Name
What to do in this step. Be specific and actionable. Include commands when appropriate.

**Success criteria**: ALWAYS include this! Shows the step is done and we can move on.

...

Per-step annotations (use where helpful):

• Success criteria - REQUIRED on every step
• Execution: Direct (default), Task agent, Teammate (parallel + inter-agent), or [human]
• Artifacts: Data this step produces that later steps need
• Human checkpoint: When to pause for user confirmation (irreversible actions, error judgment, output review)
• Rules: Hard rules for the workflow. User corrections during the session are especially useful here.

Step structure tips:

• Steps that can run concurrently use sub-numbers: 3a, 3b
• Steps requiring the user to act get [human] in the title
• Keep simple skills simple -- a 2-step skill doesn't need annotations on every step

Frontmatter rules:

• allowed-tools: Minimum permissions needed. Use patterns like Bash(gh:*) not Bash
• context: Only set fork for self-contained skills that don't need mid-process user input
• when_to_use is CRITICAL - tells the model when to auto-invoke. Start with "Use when..." and include trigger phrases
• arguments and argument-hint: Only include if the skill takes parameters. Use $name in the body for substitution

5. Review and Save

Before writing the file:

1. Output the complete SKILL.md content as a yaml code block so the user can review it with syntax highlighting
2. Ask for confirmation using AskUserQuestion: "Does this SKILL.md look good to save?"
3. Only write the file after user confirms

After writing, tell the user:

• Where the skill was saved
• How to invoke it: /{{skill-name}} [arguments]
• That they can edit the SKILL.md directly to refine it