# Skillify > Capture a repeatable process from the current session into a reusable Amplifier SKILL.md skill file. Use when the user wants to create a skill, save a workflow as a skill, turn a process into a reusable skill, or mentions "skillify", "create skill", "make a skill", "save as skill", "capture workflow", "turn this into a skill", "new skill", or wants to automate a repeatable process they just performed. - Skill: `microsoft/skillify` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add microsoft/skillify` - Raw SKILL.md: https://api.skillmd.com/api/skills/microsoft/skillify/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Productivity - Author: Microsoft (https://skillmd.com/u/microsoft) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/microsoft/skillify --- # Skillify Create a well-structured, reusable Amplifier SKILL.md skill file that captures a repeatable process from the current session so it can be invoked again later via `/skill-name`. The skill must conform to the Agent Skills specification with Amplifier extensions. ## Inputs - `$ARGUMENTS`: (Optional) Description of the process to capture as a skill. ## Steps ### 1. Consult Skills-Assist Before writing any skill content, load the authoritative skills reference: ``` load_skill("skills-assist") ``` Ask skills-assist about: - Current frontmatter field reference and best practices - Fork vs inline decision criteria - Step annotation conventions - Testing patterns for the type of skill being created skills-assist is the source of truth for Amplifier skill conventions. The examples in this skill are illustrative samples — consult skills-assist for the complete and up-to-date specification. **Success criteria**: You have loaded and consulted skills-assist for the latest skill authoring conventions. ### 2. Analyze the Session Before asking the user anything, analyze the conversation history to identify: - What repeatable process was performed - What the inputs and parameters were - The distinct steps in order - The success criteria and artifacts for each step (not just "writing code" but "an open PR with CI passing") - Where the user corrected or steered you — these are important design signals - What tools were used (read_file, write_file, bash, glob, grep, delegate, etc.) - What agents were delegated to - What the goals and measurable outcomes were **Success criteria**: You have a clear mental model of the process, its steps, inputs, outputs, and success criteria. ### 3. Interview the User **Output density rule:** Group related decisions into natural clusters — not one question per turn (tedious) and not everything at once (overwhelming). Present your analysis first, let the user absorb it, then ask related questions together. For example, present identity/routing decisions as one cluster, execution model decisions as another. Calibrate interview depth to the complexity of the process. A simple 2-step workflow needs 1-2 rounds. A complex multi-step workflow with parallel tasks and irreversible actions needs the full treatment. #### Round 1: High-level confirmation - Suggest a name and description for the skill based on your analysis. Ask the user to confirm or rename. - Suggest high-level goals and specific success criteria. - Present the high-level steps you identified as a numbered list. #### Round 2: Details and arguments - If the skill needs arguments, suggest them based on what you observed. - Ask whether this skill should run inline (in the current conversation) or forked (`context: fork`) as an isolated subagent. Forked is better for self-contained tasks that don't need mid-process user input; inline is better when the user wants to steer mid-process. - Ask where the skill should be saved: - **This project** (`.amplifier/skills//SKILL.md`) — project-specific - **Personal** (`~/.amplifier/skills//SKILL.md`) — follows you across projects - **The skills bundle** (`amplifier-bundle-skills/skills//SKILL.md`) — if contributing to the curated collection #### Round 3: Per-step breakdown (complex processes only) Skip this round for simple skills with obvious steps. For complex skills: - What does each step produce that later steps need? - What proves each step succeeded? - Should the user confirm before irreversible actions? - Are any steps independent and could run in parallel? - How should each step be executed? (direct, delegate to an agent, user action) - What are hard constraints or preferences? Pay special attention to places where the user corrected you during the session. These corrections are the most valuable design signals. #### Round 4: Final confirmation - Confirm when this skill should be invoked and review trigger phrases for the description field. - Ask for gotchas or edge cases, if still unclear. Stop interviewing once you have enough information. Don't over-ask for simple processes. **Success criteria**: You have all the information needed to write the SKILL.md and the user has confirmed the design. ### 4. Write the SKILL.md Create the skill directory and file at the location the user chose in Round 2. Use this template as a starting point. Consult skills-assist for the full set of available frontmatter fields and current conventions: --- name: {{skill-name}} description: > {{TRIGGER FIRST: the condition under which this applies, then what it does. Then "USE WHEN ." Then "DO NOT USE WHEN — use ." This is what the model sees in the skills visibility list, on EVERY request of every session that can see the skill, invoked or not. HARD CAP 400 characters (ERROR at 800) — enforced by foundation:recipes/validate-bundle-repo.yaml Phase 2.82. ZERO blocks and zero tags: a trigger belongs in the USE WHEN clause as a decision rule, not as a worked dialogue.}} user-invocable: true allowed-tools: {{list of Amplifier tool names observed during the session, e.g.:}} {{- read_file}} {{- write_file}} {{- edit_file}} {{- bash}} {{- glob}} {{- grep}} {{- delegate}} model_role: {{general | coding | reasoning | critique | writing | fast}} {{Only include if forked:}} {{context: fork}} {{disable-model-invocation: true}} --- # {{Skill Title}} {{Brief statement of what the skill does and its goal. Define concrete success artifacts — not just "writing code" but "a passing test suite and a committed implementation."}} ## Inputs - `$ARGUMENTS`: {{Description of expected arguments, or "(Optional) ..."}} ## Steps ### 1. {{Step Name}} {{Specific, actionable instructions. Include commands when appropriate.}} **Success criteria**: {{REQUIRED on every step. What proves this step is done and we can move on.}} #### Step annotations (key examples — consult skills-assist for complete conventions): - **Success criteria** is REQUIRED on every step - **Execution**: `Direct` (default — omit if direct), `Delegate to [agent]` (e.g., "Delegate to foundation:explorer"), or `[human]` (user does it) - **Artifacts**: Data this step produces that later steps need (PR number, commit SHA, file path). Only include if later steps depend on it. - **Human checkpoint**: Pause and ask before irreversible actions (merging, deploying, sending messages, destructive operations) - **Rules**: Hard constraints. User corrections during the original session are especially useful here. #### Structural conventions: - 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 (key examples — consult skills-assist for complete reference): - `description` must carry all routing weight — the trigger, the "USE WHEN..." deciding factor, and the "DO NOT USE WHEN... — use ``" boundary belong here, since this is what the visibility hook shows to the model. State a trigger as a decision rule, never as a worked `` dialogue - `allowed-tools` should be the minimum set needed - `context: fork` only for self-contained skills that don't need user steering - If forked, usually pair with `disable-model-invocation: true` - `model_role` should match the skill's primary cognitive task - `user-invocable: true` registers the skill as a `/name` slash command - Names must be kebab-case, max 64 characters Before you hand the skill back, measure the description you just wrote: python3 -c "import yaml,sys; d=yaml.safe_load(open('SKILL.md').read().split('---')[1])['description']; print(len(d), 'chars'); sys.exit(len(d) > 400)" If it exceeds 400, cut advocacy first (prose that sells the skill rather than routing to it), then compress the capability sentence. Never cut a trigger or a DO-NOT-USE boundary to make the number — a description that got shorter by dropping a routing fact is a mis-routing waiting to happen, and it surfaces later as "it didn't use the right thing" with nothing pointing back here. **Success criteria**: The complete SKILL.md content has been drafted, and its `description` measures at or below 400 characters. ### 5. Test the Skill Before committing, verify the skill works: 1. Save the skill to `.amplifier/skills//SKILL.md` (immediately discoverable, no config changes needed). 2. In the same session, call `load_skill("")` and verify: - The skill loads without errors - The description is clear enough for routing - The body instructions are actionable 3. Optionally, spawn a test session via `delegate(agent="self", context_depth="none")` that loads the skill and follows its instructions against a synthesized test input. 4. If issues are found, fix and re-test. **Success criteria**: The skill loads correctly and its instructions are actionable. ### 6. Review and Save Before writing the file, present the complete SKILL.md content in a yaml code block so the user can review it with proper syntax highlighting. Ask the user: "Does this look good to save?" After confirmation: 1. Create the skill directory: ``` mkdir -p / ``` 2. Write the SKILL.md file to `//SKILL.md` 3. If the skill references companion files (templates, examples, scripts), create those as well. 4. 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 - If saved to a project or personal directory, the skill will be auto-discovered on next session start **Success criteria**: The file is written to disk and the user knows how to use it.