skillify

/skillify — Create, adapt, or update a skill

One skill, three modes. Routes automatically to the right one based on input signal.

Consolidates and replaces the prior create-skill, adapt-skill, and update-skill (all merged as of v0.2.0 for vocabulary-moat consistency with the -ify trifecta).

Step 0 — Detect mode

Route by signal:

Signal	Mode
Invoked mid-conversation with substantive recent workflow discussion	CREATE / from-chat (default)
/skillify from-video <url> OR /skillify + a video URL	CREATE / from-video
/skillify from-dump + pasted brief/transcript/notes	CREATE / from-dump
/skillify from-scratch <name> OR "make a new skill called X" with no context	CREATE / from-scratch
/skillify <github-url> OR "adapt this skill" / "port this skill" / "fork this skill" + external source	ADAPT
/skillify <existing-skill> <change> OR "update X skill" / "propagate this learning"	UPDATE / targeted
/skillify update (mid-conversation)	UPDATE / from-chat — scan recent chat for learnings
/skillify usage <skill>	UPDATE / usage — review recent runs, suggest improvements

If ambiguous, ask before proceeding. Never guess between CREATE and ADAPT if there's an external URL involved.

---

Mode: CREATE

Build a new skill from chat / video / dump / scratch.

Step 1 — Confirm the input source

Sub-mode	What
from-chat (default)	Extract the skill from the workflow discussed in this conversation. Most common case.
from-video	User recorded a Loom / Zoom / screen-share. Calls watch-video in visual mode → transcript + key visual moments → SKILL.md. Best for visual/UI-heavy workflows.
from-dump	User pastes a brief, prior conversation transcript, exported chat, or notes.
from-scratch	Fresh idea with no source material — interactive Q&A.

Step 2 — Defer to Anthropic guidance for the schema

Don't reinvent SKILL.md format rules. For frontmatter, description-writing, references/ structure, and skill best practices:

• compound-engineering:create-agent-skill (agent) — expert guidance for creating + editing Claude Code skills
• compound-engineering:skill-creator (skill) — deeper guide for effective skills
• compound-engineering:heal-skill (skill) — for fixing existing skills
• anthropics/skills — official Anthropic examples
• agentskills.io — open Agent Skills spec

Call those directly when in doubt about format. skillify orchestrates your workflow — which repo, which conventions, which cross-references. Format expertise lives upstream.

Step 3 — Pick target repo

Load the user's sibling repo list from ${MAKERSKILLS_CONFIG:-$HOME/.config/makerskills}/skillify/repos.yaml if present. Otherwise default to makerskills (this repo). Example format:

# ~/.config/makerskills/skillify/repos.yaml
repos:
  - name: makerskills
    domain: Personal cross-cutting operator work
    path: ~/code/makerskills
  - name: marketingskills
    domain: Generic marketing tactics
    path: ~/code/marketingskills

Infer target if obvious from the skill's domain; ask if ambiguous.

Step 4 — Synthesize per sub-mode

from-chat:

1. Read backward through this conversation. Look for a workflow repeated multiple times, a process re-explained, opinions restated, tooling/voice/output decisions.
2. Identify the load-bearing pieces: trigger, steps, references, success shape, voice.
3. Surface gaps that need clarification before scaffolding — ask 2–4 tight questions.
4. Draft SKILL.md, citing chat moments inline ("Per your message about X…") so the user can verify.

from-dump:

1. Parse the dump (Notion page, Slack thread, Loom transcript, teammate brief, exported ChatGPT conversation).
2. Same synthesis logic as from-chat.
3. Cite where in the dump each rule came from.

from-video:

1. Take the URL or local path.
2. Call watch-video <url> visual — get transcript + key visual moments + summary.
3. Read outputs: <workdir>/transcript.txt, <workdir>/moments.md, <workdir>/summary.md.
4. Synthesize: trigger (first 30s), steps (visual moments + matching transcript), tools used, decision points ("if X do Y" moments), success shape (last few seconds), voice cadence.
5. Surface gaps — ambiguous moments needing clarification.
6. Draft SKILL.md citing video moments by timestamp.
7. Optionally save the source to second-brain as call-<slug>.md or note-<slug>.md.

Heuristic: recording >10 minutes = process probably too big for one skill. Suggest splitting before drafting.

from-scratch: Interactive Q&A:

1. Name (kebab-case, verb-noun preferred — matches watch-video, read-book, paste)
2. One-line purpose
3. Trigger phrases (4–8)
4. Initial reference files?
5. Composes with which other skills?

Step 5 — Generate frontmatter

---
name: <kebab-case>
description: <rich, trigger-rich, ~2–4 sentences. Lead with "When you want to..." Include 4–8 trigger phrases in quotes. Differentiate from adjacent skills.>
metadata:
  version: 0.1.0
---

Description rules (load-bearing — Claude routes by description match):

• Lead with the use case ("When you want to X…")
• Include explicit trigger phrases
• Differentiate from sibling skills
• Mention key references the skill loads
• Keep under ~500 characters

For deeper description-writing guidance, consult compound-engineering:skill-creator.

Step 6 — Scaffold the directory

<target-repo>/skills/<name>/
├── SKILL.md
└── references/
    └── ...

Body follows existing skills' pattern (pm, decide, second-brain):

• # /<name> — <one-line purpose>
• Numbered ## Step N — <phase> sections
• Composes-with cross-references
• Quality notes at the end

Step 7 — Update README, commit, push

Append to target repo's README skill table:

| [`<name>`](./skills/<name>/SKILL.md) | <one-line purpose> |

Commit + push. Report new skill path, commit hash, and reminder that /plugin install or symlink may need a refresh.

Step 8 — Offer follow-ups

• "Flesh out a specific Step now, or come back to it?"
• "Run through compound-engineering:heal-skill for a quality pass?"
• "Should this compose with [adjacent skill]?"

---

Mode: ADAPT

Port an external skill (GitHub URL, agentskills.io, local disk, or pasted SKILL.md) into your namespace.

Step 1 — Fetch the source

Accept:

• GitHub URL to a SKILL.md or repo (https://github.com/<owner>/<repo> or full path to SKILL.md)
• agentskills.io URL or skills.sh URL
• Local path to an existing skill on disk (other plugins in ~/.claude/plugins/)
• Pasted SKILL.md content

# GitHub repo: clone shallow
git clone --depth 1 <url> /tmp/skillify-adapt-<short-id>/
# OR fetch a single file
gh api -H "Accept: application/vnd.github.raw" repos/<owner>/<repo>/contents/SKILL.md > /tmp/adapt-source.md

Capture the commit SHA so attribution can point at a stable revision.

Step 2 — Analyze + classify

Read source SKILL.md + any references/ it ships. Classify into three buckets per references/adapt-buckets.md:

Bucket	What	Example
Keep verbatim	Format, schema, mechanic, scripts, frameworks	Question banks, ffmpeg flags, regex patterns, JSON schemas
Adapt	Tool defaults, paths, voice, naming, opinions	Their video tool → your watch-video, their kanban → your pm, their voice → your voice
Add	Cross-references to your existing skills, composition notes, your conventions	"Composes with decide," "Saves to second-brain raw/," "Uses MLX-Whisper local"

Output classification as a table for approval before writing anything. Don't silently rewrite — surface the changes.

Step 3 — License check

Read the source's LICENSE. Per references/adapt-license-check.md:

License	Action
MIT / Apache-2.0 / BSD / ISC / CC0	✅ Green — proceed
MPL-2.0 / LGPL	🟡 Yellow — adapt OK; warn about file-level reciprocity
GPL-2.0 / GPL-3.0 / AGPL	❌ Red — contagion risk. Surface before proceeding.
Proprietary / no license	❌ Red — stop. No legal basis to copy.
Unclear	🟡 Yellow — ask, default to skip if uncertain

For permissive licenses, attribution is the only requirement — handled in Step 6.

Step 4 — Pick target repo

Same table as CREATE Step 3.

Step 5 — Rewrite

Keep verbatim: copy as-is; add <!-- from <source> --> marker if helpful.

Adapt:

• Naming: rename to verb-noun if not (matches watch-video, read-book, paste)
• Tool swaps: their generic kanban → pm, their video tool → watch-video, their note system → second-brain, their decision framework → decide
• Voice: apply your voice rules — direct, conviction-coded. For social-adjacent skills, apply link-placement rule.
• Paths: their ~/outputs/ → your ~/Documents/<skill>-<...>/ convention; their config → your references/ pattern
• Names + context: generic examples → your portfolio context where the skill needs it

Add:

• Cross-references to existing sibling skills
• Composition notes (which other skills this calls or feeds)
• Your conventions (frontmatter version, references/ subdir structure, BACKLOG entry if it spawns sub-ideas)

Step 6 — Attribution file

Write references/attribution.md:

# Attribution

- **Source**: <original SKILL.md URL>
- **Repository**: <repo URL>
- **Author**: <name + handle>
- **Commit SHA**: <SHA at time of adapt>
- **License**: <license name + URL>
- **Adapted**: <YYYY-MM-DD>

## What was kept verbatim
- ...

## What was adapted
- <old> → <new>

## What was added
- ...

## License compliance
<upstream LICENSE text if MIT/BSD/Apache requires it, OR reference where in this repo it lives>

## Upgrade path
Run `git ls-remote <repo-url> HEAD`. If SHA differs from above, re-run `/skillify <same-url>` for a 3-way merge.

For MIT / Apache / BSD, LICENSE text either gets copied into this file or the source LICENSE file gets copied to the skill dir.

Step 7 — Scaffold + commit

Same as CREATE Steps 6–7. Commit message: "Adapt <name> skill from <source-name>".

Step 8 — Report + offer follow-ups

• Commit hash + new skill path
• Show three-bucket classification one more time so the diff is clear
• Offer:
  • "Flesh out the adaptations? Some defaults may still need your touch."
  • "Run compound-engineering:heal-skill for a QA pass?"
  • "Set up an upstream-check reminder via loopify?"

---

Mode: UPDATE

Improve existing skill(s) from learnings.

Step 1 — Confirm sub-mode

Sub-mode	When
from-chat (default)	Scan recent conversation for learnings, identify affected skill(s)
from-dump	User provides a brief, feedback, transcript, postmortem
targeted	User names the skill + change directly. Skip discovery.
usage	Review recent runs of the named skill, identify gaps

Default when invoked mid-conversation with substantive recent activity: from-chat.

Step 2 — Extract learning(s)

For from-chat / from-dump / usage: look for change-worthy signals (full taxonomy in references/update-change-types.md):

• Corrections — "no, don't do that anymore"
• Validations — "yes that worked — bake it in"
• New patterns — user just did an undocumented workflow; encode it
• Voice / tone updates — like the spoken-aloud rule for slide-deck
• Tool changes — switched defaults
• Architectural decisions — naming conventions, file layout, frontmatter
• Gaps — something the skill should have handled but didn't

Output: a clear list of N learnings, each phrased as a single change.

For targeted: skip extraction — user already named the change.

Step 3 — Identify affected skills

For each learning, search across all SKILL.md + references/ in the current repo (and optionally all sibling repos from your repos.yaml with --cross-repo):

grep -rln "<key terms>" ~/code/makerskills/skills/ --include="*.md"

# Across all sibling repos (list yours in $MAKERSKILLS_CONFIG/skillify/repos.yaml):
config="${MAKERSKILLS_CONFIG:-$HOME/.config/makerskills}/skillify/repos.yaml"
for raw in $(yq -r '.repos[].path' "$config"); do
  repo="${raw/#\~/$HOME}"  # expand leading ~ to $HOME so grep resolves the real path
  grep -rln "<terms>" "$repo/skills/" --include="*.md" 2>/dev/null
done

Classify by confidence:

Confidence	What	Action
High	Direct keyword match + clearly same topic	Propose change
Medium	Adjacent topic — rule might apply	Surface for explicit approval
Low	Tangential — rule could be stretched	Mention but don't propose

See references/update-propagation.md for the cross-skill propagation playbook.

Step 4 — Memory-vs-skill check

For each learning: is this skill-specific or a broader principle?

Type	Where it goes
Skill-specific rule	Edit the SKILL.md / references file directly
Cross-cutting principle	~/.claude/memory/feedback_<topic>.md
Both	Write the memory file AND update the skill(s) that immediately apply

Example: "links go in first comments, not body" → applies to jab-hook AND is a broader social principle → both update jab-hook AND save feedback_social_link_placement.md.

Offer the memory write explicitly: "This looks like a principle, not just a skill rule. Save to memory as feedback_<slug>.md?"

Step 5 — Propose diffs per file

For each affected file, show change as before/after or unified diff:

### `skills/<skill>/SKILL.md` — proposed change

**Before** (lines NN–NN):
> <existing text>

**After**:
> <new text>

**Why**: <one-line rationale>

**Version bump**: 0.2.0 → 0.2.1 (PATCH — clarification)

**Approve / edit / skip?**

Per-file approval. Don't batch — small changes are easy to OK; bundling forces all-or-nothing.

See references/update-versioning.md for semver rules.

Step 6 — Apply approved changes

1. Use Edit to apply the exact change
2. Bump metadata.version per the suggested level
3. If the change involves a rename, follow the cross-reference update pattern (grep all files, update all references)

Batch multiple file changes within one skill into a single commit.

Step 7 — Commit + push

Group by skill. Examples:

• One-skill update: "slide-deck: emphasize spoken-aloud voice (write for the ear, not the page)"
• Multi-skill propagation: "jab-hook, marketingskills:social: link placement (no inline URLs)"
• Cross-repo: one commit per repo. Don't atomic-commit across repos.

Step 8 — Report

Show:

• Learnings extracted
• Skill(s) updated (with version bumps)
• Memory files written
• Whether compound-engineering:heal-skill would be a useful QA pass
• Anything flagged but skipped

When NOT to use UPDATE mode

Don't add ceremony to surgical edits. If the user says "add this one bullet to X.md," just Edit. UPDATE mode earns its keep when:

1. Multiple skills are affected by one learning (cross-skill propagation)
2. Bulk extraction from a long session
3. Memory-vs-skill decision is unclear (need explicit triage)
4. Version discipline matters (about to commit and want sane semver)

For one-line updates with no cross-skill implications: just Edit.

---

Composes with

• watch-video — load-bearing for CREATE / from-video. Visual-mode output (transcript + key visual moments + summary) is the input for skill synthesis. Always call in visual mode for process recordings — UI state matters as much as words.
• second-brain — optionally capture source video/dump as raw/call-<slug>.md or raw/note-<slug>.md so the source artifact lives alongside the skill it produced.
• toolify — sibling in the -ify trifecta. Use toolify when the goal is adding an integration/MCP/API, not authoring a skill.
• loopify — sibling in the -ify trifecta. Use loopify for agent-loop setup rather than a skill.
• compound-engineering:create-agent-skill (agent) — call for format and best-practices expertise
• compound-engineering:skill-creator (skill) — deeper best-practices reference
• compound-engineering:heal-skill (skill) — QA pass after any mode

Notes on quality

• Cite the source. Whether chat, video, dump, external URL, or user-named learning — the SKILL.md body should reference where each decision came from. Makes revising easier.
• Don't over-engineer v0.1. Ship a minimal SKILL.md + 1–2 references files. Iterate after first use. Most generated skills are over-scoped.
• Verb-noun naming where possible (matches watch-video, read-book, paste). Single-word noun names are fine for distinctive concepts (decide, pm, paste, skillify).
• Always reference Anthropic's official guidance for the schema — don't invent format conventions.
• Attribution is non-negotiable for ADAPT mode. Every adapted skill ships with references/attribution.md.
• License is a hard gate. Don't proceed on GPL/proprietary without explicit approval.
• Per-file approval in UPDATE mode. Small changes are easy to OK; bundling forces all-or-nothing.