publish-skill

Skill: publish-skill

Convert a personal agent skill into a clean, distributable, open-source-ready skill package. This skill walks through a 7-phase pipeline that audits for personally identifiable information, generalizes language and role assumptions, verifies license compatibility, checks cross-platform adapter needs, and prepares the final package for commit.

Communication Rules

• Communicate with the user in their preferred language
• Use English for technical terms (PII, MIT, CC BY, GPL, YAML frontmatter)
• Present audit findings in structured tables

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Phase 0: Init and Identify Source

Required Inputs

Collect from the user:

1. Source skill path: directory containing the personal skill (e.g., ~/.claude/skills/my-skill/ or ~/.agents/skills/my-skill/)
2. Target package path: directory of the distributable package (e.g., ~/workspace/<your-package>/)
3. Target license: license of the package (default: MIT)

Actions

1. Read SKILL.md from the source skill directory
2. Inventory all files recursively (ls -R)
3. Classify skill type:
   • Standalone: self-contained skill with no agent delegation
   • Orchestrator: delegates to sub-agents (NOT suitable for distribution without refactoring)
   • Wrapper: thin wrapper around another tool/API
4. Present inventory table to the user:

| File | Lines | Type | Notes |
|------|-------|------|-------|

Gate: User confirms source skill and target package before proceeding.

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Phase 0.5: Skill-Worthiness Gate

Before spending effort on PII scrubbing and generalization, confirm the workflow is worth distributing as a skill at all. A skill earns its place by encoding a reusable decision heuristic, a hard-won constraint, or a verification step — not a snippet anyone could reconstruct in five minutes. Apply all three gates; any "no" (or "yes" on the inverse) stops publication in favor of documentation or a memory note instead.

Gate	Question	Pass condition
Uniqueness	Could a competent user get the same result by searching the web for ~5 minutes, or by asking a general assistant with no skill installed?	No
Specificity	Does it encode a workflow, decision heuristic, constraint, or convention specific to this domain or a recurring task — rather than a generic code snippet or a standard-library example?	Yes
Effort	Did discovering it take real debugging, study design, operational effort, or a reviewer-anticipation lesson (a pitfall, a verification step, a domain convention)?	Yes

Favor skills that encode reviewer-anticipation, reporting-guideline constraints, verification gates, and decision trees over thin wrappers or one-off snippets. This is the publish-time analogue of the "reusable pattern vs one-off hack" distinction: a workflow that fails the gate is better captured as a doc or a memory note than shipped as a skill that dilutes the catalog.

Gate: If any of the three fails, recommend documentation/memory instead and stop. If the value is real but the skill delegates to private agents, route through Phase 1's orchestrator finding (refactor to standalone first). Only a clear three-way pass proceeds.

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Phase 1: Originality Check

Verify the skill is original work suitable for open-source distribution.

Checks

1. External source: Is this skill adapted from another package or author? Check for attribution headers, license blocks, or "based on" comments.
2. Third-party content: Do any files in references/ come from external sources (published guidelines, textbooks, standards bodies)?
3. Competitive sensitivity: Does the skill reveal proprietary business logic or competitive advantage that should remain private?

Decision Matrix

Finding	Action
Fully original	Proceed to Phase 2
Adapted with compatible license	Add attribution header, proceed
Contains non-compatible third-party content	Flag for removal or URL manifest conversion
Orchestrator with private agent references	STOP -- requires refactoring to standalone first
Competitive/proprietary logic	STOP -- not suitable for open-source

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Phase 2: PII De-identification Audit

Zero tolerance: the skill must have exactly 0 PII matches before proceeding.

Pre-scan Setup

Before running, ask the user for everything that should also count as a PII hit but is unique to them:

• Their name(s) in all languages and romanizations (a placeholder shape: <First Last>|<native-script name>)
• Their institutional affiliation(s) (a placeholder shape: <Institution>|<Hospital>)
• Any collaborator surnames that may appear in drafts or filenames

Combine the inputs into a single grep -E alternation pattern (pipe-separated).

Automated Scan

Run the bundled audit script. The first argument is the skill directory; the second is the user-specific alternation pattern from Pre-scan Setup.

bash ${CLAUDE_SKILL_DIR}/scripts/audit_skill.sh <source_skill_path> \
    "<First Last>|<native-script name>|<Institution>|<Hospital>"

The script runs nine categories that mirror the medsci-skills monorepo linter (scripts/validate_skills.sh):

1. Hardcoded paths (/Users/<name>/, /home/<name>/, ~/Documents, ~/Desktop, ~/Downloads, ~/Projects)
2. Email addresses (any address-shaped string)
3. IP addresses / internal URLs (*.internal, *.local, *.corp)
4. Institutional references (SNUH / AMC / SMC / KAIST / SNU / ASAN / MGH / Mayo Clinic / Johns Hopkins / Samsung Medical / Severance / Asan Medical)
5. Academic roles with names (professor <Surname>, Prof. <Surname>, Dr. <Surname>, PGY[0-9], <한글이름> 교수님)
6. Language hardcoding ("in Korean", "한국어로", "in Japanese", "in Chinese")
7. Location specifics (Seoul / Busan / Daegu / Tokyo / Beijing / Shanghai / Boston / Stanford and Korean variants)
8. Blockquote dated precedent (> YYYY-MM-DD ... lines that reveal an internal review timeline)
9. Author-style filenames (<Surname>{Year}_* pattern, e.g., <Surname>2025_<Journal>_Fig01.png; allow-list excludes generic tokens like Issue2024_, Sample2025_)
10. Binary EXIF metadata (DOCX / PPTX / XLSX / PDF / PNG / JPG / TIFF — scanned via exiftool when installed; skipped silently otherwise with an install hint)

False-positive guard: text scans use grep --binary-files=without-match so byte-stream collisions inside .pyc, .png, or .docx files do not trigger findings. __pycache__/ is also explicitly skipped.

Cross-validation

For categories the script flags, also manually verify with the Grep tool against ${CLAUDE_SKILL_DIR}/references/pii-patterns.md. Pay particular attention to:

• Names not in the EXTRA_PATTERNS argument (e.g., a co-author who appeared only in one early draft)
• Domain-specific institutional acronyms (your institution may not be in the default list)
• Project-specific identifiers like CK-NN, MA-NN, dated cohort names

Output Format

Present all findings in a remediation table:

| # | File:Line | Category | Match | Suggested Fix |
|---|-----------|----------|-------|---------------|

Gate: User reviews all findings. Fix each one. Re-run audit. Proceed only when 0 hits confirmed.

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Phase 3: Generalization

Transform personal assumptions into universal defaults.

Language

• Replace: "in Korean" / "한국어로" / "Korean language" → "in the user's preferred language"
• Replace: "communicate in [specific language]" → "Communicate with the user in their preferred language"
• Keep: multilingual trigger keywords in the triggers: field (these aid discovery)

Role

• Replace: "radiology researcher" → "medical researcher" (if the skill is domain-general)
• Replace: "professor" / "fellow" → "researcher" or "user" (context-dependent)
• Keep: domain-specific terms that define the skill's scope (e.g., "diagnostic accuracy" is fine)

Paths

• Replace: hardcoded absolute paths → ${CLAUDE_SKILL_DIR} for bundled reference files
• Replace: ~/Documents/... → user-provided output directory
• Keep: relative paths within the skill directory structure

Environment

• Remove: assumptions about specific OS (macOS, Linux)
• Remove: assumptions about specific editors or IDEs
• Remove: references to personal infrastructure (agents, other personal skills)
• Keep: tool requirements listed in frontmatter tools: field

Interoperability

• Check: does the skill reference other skills by name (e.g., "route to analyze-stats")?
• If referenced skill exists in target package: keep the reference
• If referenced skill does NOT exist in target package: make it optional with fallback instructions

Output

Show a unified diff of all generalization changes for user review.

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Phase 4: License Compatibility Check

Verify all bundled files are compatible with the target package license.

Scan Process

For each file in the skill's references/ and scripts/ directories:

1. Check for license headers or declarations within the file
2. Check for LICENSE files in the same directory
3. If the file contains content from a known standard (reporting guidelines, clinical scores, etc.), identify the source and its license

Compatibility Matrix

Reference ${CLAUDE_SKILL_DIR}/references/license-compatibility-matrix.md for the full matrix.

Quick reference for MIT target:

Source License	Can Bundle?	Action
CC0 / Public Domain	Yes	No changes needed
CC BY 4.0 / 3.0	Yes	Add attribution header
MIT / BSD / Apache 2.0	Yes	Include license notice
CC BY-NC	No	Convert to URL reference
CC BY-NC-ND	No	Convert to URL reference
CC BY-SA	No	Copyleft risk -- convert to URL reference
GPL v2/v3	No	Mark as optional external dependency
Unknown / Proprietary	No	Assume incompatible -- remove or get permission

URL Manifest Pattern

For non-compatible content, convert from bundled file to a URL manifest:

## [Checklist Name]

This checklist is not bundled due to license restrictions ([License Type]).

**Official source**: [URL]
**How to use**: Download the checklist from the official source and place it in
`references/` before using this skill's reporting check feature.

Output

Present license audit table:

| File | Source | License | Compatible? | Action |
|------|--------|---------|------------|--------|

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Phase 5: Validate and Test

Structural Validation

1. YAML frontmatter: Parse and verify all required fields (name, description, tools)
2. File references: Every ${CLAUDE_SKILL_DIR}/... path resolves to an actual file
3. Script executability: Scripts in scripts/ have appropriate shebangs
4. Line count: SKILL.md should be under 500 lines for optimal loading
5. Description quality: Description should start with a verb and include trigger keywords

Final PII Re-check

Run audit_skill.sh one final time. Must return exit code 0.

Cross-Platform Adapter Review

Check whether the skill can run in common desktop-agent environments:

Platform	Check
Claude Code	No hardcoded dependency on private ~/.claude paths unless documented.
Codex	SKILL.md is self-contained and installable under ~/.agents/skills/.
Cursor	A short .cursor/rules/*.mdc adapter can point to the canonical SKILL.md.
Windows	Commands avoid Unix-only assumptions or provide PowerShell/Python alternatives.
macOS/Linux	Shell examples use portable paths where possible.

If the package is intended for a workshop or classroom, prepare direct-download ZIPs rather than asking users to navigate GitHub manually:

https://github.com/{owner}/{repo}/releases/latest/download/{package}-classroom-windows.zip
https://github.com/{owner}/{repo}/releases/latest/download/{package}-classroom-macos.zip

README Entry Draft

Generate a table row matching the target package's README format:

| **{skill-name}** | {One-sentence description of what the skill does.} |

User Testing

Instruct the user to:

1. Copy the cleaned skill to a test location: cp -r <cleaned_skill> ~/.claude/skills/<skill-name>
2. Restart Claude Code
3. Test the skill triggers by typing /<skill-name> or relevant trigger phrases
4. Verify all phases work end-to-end on a sample input

Gate: User confirms testing is complete.

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Phase 6: Package and Commit

Copy to Target Package

cp -r <cleaned_skill_path> <target_package>/skills/<skill-name>/

Update README

Apply the README entry drafted in Phase 5:

• Add row to the appropriate table (Available Now / Coming Soon)
• Update pipeline diagram if the skill adds a new stage
• Update skill count if mentioned in prose

Generate Commit Commands

Present the exact commands but do NOT auto-execute push:

cd <target_package>
git add skills/<skill-name>/
git add README.md
git diff --cached   # User reviews
git commit -m "Add <skill-name>: <one-line description>"

Gate: User reviews git diff --cached and explicitly approves the commit. Push is always manual.

Post-Publish

Remind the user to:

• Update any memory files tracking package status
• Add the skill to any marketplace listings if applicable
• Test installation from a clean clone: git clone <repo> && cp -r <repo>/skills/<skill-name> ~/.claude/skills/
• For classroom distribution, create or update GitHub Release ZIP assets and test direct download links.

Classroom Package Checklist (if applicable)

• Full skill set is installed once; lesson tasks use only 1-2 skills at a time
• Windows ZIP includes installers/install-windows.cmd
• macOS ZIP includes installers/install-macos.command
• README_FIRST.md explains unzip -> double-click -> restart -> test prompt
• Email announcement uses direct GitHub Release download links
• WSL is documented as an advanced option, not a default requirement
• First prompts avoid full end-to-end orchestration

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What This Skill Does NOT Do

• Never auto-executes git push -- push is always manual
• Never modifies the source skill in place -- works on a copy or the target directory
• Never makes judgment calls on competitive sensitivity -- always asks the user
• Never bundles content with incompatible licenses -- converts to URL references
• Never assumes a specific package license -- asks in Phase 0
• Never skips the PII audit -- zero tolerance is enforced at Phase 2 and Phase 5

Anti-Hallucination

• Never fabricate file paths, URLs, DOIs, or package names. Verify existence before recommending.
• Never invent journal metadata, impact factors, or submission policies without verification at the journal's website.
• If a tool, package, or resource does not exist or you are unsure, say so explicitly rather than guessing.