Lawve Prep

Lawve-Prep: Package a Skill for Public Distribution on Lawve

Turn a skill in any state — packaged, half-packaged, a bare SKILL.md, or an idea in a sentence — into something that can stand in a public legal catalogue: correctly structured, honestly described, properly attributed, safe to review, and accompanied by the exact form entries the submission page asks for.

The pipeline has six steps. The order is deliberate: the gate comes first because no packaging effort should be spent on a skill that will be declined, and provenance comes second because the licence decision shapes everything downstream.

Intake → Gate → Provenance → Transform → Validate → Package.

Step 0 — Intake

Identify what the user handed over:

• A .skill or .zip archive — unzip it. Both are zip files.
• A folder or bare SKILL.md — work from it directly. A bare SKILL.md becomes a folder named after its frontmatter name.
• A plugin — a bundle with .claude-plugin/, a plugin-level CLAUDE.md, or .mcp.json contains more than one thing. Lawve takes one skill per submission. Split it: each skill becomes its own package, plugin-level instructions get folded into the skill that needs them (see transforms), and cross-references between the halves are updated or cut cleanly.
• A raw idea — gate the idea first (Step 1); do not build a skill you would then decline. If it passes, build it with the skill-creator skill where available, otherwise draft a SKILL.md directly following Anthropic's skill-authoring practices (frontmatter name + pushy description; body instructions; bundled resources only where they earn their place). Then continue through the pipeline like any other input.

Read the whole skill before judging it — SKILL.md and every bundled file. Half the problems live in the bundled files.

Step 1 — Gate

Read resources/gate.md and apply it. Three outcomes:

• Proceed — the skill serves legal work in some form. The bar is broad: practice, litigation, drafting, legal education, legal research, law-office operations, document production for legal audiences, and meta-skills for legal AI all qualify.
• Offer a legal adaptation — the skill is general-purpose but a legal layer would earn its place in the catalogue (a text-to-audio skill gains citation and quotation handling; a formatting skill gains brief-and-memo structures). Propose the superset — everything it does now, plus the legal layer — and get the user's yes before building it.
• Decline — no plausible legal use even with adaptation, or a safety, licensing, or professional-responsibility bar that adaptation cannot cure. Decline in plain terms: what the bar is, and what (if anything) would change the answer. Never quietly package something the gate should have stopped.

Step 2 — Provenance and licence

Establish whose work this is before touching the files.

• The user's original work → the user picks the licence; default Apache-2.0 (MIT also fine). Both are on Lawve's accepted list.
• A derivative of someone else's skill → match the upstream licence where it permits derivatives (Apache-2.0, MIT, BSD, and CC-BY all do). Get attribution right in three places: frontmatter (derived_from), an attribution section in the SKILL.md body, and the NOTICE file, which itemizes what is upstream and what is added and disclaims endorsement.
• Upstream licence forbids or is absent — "All Rights Reserved," "No License," or a proprietary notice → stop. The skill cannot be published without permission. Say so.
• A placeholder copyright line — "Contributors," a scaffolding default, a company name that doesn't fit — is a question, not an answer. Ask the user who wrote it rather than assuming either way. Scaffolding tools routinely stamp wrong attributions on original work, and original authors routinely forget to replace them.

Step 3 — Transform

Read resources/transforms.md and apply every transform that fits. In brief: fix the structure (folder name, resources/ directory, LICENSE and NOTICE inside the folder); rewrite the frontmatter (description under 1,024 characters with honest triggers and a host-requirements sentence where needed); fold in any plugin-level logic the skill depends on; replace host-specific tool names with host-neutral instructions; convert hard external dependencies into a graceful ladder; repair orphaned files and broken links; add the limitations, jurisdiction-honesty, and bundled-resources sections; and clean up code.

The one meta-rule: the description on the form and the description in the frontmatter must tell the same truth as the skill's body. Every time a transform changes what the skill does — a new format, a renamed dependency, an added fallback — check whether the description still describes it.

Step 4 — Validate

Where the host can run Python, run the bundled validator:

python scripts/validate.py <skill-folder>

It checks structure, frontmatter, description length, link resolution, orphaned files, leftover host-specific tokens, and the presence of LICENSE, NOTICE, and a limitations section. Fix everything it reports and run it again; a package ships only on a clean pass.

Where the host cannot run Python, perform the same checks by hand — the checklist at the end of resources/requirements.md mirrors the script exactly.

Two checks the script cannot do, so do them yourself every time:

1. Read the final SKILL.md top to bottom as a stranger. Does it run correctly with nothing but what is inside the folder? Anything it assumes — a connector, a companion skill, a plugin file, a directory layout — must be either bundled, laddered, or disclosed.
2. Check the catalogue for duplicates. Use the Lawve connector's search where connected; otherwise web-search the lawve.ai catalogue; otherwise say plainly that the duplicate check could not be run. A near-duplicate is not automatically fatal — but the user decides that with the information, not without it.

Step 5 — Package and deliver

• Zip the folder so the archive contains the named directory at its root: zip -r <name>.zip <name>/. Deliver as .zip — browsers and Finder handle it predictably, and Lawve accepts it.
• Produce the form entries block: skill name; description (exact text, with character count); jurisdictions; category recommendation with one line of reasoning; language; visibility; licence. Flag explicitly which fields differ from the form's defaults — as of this writing the licence default is AGPL 3.0 and jurisdictions default to All, so most submissions need the licence changed and some need jurisdictions narrowed.
• Report what changed, briefly: the substantive adaptations first (what was folded in, laddered, or added), the mechanical fixes second. The user should be able to see at a glance what is different about the published version and why.

Bundled resources

• resources/requirements.md — the codified Lawve submission requirements: form fields and limits, directory conventions, attestations, review notes, and the manual validation checklist. Read at the start of any packaging run.
• resources/gate.md — the accept / adapt / decline criteria: the law-relatedness test, safety and code screens, professional-responsibility flags, licensing bars, duplicates, and the quality floor. Read in Step 1.
• resources/transforms.md — the standard compliance transforms with the failure each one prevents. Read in Step 3.
• scripts/validate.py — the programmatic validator. Python 3, standard library only. Run in Step 4 where the host allows.

Limitations and risks

This skill packages other skills for public distribution. It is not legal advice, and its gate is an editorial judgment, not a legal clearance.

The requirements are a snapshot. Lawve's form fields, limits, licence list, and review criteria are recorded in resources/requirements.md as of the version date and will drift. Where the live form disagrees with the reference file, the live form wins — and the reference file should be updated.

The licence step depends on honest provenance. The skill asks who wrote the input and takes the answer it is given. It cannot detect misattributed authorship, and a package built on a wrong answer misattributes in three places at once.

The gate can be wrong in both directions. A declined skill may have a legal use the gate did not see; an accepted one may have a problem review catches later. Lawve's own review is the real backstop; the gate exists so the user doesn't spend effort on obvious rejections.

Packaging improves presentation, not substance. A weak skill emerges from this pipeline structured, attributed, and disclosed — and still weak. The quality floor in the gate is a floor, not an endorsement.

The duplicate check degrades. With no catalogue access and no web search, it does not run, and the skill says so rather than pretending it did.

The bundled validator uses only the Python standard library, makes no network calls, spawns no subprocesses, and writes nothing — it only reads the folder it is pointed at.