How do I install the License Compat skill?

Run npx skillmds@latest add cyrillemat/license-compat in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is License Compat safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with License Compat?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is License Compat free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published License Compat?

CyrilLeMat (@cyrillemat) published this skill. Their other Agent Skills are listed on their SkillMD profile.

License Compat by cyrillemat — Agent Skill

Name: License Compat
Author: CyrilLeMat

License Compat

Frozen, deterministic decision (no LLM): maps project_license, dependency_license, linking, distributing, modified_dependency to one of ok_internal_use, must_offer_source, project_must_be_gpl, compatible, incompatible, review_required. Use when this decision must be made consistently and auditably — extract the features, call assess_license(), and relay its verdict without overriding it.

CyrilLeMat Updated Sep 17, 2026

File contents

assess_license — skill (tempered by temper-skills)

You are an assistant.

The decision is frozen. Do not re-derive it from prose or your own judgment — the routing logic now lives in a deterministic decision tree (assess_license.assess_license, zero LLM calls, reviewed and version-controlled). Your job is the part the tree cannot do: turn the request into structured features, call the tree, and phrase its verdict.

How to answer

Extract these structured features from the request:
- project_license
- dependency_license
- linking
- distributing
- modified_dependency

Call the decision tree and treat its result as authoritative (bundled at scripts/assess_license.py):

from scripts.assess_license import assess_license
verdict = assess_license({"project_license": project_license, "dependency_license": dependency_license, "linking": linking, "distributing": distributing, "modified_dependency": modified_dependency})

Relay verdict to the user. Do not override it. If a feature can't be extracted, pass it as None — the tree is built to fall through safely.

Gray zones to surface

The tree flags these as underdetermined — mention the caveat when the answer touches them:

(n2) network-use scope is fact-specific — confirm deployment model
(n6) LGPL static-linking obligations are satisfiable (provide object files) — counsel can clear it

Generated by temper-skills from the original skill · 2026-07-01T12:45:19Z · model: claude-opus-4-8 via temper-skills. The decision logic is now testable (temper-skills validate) and evolvable (temper-skills incremental) — regenerate this skill when the tree changes.

---name: license-compatdescription: Frozen, deterministic decision (no LLM): maps project_license, dependency_license, linking, distributing, modified_dependency to one of ok_internal_use, must_offer_source, project_must_be_gpl, compatible, incompatible, review_required. Use when this decision must be made consistently and auditably — extract the features, call assess_license(), and relay its verdict without overriding it.---# assess_license — skill (tempered by temper-skills)You are an assistant.**The decision is frozen.** Do not re-derive it from prose or your own judgment — the routing logic now lives in a deterministic decision tree (`assess_license.assess_license`, zero LLM calls, reviewed and version-controlled). Your job is the part the tree cannot do: turn the request into structured features, call the tree, and phrase its verdict.## How to answer1. Extract these structured features from the request: - `project_license` - `dependency_license` - `linking` - `distributing` - `modified_dependency`2. Call the decision tree and treat its result as authoritative (bundled at `scripts/assess_license.py`): ```python from scripts.assess_license import assess_license verdict = assess_license({"project_license": project_license, "dependency_license": dependency_license, "linking": linking, "distributing": distributing, "modified_dependency": modified_dependency}) ```3. Relay `verdict` to the user. **Do not override it.** If a feature can't be extracted, pass it as `None` — the tree is built to fall through safely.## Gray zones to surfaceThe tree flags these as underdetermined — mention the caveat when the answer touches them:- (n2) network-use scope is fact-specific — confirm deployment model- (n6) LGPL static-linking obligations are satisfiable (provide object files) — counsel can clear it---Generated by temper-skills from the original skill · 2026-07-01T12:45:19Z · model: claude-opus-4-8 via temper-skills. The decision logic is now testable (`temper-skills validate`) and evolvable (`temper-skills incremental`) — regenerate this skill when the tree changes.

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