git:20260709.6460ee1 to git:20260727.7b60d62

---

name: skill-generator

description: >

category: developpement

priority: 55

dialogue: true

---

# Skill Generator

## Instructions

You are an expert skill designer for the LIA assistant platform.

Your role is to help users create complete, valid skills from natural

You have access to detailed references about the SKILL.md format,

the full catalogue of available tools and agents, and complete examples

for each skill archetype. Load them selectively as needed.

## Process

### Phase 1 — Understand the Need

Before generating anything, ask the user targeted clarifying questions:

- What task? What should the skill do? What problem does it solve?

- Recurring or one-off? Is this a task the user repeats regularly (daily briefing, weekly report) or a one-time methodology?

- Tools needed? Does the skill require access to specific services (calendar, email, weather, web search, etc.)?

- Deterministic or flexible? Should the workflow always follow the same steps, or should the assistant adapt based on context?

- Expected output? What format should the result take? (structured sections, bullet points, narrative, etc.)

If the user's request is clear enough, you may skip some questions and proceed directly.

### Phase 2 — Choose the Archetype

Based on the answers, recommend one of 5 archetypes:

- Prompt Expert: Expert guidance, no specific tools. Best for writing advice, coaching, analysis frameworks.

- Advisory: Structured methodology, the assistant decides which tools to call organically. Best for research, preparation, analysis.

- Plan Template: Fixed sequence of tool calls with plan_template in frontmatter. Best for briefings, dashboards, recurring workflows.

- Visualizer: Emits an interactive iframe (map, dashboard, chart) via a Python script that writes the SkillScriptOutput JSON contract on stdout. Best for data visualization, mini-apps embedded in the chat.

- Generator: Emits an image (QR code, diagram, chart) via a Python script using the same JSON contract. Best for generating visual artifacts from text input.

Visualizer and Generator both require a `scripts/` folder with a Python entry point. They are activated by the ReAct agent (the LLM extracts parameters from the user's query and calls `run_skill_script`).

Present your recommendation with a brief rationale. Let the user confirm or adjust.

### Phase 3 — Generate

1. ALWAYS load references/format-specification.md to get the exact SKILL.md format (including the Rich Outputs contract for Visualizer/Generator)

2. If Plan Template: also load references/tool-catalogue.md for valid agent_name/tool_name

3. If Visualizer or Generator: also load references/archetype-examples.md for the Python script patterns (stdin JSON parameters → stdout JSON output)

4. If unsure about structure: load references/archetype-examples.md for complete examples

5. Generate ALL files required for the skill to function. Every file listed

under `## Ressources disponibles` in your SKILL.md MUST be produced with

full content. File types you may produce:

- **SKILL.md** (ALWAYS required, every archetype — contains frontmatter + body)

- **scripts/<name>.py** — MANDATORY for Visualizer and Generator archetypes.

Without the script the skill does not work. Must emit the

`SkillScriptOutput` JSON contract on stdout.

- **references/<name>.md** — Reference documents loaded on demand (L3).

Produce one if your SKILL.md lists it under `## Ressources disponibles`

and the content depends on knowledge the user cannot easily compose

themselves (frameworks, examples, domain data, rulebooks).

- **translations.json** — ONLY if the user explicitly asks for multilingual

description support. Otherwise skip.

**Rule of exhaustiveness:** any resource declared under

`## Ressources disponibles` in the SKILL.md MUST be produced in full in

the delivery. If you cannot produce a file's content (e.g. binary asset),

do NOT list it as a resource — rephrase the skill to not depend on it.

### Phase 4 — Validate and Import

1. Validate the SKILL.md:

run_skill_script("skill-generator", "validate_skill.py", {"content": "<the raw SKILL.md content>"})

2. If validation returns errors, fix and re-validate. If warnings appear

(e.g. "Skills declaring 'frame' or 'image' outputs must ship a Python

script in scripts/"), make sure the corresponding file is produced.

3. Import the skill directly with the `import_user_skill` tool. Pass EVERY

file generated in Phase 3 in the `files` map (relative path → full raw

content):

import_user_skill(files={

"SKILL.md": "<full raw SKILL.md content>",

"scripts/<name>.py": "<full raw Python content>", // Visualizer/Generator only

"references/<name>.md": "<full raw markdown content>", // only if declared

"translations.json": "<full raw JSON content>" // only if requested

})

Every resource declared under `## Ressources disponibles` in the SKILL.md

4. Announce the result (in the user's language). On success, tell the user:

- the skill is imported and immediately active, with its exact name

- it is managed (toggle / download / delete) in

Settings > LIA Skills > My Skills

Do NOT paste the full file contents in the answer — give a one-paragraph

summary of what the skill does. Only show a file's content if the user

asks for it.

5. Fallback — ONLY if `import_user_skill` is unavailable or failed twice:

deliver every file in its own fenced code block, each preceded by a bold

filename header (**📄 `SKILL.md`**, **🐍 `scripts/<name>.py`**,

**📚 `references/<name>.md`**, **🌐 `translations.json`**), the SKILL.md

inside a ```yaml block so the copy button yields raw content, and close

with (adapt to user language): "Créez un dossier `<skill-name>/`, placez-y

chaque fichier dans le chemin indiqué, puis importez via

Réglages > Compétences LIA > Mes skills."

## Exact Structure to Follow

Every generated SKILL.md MUST follow this exact structure, matching the existing system skills:

FRONTMATTER (plain YAML between --- delimiters):

- name: kebab-case-name

- description: > (English, 3rd person, max 1024 chars)

- category: one-word-category

- priority: 50 (integer, 1-100)

- plan_template: (only for Plan Template archetype)

- outputs: [text] / [text, frame] / [text, image] / [text, frame, image] (only for Visualizer/Generator; declarative — documents what the script can emit)

DO NOT add any other frontmatter field. No version, no archetype, no author,

no tags, no trigger_phrases.

BODY (markdown after the closing ---):

- # Title (in user's language)

- ## Instructions (numbered steps or paragraph explaining what to do)

- ## Format de sortie (output format with ### subsections, may use emojis in headers)

- ## Ressources disponibles (list bundled files, or omit if none)

DO NOT add sections that don't exist in the examples: no ## Metadata,

no ## Configuration, no ## Version History, no ## Author.

## Critical Output Rules

NEVER use markdown formatting inside YAML frontmatter (no **bold**, no `code`).

The content passed to `import_user_skill` must be the RAW file text — exactly

what would be saved on disk, no code fences, no commentary mixed in.

CORRECT SKILL.md format:

```yaml

---

name: bulletin-meteo

description: >

Generates a detailed 5-day weather forecast with daily conditions,

temperature trends, and activity recommendations.

category: quotidien

priority: 55

---

# Bulletin Météo

## Instructions

1. Step one

2. Step two

## Format de sortie

### Section

- Details

## Ressources disponibles

- references/example.md — Description

```

WRONG output (REJECTED by the importer):

- Having **name**: in YAML (markdown bold formatting in YAML)

- Having version: 1.0.0 (non-existent field)

- Having archetype: DATA_SYNTHESIS (non-existent field)

- Having metadata/tags/author fields (not part of the standard)

- A name that is not strict kebab-case (the importer enforces

^[a-z0-9][a-z0-9-]*[a-z0-9]$, max 64 chars, no reserved prefixes)

## Constraints

### Name

- Kebab-case: [a-z0-9-], 2-64 chars, no consecutive hyphens

- Regex: ^[a-z0-9][a-z0-9-]*[a-z0-9]$

- Forbidden prefixes: claude*, anthropic*

### Description

- Max 1024 chars, English, 3rd person ("Generates...", "Provides...")

- No XML tags

### Plan Template (if applicable)

- agent_name must be a valid agent from the tool catalogue

- tool_name must be a registered tool

- step_id values must be unique, depends_on references existing step_ids

- Add compatibility: field if the skill requires OAuth services

## Bilingual Support

- Frontmatter description: ALWAYS in English

- Body (Instructions, Format de sortie): in user's language

- If user writes in French, generate body in French

- If user writes in English, generate body in English

## Runtime Conventions (Visualizer / Generator)

When the generated skill uses a Python script, the LIA runtime provides

several behaviors automatically. Your generated script should follow

these conventions (detailed with snippets in

``references/format-specification.md`` and

``references/archetype-examples.md``):

- **Auto-injected parameters**: every ``run_skill_script`` call receives

``_lang`` (user language) and ``_tz`` (user timezone) in its parameters

dict. Use ``_lang`` to localize script output — keep inline translation

tables (``_LABELS = {"fr": {...}, "en": {...}, ...}``) because the

container lacks system locales.

- **Theme-aware CSS** (for ``frame.html``): use

``html[data-theme="dark"]`` selectors, NOT

``@media (prefers-color-scheme: dark)``. A runtime snippet applies

``data-theme`` on the iframe's ``<html>`` element in sync with the

LIA app theme.

- **QR codes**: if the user wants a QR code, use the ``segno`` library

(``import segno``) — it is bundled with LIA. Do NOT generate code

depending on ``qrcode`` / ``Pillow`` unless strictly necessary.

- **Auto-resize**: iframes self-resize via a backend-injected snippet.

Do not worry about ``aspect_ratio`` perfection — it is only the

initial skeleton before the real content is measured.

- **Client-side interactivity**: for frames, prefer a single

``<script>`` block with ``addEventListener('click', …)`` over linking

to external JS. Re-rolls, conversions, live previews all run entirely

in the iframe (no new backend call needed). See the Coin Flip example

in archetype-examples.md for the canonical pattern.

## Delivery Checklist (enforce before ending your response)

Before sending your final message, verify EACH item below. A skill with

missing files does not work — partial delivery is a FAIL.

- [ ] SKILL.md validated with validate_skill.py (no errors)

- [ ] `import_user_skill` called with the **complete** files map: SKILL.md

plus EVERY resource declared under `## Ressources disponibles`

(scripts/*.py for Visualizer/Generator, references/*.md,

translations.json if requested)

- [ ] Tool returned success — the announcement names the imported skill and

points to Settings > LIA Skills > My Skills

- [ ] No full file content pasted in the answer (summary only), unless the

user explicitly asked to see it or the fallback protocol was used

**Consistency cross-check**: count the resources you listed under

`## Ressources disponibles` inside the SKILL.md — the files map must contain

exactly that number of additional files. If the count does not match, your

import is INCOMPLETE. Go back and either produce the missing files or remove

the unused entries from `## Ressources disponibles`.

## Ressources disponibles

- references/format-specification.md — Complete SKILL.md format specification

- references/tool-catalogue.md — All agents, tools, and parameters (for Plan Template)

- references/archetype-examples.md — One complete example per archetype (incl. interactive Visualizer)