Notre avis
Génère des données synthétiques pour la suite Forward Impact à partir d'un fichier DSL, produisant des définitions de frameworks, des documents organisationnels, des données d'activité et du contenu de base de connaissances.
Points forts
- Crée des données synthétiques réalistes sur plusieurs types de sortie (HTML, YAML, JSON, Markdown)
- Prend en charge la génération de prose via LLM pour un contenu en langage naturel
- Offre des modes de mise en cache et de simulation pour une itération et un test rapides
- Permet de générer un seul type de contenu avec l'option --only
Limites
- Nécessite la compréhension de la syntaxe DSL personnalisée
- La génération LLM nécessite une configuration de token et d'URL de base externe
- La sortie est liée à la structure de la suite Forward Impact
Utilisez-le lorsque vous avez besoin de données synthétiques pour les tests, le développement ou le remplissage d'un nouvel environnement Forward Impact.
Évitez-le lorsque vous avez besoin de données prêtes pour la production ou lorsque le DSL est trop rigide pour vos besoins personnalisés.
Analyse de sécurité
PrudenceThe skill instructs running npx fit-universe, which fetches and executes code from npm. This introduces supply-chain risk if the package is malicious. It also relies on LLM credentials from environment, which could be leaked if the tool behaves unexpectedly. However, the intended purpose is legitimate synthetic data generation.
- •Instructs use of npx fit-universe, downloading and executing remote code; package trustworthiness unknown.
- •Uses LLM_TOKEN from environment for LLM calls, which may expose credentials if tool is compromised.
Exemples
Run `npx fit-universe` to generate synthetic data with no LLM prose.Run `npx fit-universe --cached` to generate synthetic data using cached LLM prose.Run `npx fit-universe --only=pathway --cached` to generate only pathway framework files.name: fit-universe description: > Synthetic data generation CLI. Generates framework definitions, organizational documents, activity data, and personal knowledge base content from a universe DSL file. Use when generating example data, testing with synthetic datasets, or working with the universe DSL.
fit-universe CLI
Generate synthetic data for the entire Forward Impact suite from a single DSL file. The CLI orchestrates parsing, entity generation, optional LLM prose, and rendering into multiple output formats.
When to Use
- Generating example data for development or testing
- Creating synthetic pathway frameworks for new installations
- Producing organizational documents, activity records, and KB content
- Testing pipeline changes end-to-end
- Writing or editing universe DSL files
CLI Reference
npx fit-universe # Structural generation only (no LLM)
npx fit-universe --cached # Use cached prose (fast, repeatable)
npx fit-universe --generate # Generate prose via LLM (requires LLM_TOKEN)
npx fit-universe --cached --strict # Fail on cache miss
npx fit-universe --load # Load raw docs to Supabase Storage
npx fit-universe --only=pathway # Render only one content type
npx fit-universe --dry-run # Show what would be written
npx fit-universe --universe=path # Custom universe file
Content Types
Use --only=<type> to generate a single content type:
| Type | Output Directory | Contents |
| ---------- | ------------------------- | --------------------------------- |
| html | examples/organizational | Articles, guides, FAQs, courses |
| pathway | examples/pathway | YAML framework files |
| raw | examples/activity | Roster, GitHub events, evidence |
| markdown | examples/personal | Briefings, notes, KB content |
Prose Modes
| Mode | Flag | Description |
| ---------- | -------------- | ---------------------------------------- |
| no-prose | (default) | Structural only, no LLM calls |
| cached | --cached | Read from .prose-cache.json |
| generate | --generate | Call LLM, write to cache |
Universe DSL
Universe files define a complete synthetic environment. The default file is at
libraries/libuniverse/data/default.dsl.
Top-Level Blocks
universe Name {
domain "example.dev"
industry "technology"
seed 42
org hq { ... }
department engineering { ... }
team backend { ... }
people { ... }
project alpha { ... }
snapshots { ... }
scenario launch_push { ... }
framework { ... }
content guide_html { ... }
content basecamp_markdown { ... }
}
Key Blocks
org / department / team — Organizational hierarchy with headcounts, managers, and repo assignments.
people — Count, name theme, level distribution, discipline distribution.
project — Cross-team initiatives with timelines and prose topics.
snapshots — GetDX snapshot generation (quarterly intervals).
scenario — Time-bounded effects on teams (commit volume, DX driver trajectories, evidence generation).
framework — Full pathway framework: levels, capabilities with skills, behaviours, disciplines with skill tiers, tracks, drivers, and stages.
content — Output content blocks specifying article/blog/FAQ counts, persona configurations, and briefing counts.
Data Resolution
The production universe DSL lives at libraries/libuniverse/data/universe.dsl.
The default test universe is libraries/libuniverse/data/default.dsl. Use
--universe=path to specify a custom file.
All generated output writes to examples/ at the monorepo root.
Environment
Generation requires LLM_TOKEN and LLM_BASE_URL when using --generate
mode. These are always available in the standard environment (see AGENTS.md).
npx fit-universe --generate # Uses LLM_TOKEN from environment
Verification
After generation, the CLI runs cross-content validation automatically and reports pass/fail for each check. Validate the generated pathway data separately:
npx fit-map validate --data=examples/pathway
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