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dag-typesafe
Analyze a repository's type system and generate type-safe DAG execution pipelines with GraphSentry-style certificate verification. This skill should be used whe
Vue d’ensemble
Analyze a repository's type system and generate type-safe DAG execution pipelines with GraphSentry-style certificate verification. This skill should be used when building LLM-driven workflows that need deterministic type safety, when composing typed operations into validated execution graphs, or when adding contract-checked pipeline orchestration to any codebase. Supports Python (Pydantic) and TypeScript (Zod).
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dag-typesafe
Deterministic type safety via directed acyclic graphs for reasoning language models.
Analyze any repo's public API surface, extract a typed node registry, compose validated
execution DAGs from natural language, and compile them into native pipeline code with
GraphSentry-style (artifact, certificate) verification at every node boundary.
Category
Code Scaffolding & Templates
Core Concepts
Three layers compose into a single architecture:
-
Typed Node Registry — the repo's public functions/classes extracted as typed nodes with JSON Schema input/output contracts. The LLM selects from this registry; it never generates arbitrary code.
-
DAG Plan — a language-neutral execution graph where nodes reference registry entries and edges are schema-validated. No cycles, all inputs satisfied, all types compatible.
-
Certificates — each node emits an
(artifact, certificate)pair. Certificates are deterministic predicates evaluated from logged evidence. Failed certificates halt the pipeline with diagnostic context. Based on GraphSentry (Li et al., 2026).
Commands
Parse $ARGUMENTS to determine which command to run:
analyze
Extract a typed node registry from the current repository.
python3 ~/.claude/skills/dag-typesafe/scripts/analyze.py [--language python|typescript|auto] [--output dag-registry.json]
- Detect repo language(s) from file extensions and config files
- Run the appropriate extractor(s) from
extractors/ - Walk the AST for public API surface only (exported functions, public classes, API endpoints)
- Convert type annotations to JSON Schema
- Output
dag-registry.jsonat repo root
compose
Generate a DAG plan from natural language using the typed registry.
The compose phase is endpoint-agnostic — it works with any OpenAI-compatible API (OpenRouter, Groq, Subq Code, local models) or in-session via Claude Code.
In-session mode (default): Build a structured prompt from the registry and task description, then use the current Claude Code session to generate the DAG plan.
Headless mode: Set DAG_LLM_BASE_URL and DAG_LLM_API_KEY environment variables
to point at any OpenAI-compatible endpoint.
python3 ~/.claude/skills/dag-typesafe/scripts/compose.py "task description" --registry dag-registry.json [--output dag-plan.json] [--provider session|openai]
- Load registry from
dag-registry.json - Build structured prompt constraining LLM to node selection and parameterization
- Generate DAG plan conforming to
schemas/dag-plan.schema.json - Validate the plan (type compatibility, no cycles, all inputs satisfied)
- Generate certificate predicates for each node
- Output
dag-plan.json
compile
Compile a validated DAG plan into native executable code.
python3 ~/.claude/skills/dag-typesafe/scripts/compile.py dag-plan.json [--target python|typescript] [--output pipeline.py]
- Read validated
dag-plan.jsonanddag-registry.json - Topologically sort nodes
- Generate native pipeline code via inline code generation
- Wrap each node call in schema validation (Pydantic
.model_validate()/ Zod.parse()) - Inject certificate predicate evaluation at every node boundary
- Output executable pipeline file with zero runtime dependencies beyond standard validation libs
validate
Type-check an existing DAG plan against a registry.
python3 ~/.claude/skills/dag-typesafe/scripts/validate.py dag-plan.json --registry dag-registry.json
Checks: cycle detection, edge type compatibility, required input satisfaction, certificate predicate well-formedness, registry reference integrity.
registry
Inspect, filter, and query the typed node registry.
python3 ~/.claude/skills/dag-typesafe/scripts/registry.py [--filter "pattern"] [--show-schemas] [--stats]
Schemas
All schemas live in schemas/ as JSON Schema (draft 2020-12):
dag-plan.schema.json— DAG plan format (nodes, edges, metadata)registry.schema.json— typed node registry formatcertificate.schema.json— GraphSentry certificate format
Read these schemas before generating or validating any DAG artifacts.
Extractors
Language-specific type extractors in extractors/:
| Extractor | Source Types | Output |
|---|---|---|
python_extractor.py | Pydantic BaseModel, dataclass, TypedDict, function annotations | JSON Schema |
typescript_extractor.py | Zod schemas, interfaces, type aliases, function signatures | JSON Schema |
Each extractor walks the AST, identifies public API surface, and converts type definitions to JSON Schema for the registry. Internal/private symbols are excluded.
Code Generation
The compiler generates pipeline code inline (no external templates). Both Python and TypeScript targets produce complete executable files with:
- Certificate/CertificateError classes
- Topologically sorted node execution
- Per-node certificate emission and predicate evaluation
- Predicate expression sandboxing (AST-validated safe subset only)
LLM Provider Configuration
For headless/CI use, configure via environment variables:
export DAG_LLM_BASE_URL="https://openrouter.ai/api/v1" # or Groq, Subq, local
export DAG_LLM_API_KEY="your-key"
export DAG_LLM_MODEL="anthropic/claude-sonnet-4-20250514" # model identifier
When these are unset, compose mode generates a structured prompt for in-session use.
Research Basis
This skill synthesizes patterns from:
- GraphSentry (Li et al., 2026) — certificate-driven typed DAGs, contract-checked graph surgery
- ChopChop (Nagy et al., POPL 2026) — semantic constrained decoding via coinductive realizability
- PlanCompiler (prnvh, 2026) — LLM confined to typed node registry selection
- PlanAI (Provos, 2026) — Pydantic-typed DAG orchestration with automatic routing
- Agint (Chivukula et al., 2025) — type floors (text→data→spec→code) in agentic graph compilation
Full source list: references/research-sources.md
Métadonnées du fichier
name: dag-typesafe description: >- Analyze a repository's type system and generate type-safe DAG execution pipelines with GraphSentry-style certificate verification. This skill should be used when building LLM-driven workflows that need deterministic type safety, when composing typed operations into validated execution graphs, or when adding contract-checked pipeline orchestration to any codebase. Supports Python (Pydantic) and TypeScript (Zod). argument-hint: "[analyze|compose|compile|validate|registry] [args...]"
Voir le texte original
--- name: dag-typesafe description: >- Analyze a repository's type system and generate type-safe DAG execution pipelines with GraphSentry-style certificate verification. This skill should be used when building LLM-driven workflows that need deterministic type safety, when composing typed operations into validated execution graphs, or when adding contract-checked pipeline orchestration to any codebase. Supports Python (Pydantic) and TypeScript (Zod). argument-hint: "[analyze|compose|compile|validate|registry] [args...]" --- # dag-typesafe Deterministic type safety via directed acyclic graphs for reasoning language models. Analyze any repo's public API surface, extract a typed node registry, compose validated execution DAGs from natural language, and compile them into native pipeline code with GraphSentry-style `(artifact, certificate)` verification at every node boundary. ## Category Code Scaffolding & Templates ## Core Concepts Three layers compose into a single architecture: 1. **Typed Node Registry** — the repo's public functions/classes extracted as typed nodes with JSON Schema input/output contracts. The LLM selects from this registry; it never generates arbitrary code. 2. **DAG Plan** — a language-neutral execution graph where nodes reference registry entries and edges are schema-validated. No cycles, all inputs satisfied, all types compatible. 3. **Certificates** — each node emits an `(artifact, certificate)` pair. Certificates are deterministic predicates evaluated from logged evidence. Failed certificates halt the pipeline with diagnostic context. Based on GraphSentry (Li et al., 2026). ## Commands Parse `$ARGUMENTS` to determine which command to run: ### `analyze` Extract a typed node registry from the current repository. ```bash python3 ~/.claude/skills/dag-typesafe/scripts/analyze.py [--language python|typescript|auto] [--output dag-registry.json] ``` 1. Detect repo language(s) from file extensions and config files 2. Run the appropriate extractor(s) from `extractors/` 3. Walk the AST for public API surface only (exported functions, public classes, API endpoints) 4. Convert type annotations to JSON Schema 5. Output `dag-registry.json` at repo root ### `compose` Generate a DAG plan from natural language using the typed registry. The compose phase is **endpoint-agnostic** — it works with any OpenAI-compatible API (OpenRouter, Groq, Subq Code, local models) or in-session via Claude Code. **In-session mode (default):** Build a structured prompt from the registry and task description, then use the current Claude Code session to generate the DAG plan. **Headless mode:** Set `DAG_LLM_BASE_URL` and `DAG_LLM_API_KEY` environment variables to point at any OpenAI-compatible endpoint. ```bash python3 ~/.claude/skills/dag-typesafe/scripts/compose.py "task description" --registry dag-registry.json [--output dag-plan.json] [--provider session|openai] ``` 1. Load registry from `dag-registry.json` 2. Build structured prompt constraining LLM to node selection and parameterization 3. Generate DAG plan conforming to `schemas/dag-plan.schema.json` 4. Validate the plan (type compatibility, no cycles, all inputs satisfied) 5. Generate certificate predicates for each node 6. Output `dag-plan.json` ### `compile` Compile a validated DAG plan into native executable code. ```bash python3 ~/.claude/skills/dag-typesafe/scripts/compile.py dag-plan.json [--target python|typescript] [--output pipeline.py] ``` 1. Read validated `dag-plan.json` and `dag-registry.json` 2. Topologically sort nodes 3. Generate native pipeline code via inline code generation 4. Wrap each node call in schema validation (Pydantic `.model_validate()` / Zod `.parse()`) 5. Inject certificate predicate evaluation at every node boundary 6. Output executable pipeline file with zero runtime dependencies beyond standard validation libs ### `validate` Type-check an existing DAG plan against a registry. ```bash python3 ~/.claude/skills/dag-typesafe/scripts/validate.py dag-plan.json --registry dag-registry.json ``` Checks: cycle detection, edge type compatibility, required input satisfaction, certificate predicate well-formedness, registry reference integrity. ### `registry` Inspect, filter, and query the typed node registry. ```bash python3 ~/.claude/skills/dag-typesafe/scripts/registry.py [--filter "pattern"] [--show-schemas] [--stats] ``` ## Schemas All schemas live in `schemas/` as JSON Schema (draft 2020-12): - `dag-plan.schema.json` — DAG plan format (nodes, edges, metadata) - `registry.schema.json` — typed node registry format - `certificate.schema.json` — GraphSentry certificate format Read these schemas before generating or validating any DAG artifacts. ## Extractors Language-specific type extractors in `extractors/`: | Extractor | Source Types | Output | |-----------|-------------|--------| | `python_extractor.py` | Pydantic BaseModel, dataclass, TypedDict, function annotations | JSON Schema | | `typescript_extractor.py` | Zod schemas, interfaces, type aliases, function signatures | JSON Schema | Each extractor walks the AST, identifies public API surface, and converts type definitions to JSON Schema for the registry. Internal/private symbols are excluded. ## Code Generation The compiler generates pipeline code inline (no external templates). Both Python and TypeScript targets produce complete executable files with: - Certificate/CertificateError classes - Topologically sorted node execution - Per-node certificate emission and predicate evaluation - Predicate expression sandboxing (AST-validated safe subset only) ## LLM Provider Configuration For headless/CI use, configure via environment variables: ```bash export DAG_LLM_BASE_URL="https://openrouter.ai/api/v1" # or Groq, Subq, local export DAG_LLM_API_KEY="your-key" export DAG_LLM_MODEL="anthropic/claude-sonnet-4-20250514" # model identifier ``` When these are unset, compose mode generates a structured prompt for in-session use. ## Research Basis This skill synthesizes patterns from: - **GraphSentry** (Li et al., 2026) — certificate-driven typed DAGs, contract-checked graph surgery - **ChopChop** (Nagy et al., POPL 2026) — semantic constrained decoding via coinductive realizability - **PlanCompiler** (prnvh, 2026) — LLM confined to typed node registry selection - **PlanAI** (Provos, 2026) — Pydantic-typed DAG orchestration with automatic routing - **Agint** (Chivukula et al., 2025) — type floors (text→data→spec→code) in agentic graph compilation Full source list: `references/research-sources.md`
Examiner la source
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- Prérequis non confirmés. Consultez les frais d’agent, d’API et de services à la source.
- Licence
- MIT
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- Le prix n’est pas confirmé. Les liens existants vers les sources et l’installation restent disponibles.
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Source à réexaminer
La source a changé ou sa synchronisation a échoué. Vérifiez-la avant installation.
Réviser avant installation: Éviter l’installation automatique
Licence: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- No critical security issues identified. The skill only parses ASTs and generates code; it does not execute arbitrary repo code directly.
- The TypeScript extractor uses regex-based parsing, which may miss complex type constructs or produce incomplete schemas for advanced TypeScript patterns.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 41 GitHub stars
- Stars/forks activity: 41 stars, 4 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.
Commencer par une petite tâche
- 1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
- 2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
- 3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.
Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.
Source et conseils d’utilisation
Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.
- Dépôt source
- tdimino/claude-code-minoan
- Licence
- MIT
- Version
- 1.0.0
- Dernier push GitHub
- 6 sept. 2026
- Registre mis à jour
- 9 oct. 2026
- Chemin des instructions
- skills/core-development/dag-typesafe/SKILL.md @ 72da3657c7d7
Version déclarée dans le registre ; vérifiez les versions de la source.
Qualité
60/100
Prometteur
Confiance
53/100
Do not auto-install
Audit
69/100
Revue nécessaire
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- No critical security issues identified. The skill only parses ASTs and generates code; it does not execute arbitrary repo code directly.
- The TypeScript extractor uses regex-based parsing, which may miss complex type constructs or produce incomplete schemas for advanced TypeScript patterns.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 41 GitHub stars
- Stars/forks activity: 41 stars, 4 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Verified installs
- —
- Résultats
- —
Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.
Accès agent
L’API Registry fournit les signaux de décision, confiance, audit, cas d’usage et installation sans analyser l’interface.
Plus de détails
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}Pour le créateur
Source de la fiche
Indexé par Registry
Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.
- Créateur
- tdimino
- Indexé par
- Index communautaire OpenAgentSkill
L’attribution renvoie au dépôt public ou au profil du créateur. Les créateurs peuvent revendiquer la fiche pour mettre à jour les signaux de propriété.
Revendiquer ce skillRevendication du propriétaire
Revendiquer cette fiche de skill
Cette fiche Indexé par Registry est attribuée à tdimino, mais n’est pas encore marquée officielle. Revendiquez-la pour ajouter un signal de propriétaire vérifié et rendre les futures mises à jour de lancement, d’installation et d’audit plus fiables.
Kit de partage
Kit de backlinks créateur
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Affichez la fiche canonique, les signaux actuels de confiance et d’audit, ainsi que de vraies preuves Agent-Proven là où les développeurs évaluent le dépôt.
[](https://www.openagentskill.com/skills/tdimino-dag-typesafe?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/tdimino-dag-typesafe?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/tdimino-dag-typesafe/audit)
[](https://www.openagentskill.com/skills/tdimino-dag-typesafe?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)Signal de communauté
Indiquez si ce skill semble utile à votre workflow Agent. Les retours agrégés améliorent le classement au fil du temps.
