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dag-typesafe

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Price unconfirmed★ 41 GitHub starsRegistry updated · Sep 10, 2026agent-skill

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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.

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.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.

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.json and dag-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 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:

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

View source ↗

File metadata

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...]"

View original text

---
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`

Review the source

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Source needs review

The tracked source changed or could not be synchronized. Review the current source before installing.

Review before install: Avoid automatic install

License: 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
README/SKILL.md completeness: Public metadata needs stronger README/SKILL.md context
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: tdimino/claude-code-minoan
License: MIT
Version: 1.0.0
Last GitHub push: Sep 6, 2026
Registry updated: Sep 10, 2026
Instruction path: skills/core-development/dag-typesafe/SKILL.md @ 72da3657c7d7

Version reported in registry metadata; check source releases before relying on it.

Quality

60/100

Promising

Trust

51/100

Do not auto-install

Audit

69/100

Needs review

Open full audit View eval report

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
README/SKILL.md completeness: Public metadata needs stronger README/SKILL.md context
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Outcomes: —

Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.

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More details

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dag-typesafe

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Price unconfirmed★ 41 GitHub starsRegistry updated · Sep 10, 2026agent-skill

Overview

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dag-typesafe

Deterministic type safety via directed acyclic graphs for reasoning language models.

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.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.

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.json and dag-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 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:

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

View source ↗

File metadata

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...]"

View original text

---
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`

Review the source

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Review before install: Avoid automatic install

License: 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
README/SKILL.md completeness: Public metadata needs stronger README/SKILL.md context
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: tdimino/claude-code-minoan
License: MIT
Version: 1.0.0
Last GitHub push: Sep 6, 2026
Registry updated: Sep 10, 2026
Instruction path: skills/core-development/dag-typesafe/SKILL.md @ 72da3657c7d7

Version reported in registry metadata; check source releases before relying on it.

Quality

60/100

Promising

Trust

51/100

Do not auto-install

Audit

69/100

Needs review

Open full audit View eval report

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
README/SKILL.md completeness: Public metadata needs stronger README/SKILL.md context
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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Outcomes: —

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    "expected_agent_output": {
      "selected_skill": "tdimino-dag-typesafe (dag-typesafe)",
      "install_command": "",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "tdimino-dag-typesafe",
      "task": "Use dag-typesafe in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/tdimino-dag-typesafe",
    "api": "https://www.openagentskill.com/api/agent/skills/tdimino-dag-typesafe",
    "audit": "https://www.openagentskill.com/skills/tdimino-dag-typesafe/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=tdimino-dag-typesafe&task=Use%20dag-typesafe%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20dag-typesafe%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20dag-typesafe%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/tdimino-dag-typesafe/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/tdimino-dag-typesafe"
  }
}

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Creator: tdimino
Source: tdimino/claude-code-minoan
Indexed by: OpenAgentSkill community index

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/tdimino-dag-typesafe?metric=listed&label=Listed)](https://www.openagentskill.com/skills/tdimino-dag-typesafe?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
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[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/tdimino-dag-typesafe?metric=audit&label=Audit)](https://www.openagentskill.com/skills/tdimino-dag-typesafe/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/tdimino-dag-typesafe?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/tdimino-dag-typesafe?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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Overview

dag-typesafe

Category

Core Concepts

Commands

analyze

compose

compile

validate

registry

Schemas

Extractors

Code Generation

LLM Provider Configuration

Research Basis

Review the source

Price & running costs

Start with one small task

Source & usage notes

Agent access

Related skills

For the creator

Registry indexed

Claim this skill listing

Add the evidence badges to your README

Community signal

Overview

dag-typesafe

Category

Core Concepts

Commands

analyze

compose

compile

validate

registry

Schemas

Extractors

Code Generation

LLM Provider Configuration

Research Basis

Review the source

Price & running costs

Start with one small task

Source & usage notes

Agent access

Related skills

For the creator

Registry indexed

Claim this skill listing

Add the evidence badges to your README

Community signal

`analyze`

`compose`

`compile`

`validate`

`registry`

`analyze`

`compose`

`compile`

`validate`

`registry`