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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

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価格未確認★ 41 GitHub スター登録情報の更新日 · 2026年10月9日agent-skill

概要

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:

  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.

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.

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.

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.

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/:

ExtractorSource TypesOutput
python_extractor.pyPydantic BaseModel, dataclass, TypedDict, function annotationsJSON Schema
typescript_extractor.pyZod schemas, interfaces, type aliases, function signaturesJSON 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

ファイルのメタデータ
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...]"
元のテキストを表示
---
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`

ソースを確認

価格と実行コスト

Skill の入手
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実行
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ライセンス
MIT
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無料で入手できても実行が無料とは限りません。価格は安全評価ではありません。 価格情報を送る →

ソースの再確認が必要

ソースが変更されたか同期に失敗しました。インストール前に確認してください。

インストール前にレビュー: 自動インストールを避ける

ライセンス: 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
完全な監査を開く

ツール一覧はメタデータであり、互換性のテスト結果ではありません。プロンプトは提案です。

小さなタスクから始める

  1. 1ソースを読み、入力、出力、依存関係、権限を確認します。
  2. 2Agent に計画を求め、設定と費用を承認してから隔離環境でテストします。
  3. 3出力と変更ファイルを確認し、実行した結果だけを報告します。再現用にソースの版を保存します。

依存関係、API キー、外部サービスの料金をソースで確認してください。公開リポジトリでも全サービスが無料とは限りません。

出典と利用上の注意

登録済み

メタデータと審査情報は参考です。人気、ソースの発見、実行成功は別の事実です。

ソースリポジトリ
tdimino/claude-code-minoan
ライセンス
MIT
バージョン
1.0.0
最終 GitHub プッシュ
2026年9月6日
登録情報の更新日
2026年10月9日

登録されたバージョンです。ソースのリリース情報を確認してください。

品質

60/100

有望

信頼

53/100

Do not auto-install

監査

69/100

要レビュー

  • 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
—
成果
—

コピーはインストールではありません。件数は成功報告に基づき、品質全体を保証しません。

Agent 接続

Registry API 経由で判断、信頼、監査、ユースケース、インストールのシグナルを提供し、UI をスクレイピングせずに Agent が順位付けできます。

詳細情報
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      "installSafety": "standard package or runtime install path",
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      "Safety: 25/100 Avoid automatic install",
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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"
  }
}

クリエイター向け

掲載元

Registry により登録

申請可能

この掲載は公開ソースから登録されており、メンテナー申請が承認されるまで公式として表示されません。

作成者
tdimino
インデックス作成者
OpenAgentSkill コミュニティインデックス

帰属は公開リポジトリまたは作成者プロフィールにリンクされています。作成者は掲載を申請して所有権シグナルを更新できます。

このスキルを申請

所有者の申請

このスキル掲載を申請

この Registry により登録 掲載は tdimino に帰属していますが、まだ公式として表示されていません。申請すると、確認済み所有者シグナルが追加され、今後の公開、インストール、監査更新の信頼性が高まります。

共有キット

クリエイター被リンクキット

README にエビデンスバッジを追加

開発者がリポジトリを評価する場所で、正規掲載、現在の信頼・監査シグナル、実際の Agent-Proven エビデンスを表示します。

[![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)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/tdimino-dag-typesafe?metric=trust&label=Trust)](https://www.openagentskill.com/skills/tdimino-dag-typesafe?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![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)

コミュニティシグナル

このスキルが Agent ワークフローに役立つかを共有してください。集約されたフィードバックがランキングを改善します。