Story generation pipeline skill

v1.0.4

Story generation pipeline skill. Supports multi-episode continuous generation, graph management, AI quality check + human confirmation dual control mechanism...

⭐ 2· 536·6 current·6 all-time

by@hexidyg

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Purpose & Capability

Name/description match the code: generator, reviewer, graph manager, and state persistence are present. Minor mismatch: SKILL.md and some textual descriptions mention 'call remote API' for graph query/storage, but the included graph_manager.py implements only local JSON file storage under data/graphs. Functionality requested (graph queries, episode generation, AI review) is coherent with the stated purpose.

Instruction Scope

SKILL.md instructs LLM-driven prompts and local state/graph file usage which the code follows. However there are inconsistent control rules: the SKILL.md earlier states AI retries 'max 3 times' for review, while pipeline.py and process_ai_review allow unlimited AI retry attempts (and a top-line note says human review has no max). Also SKILL.md implies graph operations may call a remote Graph API; the code's graph_manager only reads/writes local JSON. These discrepancies mean the runtime behavior may differ from what documentation promises.

✓

Install Mechanism

No install spec or external downloads are present — the skill is instruction+bundled Python code that operates on local files. No network-based install URLs or third-party package pulls were found in the bundle. Risk from installation is low, but the skill will write/read files in its data/ directory when executed.

✓

Credentials

The skill declares no required environment variables or credentials and the code does not access secrets or external credentials. It only uses local filesystem paths (data/pipeline_state.json and data/graphs). No disproportionate credential requests detected.

✓

Persistence & Privilege

always:false (not force-included) and the skill does not modify other skills or system settings. It persists state and graphs to data/ within the skill bundle path, which is normal for this kind of tool. No elevated platform privileges are requested.

What to consider before installing

This skill appears to be a self-contained, local story-generation pipeline that stores state and graphs under data/. Before installing or running it, consider the following: - The documentation and code disagree in two places: (1) SKILL.md mentions graph API calls but the code uses local JSON files; (2) SKILL.md says AI retry is capped at 3, but pipeline.py allows unlimited retries. Decide which behavior you expect and inspect/modify the code to enforce desired limits or change storage behavior. - The skill will create and modify files under data/ (pipeline_state.json and data/graphs/*.json). If you run it in an environment with sensitive files, ensure the working directory is isolated and not containing secrets. - No network endpoints or credentials are required by the included code, which reduces remote exfiltration risk. However, the skill bundles runnable Python code — review the files for any modifications you’d want (e.g., add retry caps, explicit user prompts, or logging controls) before enabling autonomous invocation. - Source is unknown. If you plan to use this in production or with sensitive inputs, request provenance from the author or run it in a sandbox first. If you want strict behavior (e.g., limit AI retries, or use a remote graph service), update the code to make that explicit and documented.

Like a lobster shell, security has layers — review code before you run it.

latestvk97aazv7zm84ws9v0gz365ka7582c4q8

536downloads

2stars

5versions

Updated 1mo ago

v1.0.4

MIT-0

Story Generation Pipeline Skill

Features

This skill implements a complete story generation pipeline:

Continuous Episode Generation - Automatically generates next episode based on previous content
Graph Management - Storage and querying of character, scene, and hook relationships
Dual Confirmation Control - AI quality check followed by human confirmation
State Persistence - Supports pause, resume, and multiple parallel stories

Core Workflow

┌──────────────┐    ┌──────────────┐    ┌──────────────┐    ┌──────────────────┐
│ Generate Ep N│ -> │ AI Review    │ -> │ Graph Storage│ -> │ Wait Human Confirm│
└──────────────┘    └──────────────┘    └──────────────┘    └──────────────────┘
       │                   │                   │                      │
       ▼                   ▼                   ▼                      ▼
  Query Graph         Pass/Retry        Store Relations        Continue/Modify/End

Usage

Start New Pipeline

User: Start a new story pipeline, theme: Chinese girl comeback, target episodes: 10

Continue Pipeline

User: Continue story pipeline [pipeline_id]

User Confirmation Actions

User: Approved, continue to next episode
User: Modify: [specific feedback]
User: Pause
User: End

Dual Confirmation Control Mechanism

Layer 1: AI Quality Check

After each episode is generated, AI automatically checks:

Check Item	Description
Plot Coherence	Natural connection with previous episode
Character Consistency	Character behavior matches established traits
Hook Handling	Reasonable addition/closure of hooks
Pacing Control	Appropriate plot progression speed
Emotional Curve	Reasonable emotional ups and downs

Scoring Standard: 0-10 points, below 7 triggers retry (max 3 times)

Layer 2: Human Confirmation

After AI review passes, display preview and wait for user confirmation:

📋 Episode N Preview
━━━━━━━━━━━━━━━━━━━━━━━━
【Plot Summary】
...

✅ AI Score: 8.5/10
✅ Hook Status: 1 new, 1 closed

Please select:
1️⃣ Approved, continue to next episode
2️⃣ Needs modification (please specify)
3️⃣ Pause
4️⃣ End
━━━━━━━━━━━━━━━━━━━━━━━━

Graph Management

Graph Query

Before generating episode N, query episode N-1's graph:

graph_manager.query_graph(pipeline_id, episode=N-1)

Returns:

Complete content of previous episode
Character list and their statuses
Unclosed hooks
Key scenes
Relationship network

Graph Storage

After user confirmation, store current episode's complete content:

graph_manager.save_graph(pipeline_id, episode=N, content)

Stores: Complete generation result of the episode (not split into elements)

State Management

State File: `data/pipeline_state.json`

{
  "pipelines": {
    "pipeline_2026-03-05-001": {
      "theme": "Chinese girl comeback",
      "target_episodes": 10,
      "current_episode": 3,
      "status": "waiting_user_confirm",
      "created_at": "2026-03-05T15:00:00",
      "updated_at": "2026-03-05T15:30:00",
      "ai_review": {
        "score": 8.5,
        "checks": {...}
      },
      "last_output": {
        "episode": 3,
        "summary": "...",
        "content": "Complete content..."
      }
    }
  }
}

Status Types

Status	Description
`generating`	Currently generating
`ai_reviewing`	AI review in progress
`waiting_user_confirm`	Waiting for human confirmation
`paused`	Paused
`completed`	Completed
`error`	Error state

Script Description

pipeline.py - Main Control Loop

Initialize pipeline
Coordinate modules
Handle user commands
Manage loop state

ai_reviewer.py - AI Quality Check

Execute quality scoring
Generate review report
Determine pass/fail

episode_generator.py - Episode Generation

Generate new episode based on graph context
Handle hook continuation and closure
Handle retry logic

graph_manager.py - Graph Management

Graph query (call remote API)
Graph storage (call remote API)
Local cache management

API Description

Graph API

Query API:

{
  "action": "query",
  "pipeline_id": "pipeline_2026-03-05-001",
  "episode": 2
}

Storage API:

{
  "action": "save",
  "pipeline_id": "pipeline_2026-03-05-001",
  "episode": 3,
  "content": "Complete generation content for episode 3..."
}

Episode Generation Logic

First Episode Generation

Based on user-provided theme and goals, generate:

Main character settings
Initial scenes
Core conflict
Open hooks

Subsequent Episode Generation

Based on graph query results:

Read previous episode content and unclosed hooks
Continue main plot line
Handle hooks (continue/close/add)
Advance character growth arc
Adjust emotional curve

Finale Generation

When target episodes reached or user requests end:

Close all remaining hooks
Complete character growth arcs
Generate conclusive ending

Important Notes

Retry Mechanism - Max 3 retries when AI review fails
Pause/Resume - Can resume via pipeline_id after pause
Multiple Pipelines - Supports running multiple different-themed pipelines simultaneously
Graph Consistency - Ensure correct character and hook relationships
Hook Management - Track creation and closure status of each hook

Complete Workflow

Step 1: Create Pipeline

User: Start a new pipeline, theme: Cultivation boy, target 20 episodes

AI: OK, creating pipeline pipeline_20260305160000
    Theme: Cultivation boy
    Target: 20 episodes
    Style: Realistic cinematic
    
    Status: Initialized, ready to generate episode 1

Step 2: Generate Episode

AI calls start_generation(pipeline_id) to get generation prompt, then generates episode content based on the prompt.

Step 3: Submit AI Review

AI calls submit_episode(pipeline_id, episode, content) to submit generated content, then executes AI review.

Step 4: Process Review Result

AI calls process_ai_review(pipeline_id, episode, ai_result, content) to process review results.

If review fails (score < 7), automatically retry (max 3 times).

Step 5: Wait User Confirmation

After review passes, display preview and wait for user confirmation:

📋 Episode 1 Preview
━━━━━━━━━━━━━━━━━━━━━━━━
【Cultivation Journey Begins】

Young Li Yun discovers a mysterious jade pendant in the mountains,
从此踏上修仙之路...

✅ AI Score: 9.0/10
✅ New Hook: H-001 Origin of mysterious jade pendant

━━━━━━━━━━━━━━━━━━━━━━━━

Please select:
1️⃣ Approved, continue to next episode
2️⃣ Needs modification (please specify)
3️⃣ Pause
4️⃣ End

Step 6: Process User Confirmation

After user confirms, AI calls user_confirm(pipeline_id, action, note):

approve: Store graph, prepare next episode
modify: Regenerate based on feedback
pause: Pause pipeline
end: End pipeline

Step 7: Loop Generation

Repeat steps 2-6 until target episodes reached or user ends.

API Reference

Create Pipeline

create_pipeline(theme: str, target_episodes: int, style: str = "realistic cinematic")
# Returns: {"success": True, "pipeline_id": "...", "message": "..."}

Start Generation

start_generation(pipeline_id: str)
# Returns: {"success": True, "episode": N, "prompt": "generation prompt"}

Submit Episode

submit_episode(pipeline_id: str, episode: int, content: str)
# Returns: {"success": True, "review_prompt": "review prompt"}

Process AI Review

process_ai_review(pipeline_id: str, episode: int, ai_result: str, content: str)
# Returns: {"success": True, "passed": True/False, ...}

User Confirm

user_confirm(pipeline_id: str, action: str, note: str = None)
# action: "approve" / "modify" / "pause" / "end"
# Returns: {"success": True, "status": "...", ...}

Get Status

get_status(pipeline_id: str)
# Returns: Current pipeline status info

List Pipelines

list_pipelines()
# Returns: List of all pipelines

Resume Pipeline

resume_pipeline(pipeline_id: str)
# Returns: Resume result

Example Dialogue

User: Start a new pipeline, theme: Cultivation boy, target 20 episodes

AI: OK, creating pipeline pipeline_20260305160000
    Generating episode 1...
    
    📋 Episode 1 Preview
    ━━━━━━━━━━━━━━━━━━
    【Cultivation Journey Begins】
    Young Li Yun discovers a mysterious jade pendant in the mountains...
    
    ✅ AI Score: 9.0/10
    ✅ New Hook: H-001 Origin of mysterious jade pendant
    
    Please confirm: Approve/Modify/Pause/End
    
User: Approved

AI: Storing graph...
    Starting generation of episode 2...

Important Notes

Retry Mechanism: Max 3 retries when AI review fails
State Persistence: All states saved in data/pipeline_state.json
Graph Storage: Stored via remote API, requires network connection
Pause/Resume: Can resume via resume_pipeline after pause
Multiple Pipelines: Supports running multiple different-themed pipelines simultaneously

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Story generation pipeline skill

Story Generation Pipeline Skill

Features

Core Workflow

Usage

Start New Pipeline

Continue Pipeline

User Confirmation Actions

Dual Confirmation Control Mechanism

Layer 1: AI Quality Check

Layer 2: Human Confirmation

Graph Management

Graph Query

Graph Storage

State Management

State File: data/pipeline_state.json

Status Types

Script Description

pipeline.py - Main Control Loop

ai_reviewer.py - AI Quality Check

episode_generator.py - Episode Generation

graph_manager.py - Graph Management

API Description

Graph API

Episode Generation Logic

First Episode Generation

Subsequent Episode Generation

Finale Generation

Important Notes

Complete Workflow

Step 1: Create Pipeline

Step 2: Generate Episode

Step 3: Submit AI Review

Step 4: Process Review Result

Step 5: Wait User Confirmation

Step 6: Process User Confirmation

Step 7: Loop Generation

API Reference

Create Pipeline

Start Generation

Submit Episode

Process AI Review

User Confirm

Get Status

List Pipelines

Resume Pipeline

Example Dialogue

Important Notes

Comments

State File: `data/pipeline_state.json`