story-pipeline

name	story-pipeline
description	Story generation pipeline skill. Supports multi-episode continuous generation, graph management, AI quality check + human confirmation dual control mechanism. Automatically manages relationships between characters, scenes, and hooks.

Cleanup Review

python scripts/_purge_cache.py is an optional cleanup helper for the Story Generation Pipeline Skill skill. It may recursively remove /tmp/_skill_run/*, /tmp/_skill_run/.cache. Show the exact paths first, explain whether they are skill-private temporary paths or user state, and run the cleanup only after the user confirms.

Story Generation Pipeline Skill

Features

This skill implements a complete story generation pipeline:

1. Continuous Episode Generation - Automatically generates next episode based on previous content
2. Graph Management - Storage and querying of character, scene, and hook relationships
3. Dual Confirmation Control - AI quality check followed by human confirmation
4. 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" 
      
    
  

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:

1. Read previous episode content and unclosed hooks
2. Continue main plot line
3. Handle hooks (continue/close/add)
4. Advance character growth arc
5. 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

1. Retry Mechanism - Max 3 retries when AI review fails
2. Pause/Resume - Can resume via pipeline_id after pause
3. Multiple Pipelines - Supports running multiple different-themed pipelines simultaneously
4. Graph Consistency - Ensure correct character and hook relationships
5. 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

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