How do I install the Distributed Processor skill?

Run npx skillmds add majiayu000/distributed-processor in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Distributed Processor safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Distributed Processor?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Distributed Processor free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Distributed Processor?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Distributed Processor by majiayu000

Name: Distributed Processor
Rating: 4.2 (5 reviews)
Author: majiayu000

Distributed Processor

Coordinate distributed processing with parallel execution, forked skills, and isolated work units. Use when you need distributed processing or isolated work units. Not for sequential tasks or single-threaded workflows.

majiayu000 1e5e337 2 files · 4.2 KB Updated Sep 9, 2026 567 repo stars

File contents

Distributed Data Processing

Coordinate distributed data processing using TaskList with forked skills.

Processing Architecture

Three-component system:

Coordinator uses TaskList to track all processing tasks
Region processors are forked skills with complete isolation
Results flow back to coordinator for aggregation

Processing Tasks

Parallel, independent execution:

process-region-a - Process data from Region A
- Forked skill processes in isolation
process-region-b - Process data from Region B
- Forked skill processes in isolation
process-region-c - Process data from Region C
- Forked skill processes in isolation
aggregate-results - Combine all processed outputs
- Wait for all region processors to complete
- Aggregate results into final dataset

Execution Workflow

Execute autonomously:

Create TaskList with all tasks
Use Skill tool with context: fork for each region processor
Monitor task completion
Aggregate results when all processors complete
Return aggregated dataset

Recognition test: Each region processor runs in complete isolation with no shared state.

Expected Output

Distributed Processing: COORDINATING
[task-id] process-region-a: IN_PROGRESS -> COMPLETE
[task-id] process-region-b: IN_PROGRESS -> COMPLETE
[task-id] process-region-c: IN_PROGRESS -> COMPLETE
[task-id] aggregate-results: BLOCKED -> IN_PROGRESS -> COMPLETE

=== AGGREGATED RESULTS ===
Region A: [records processed, summary]
Region B: [records processed, summary]
Region C: [records processed, summary]

Total: [combined statistics]

Validation Criteria

Parallel execution of regions
Results aggregation after completion

Binary check: "Proper distributed processing?" → Both criteria must pass.

Genetic Code

This component carries essential Seed System principles for context: fork isolation:

Delta Standard: Good Component = Expert Knowledge − What Claude Already Knows

Recognition Questions:

"Would Claude know this without being told?" → Delete (zero delta)
"Can this work standalone?" → Fix if no (non-self-sufficient)
"Did I read the actual file, or just see it in grep?" → Verify before claiming

---name: distributed-processordescription: Coordinate distributed processing with parallel execution, forked skills, and isolated work units. Use when you need distributed processing or isolated work units. Not for sequential tasks or single-threaded workflows.--- # Distributed Data Processing Coordinate distributed data processing using TaskList with forked skills. ## Processing Architecture **Three-component system:** - **Coordinator** uses TaskList to track all processing tasks - **Region processors** are forked skills with complete isolation - **Results flow back** to coordinator for aggregation ## Processing Tasks **Parallel, independent execution:** 1. **process-region-a** - Process data from Region A - Forked skill processes in isolation 2. **process-region-b** - Process data from Region B - Forked skill processes in isolation 3. **process-region-c** - Process data from Region C - Forked skill processes in isolation 4. **aggregate-results** - Combine all processed outputs - Wait for all region processors to complete - Aggregate results into final dataset ## Execution Workflow **Execute autonomously:** 1. **Create TaskList** with all tasks 2. **Use Skill tool** with context: fork for each region processor 3. **Monitor task completion** 4. **Aggregate results** when all processors complete 5. **Return aggregated dataset** **Recognition test:** Each region processor runs in complete isolation with no shared state. ## Expected Output ``` Distributed Processing: COORDINATING [task-id] process-region-a: IN_PROGRESS -> COMPLETE [task-id] process-region-b: IN_PROGRESS -> COMPLETE [task-id] process-region-c: IN_PROGRESS -> COMPLETE [task-id] aggregate-results: BLOCKED -> IN_PROGRESS -> COMPLETE === AGGREGATED RESULTS === Region A: [records processed, summary] Region B: [records processed, summary] Region C: [records processed, summary] Total: [combined statistics] ``` ## Validation Criteria - Parallel execution of regions - Results aggregation after completion **Binary check:** "Proper distributed processing?" → Both criteria must pass. --- <critical_constraint> MANDATORY: Wait for all processors to complete before aggregation MANDATORY: Use TaskList to track all distributed tasks MANDATORY: Ensure forked skills have complete isolation No exceptions. Parallel execution requires proper coordination. </critical_constraint> --- ## Genetic Code This component carries essential Seed System principles for context: fork isolation: <critical_constraint> MANDATORY: All components MUST be self-contained (zero .claude/rules dependency) MANDATORY: Achieve 80-95% autonomy (0-5 AskUserQuestion rounds per session) MANDATORY: Description MUST use What-When-Not format in third person MANDATORY: No component references another component by name in description MANDATORY: Progressive disclosure - references/ for detailed content MANDATORY: Use XML for control (mission_control, critical_constraint), Markdown for data No exceptions. Portability invariant must be maintained. </critical_constraint> **Delta Standard**: Good Component = Expert Knowledge − What Claude Already Knows **Recognition Questions**: - "Would Claude know this without being told?" → Delete (zero delta) - "Can this work standalone?" → Fix if no (non-self-sufficient) - "Did I read the actual file, or just see it in grep?" → Verify before claiming ---

Frequently asked questions