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Parallel Investigation

CommunityPopular
rohitg00
parallel-investigation

Coordinates parallel investigation threads to simultaneously explore multiple hypotheses or root causes across different system areas. Use when debugging production incidents, slow API performance, multi-system integration failures, or complex bugs where the root cause is unclear and multiple plausible theories exist; when serial troubleshooting is too slow; or when multiple investigators can divide root-cause analysis work. Provides structured phases for problem decomposition, thread assignment, sync points with Continue/Pivot/Converge decisions, and final report synthesis.

Overview

Publisherrohitg00
Repositoryskillkit
Skill nameparallel-investigation
Stars
1.5K
Forks
147
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Parallel Investigation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rohitg00/skillkit.git /tmp/skillkit
mkdir -p .claude/skills
cp -r /tmp/skillkit/packages/core/src/methodology/packs/collaboration/parallel-investigation .claude/skills/parallel-investigation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Parallel Investigation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Parallel Investigation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Parallel Investigation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Parallel Investigation

Coordinate parallel investigation threads to explore multiple hypotheses simultaneously. Most effective for production incidents, performance regressions, or integration failures where the root cause is unclear.

Core Principle

When uncertain, explore multiple paths in parallel. Converge when evidence points to an answer.

Parallel investigation reduces time-to-solution by eliminating serial bottlenecks.

Investigation Structure

Phase 1: Problem Decomposition

Break the problem into independent investigation threads:

Problem: API responses are slow

Investigation Threads:
├── Thread A: Database performance
│   └── Check slow queries, indexes, connection pool
├── Thread B: Application code
│   └── Profile endpoint handlers, check for N+1
├── Thread C: Infrastructure
│   └── Check CPU, memory, network latency
└── Thread D: External services
    └── Check third-party API response times

Each thread should be independent (no blocking dependencies), focused (clear scope), and time-boxed.

Phase 2: Thread Assignment

Assign threads with clear ownership:

markdown
## Thread A: Database Performance
**Investigator:** [Name/Agent A]
**Duration:** 30 minutes
**Scope:**
- Query execution times
- Index utilization
- Connection pool metrics
**Report Format:** Summary + evidence

Phase 3: Parallel Execution

Each thread follows this pattern:

  1. Gather evidence specific to thread scope
  2. Document findings as you go
  3. Identify if thread is a lead or dead end
  4. Prepare summary for sync point

Thread Log Template:

markdown
## Thread: [Name]
**Start:** [Time]

### Findings
- [Timestamp] [Finding]

### Evidence
- [Log/Metric/Screenshot]

### Preliminary Conclusion
[What this thread suggests about the problem]

Phase 4: Sync Points

Regular convergence to share findings:

Sync Point Agenda:
1. Each thread report (2 min each)
2. Discussion & correlation (5 min)
3. Decision: Continue, Pivot, or Converge (3 min)

Sync Point Decisions:

  • Continue: Threads are progressing, maintain parallel execution
  • Pivot: Redirect threads based on new evidence
  • Converge: One thread found the answer, others join to validate

Phase 5: Convergence

When a thread identifies the likely cause:

  1. Validate — Other threads verify the finding
  2. Deep dive — Focused investigation on identified cause
  3. Document — Compile findings from all threads

Coordination Patterns

Hub and Spoke: One coordinator assigns threads, tracks progress, calls sync points, and makes convergence decisions. Best when one person has the most context.

Peer Network: Equal investigators post findings to a shared channel and self-organize convergence when a pattern emerges. Best when investigators have similar expertise.

Communication Protocol

During Investigation

[Thread A] [Status] Starting query analysis
[Thread B] [Finding] No N+1 patterns in user endpoint
[Thread A] [Finding] Slow query: SELECT * FROM orders WHERE...
[Thread C] [Dead End] CPU and memory within normal
[Thread A] [Hot Lead] Missing index on orders.user_id

At Sync Point

markdown
## Thread A Summary

**Status:** Hot Lead
**Key Finding:** Missing index on orders.user_id
**Evidence:** Query taking 3.2s, explain shows full table scan
**Recommendation:** Likely root cause — suggest converge

Decision Framework

Thread StatusAction
All exploringContinue parallel
One hot leadValidate lead, others support
Multiple leadsPrioritize by evidence strength
All dead endsReframe problem, new threads
Confirmed causeConverge, begin fix

Time Management

A typical two-hour investigation:

0:00  Problem decomposition & thread assignment
0:15  Parallel investigation begins
0:45  Sync point #1 → Continue/Pivot/Converge decision
1:30  Sync point #2 (if continuing)
1:35  Final convergence & documentation

Adjust sync point cadence based on incident severity — every 20 minutes for critical outages, every 45 minutes for lower-urgency investigations.

Documentation

Final Report Structure

markdown
# Investigation: [Problem]

## Summary
[Brief description and resolution]

## Threads Explored

### Thread A: [Area]
- Investigator: [Name]
- Findings: [Summary]
- Outcome: [Lead / Dead End / Root Cause]

## Root Cause
[Detailed explanation of what was found]

## Evidence
- [Evidence 1]
- [Evidence 2]

## Resolution
[What was done to fix]

## Lessons Learned
- [Learning 1]

Integration with Other Skills

  • debugging/root-cause-analysis: Each thread follows RCA principles
  • debugging/hypothesis-testing: Threads test specific hypotheses
  • handoff-protocols: When passing a thread to another person

Frequently asked questions

What does the Parallel Investigation AI skill do?

Coordinates parallel investigation threads to simultaneously explore multiple hypotheses or root causes across different system areas. Use when debugging production incidents, slow API performance, multi-system integration failures, or complex bugs where the root cause is unclear and multiple plausible theories exist; when serial troubleshooting is too slow; or when multiple investigators can divide root-cause analysis work. Provides structured phases for problem decomposition, thread assignment, sync points with Continue/Pivot/Converge decisions, and final report synthesis.

Why use Parallel Investigation on TypingMind?

Because you install it once and use it with any model. Parallel Investigation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Parallel Investigation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/skillkit/tree/main/packages/core/src/methodology/packs/collaboration/parallel-investigation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Parallel Investigation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Parallel Investigation?

As many as you like. As long as a model supports skills, you can use Parallel Investigation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Parallel Investigation AI skill free?

Yes. It is published on GitHub by rohitg00 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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