/team-debug
Debug issues using competing hypotheses with parallel investigation by multiple agents
$ npx -y skills add wshobson/agents --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/team-debug
Context preview
What this command does when you run it.
Debug issues using competing hypotheses with parallel investigation by multiple agents
Command definition
team-debug.mddescription: "Debug issues using competing hypotheses with parallel investigation by multiple agents"
argument-hint: "<error-description-or-file> [--hypotheses N] [--scope files|module|project]"
Team Debug
Debug complex issues using the Analysis of Competing Hypotheses (ACH) methodology. Multiple debugger agents investigate different hypotheses in parallel, gathering evidence to confirm or falsify each one.
Pre-flight Checks
1. Verify `CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1` is set 2. Parse `$ARGUMENTS`:
- `<error-description-or-file>`: description of the bug, error message, or path to a file exhibiting the issue
- `--hypotheses N`: number of hypotheses to generate (default: 3)
- `--scope`: investigation scope — `files` (specific files), `module` (module/package), `project` (entire project)
Phase 1: Initial Triage
1. Analyze the error description or file:
- If file path: read the file, look for obvious issues, collect error context
- If error description: search the codebase for related code, error messages, stack traces
2. Identify the symptom clearly: what is failing, when, and how 3. Gather initial context: recent git changes, related tests, configuration
Phase 2: Hypothesis Generation
Generate N hypotheses about the root cause, covering different failure mode categories:
1. **Logic Error** — Incorrect algorithm, wrong condition, off-by-one, missing edge case 2. **Data Issue** — Invalid input, type mismatch, null/undefined, encoding problem 3. **State Problem** — Race condition, stale cache, incorrect initialization, mutation bug 4. **Integration Failure** — API contract violation, version mismatch, configuration error 5. **Resource Issue** — Memory leak, connection exhaustion, timeout, disk space 6. **Environment** — Missing dependency, wrong version, platform-specific behavior
Present hypotheses to user: "Generated {N} hypotheses. Spawning investigators..."
Phase 3: Investigation
1. Use `TeamCreate` tool to create the team with `team_name: "debug-{timestamp}"` and `description` 2. For each hypothesis, use `Agent` tool to spawn a teammate:
- `name`: `investigator-{n}` (e.g., "investigator-1")
- `subagent_type`: "agent-teams:team-debugger"
- `prompt`: Include the hypothesis, investigation scope, and relevant context
3. Use `TaskCreate` for each investigator's task:
- Subject: "Investigate hypothesis: {hypothesis summary}"
- Description: Full hypothesis statement, scope boundaries, evidence criteria
Phase 4: Evidence Collection
1. Monitor TaskList for completion 2. As investigators complete, collect their evidence reports 3. Track: "{completed}/{total} investigations complete"
Phase 5: Arbitration
1. Compare findings across all investigators:
- Which hypotheses were confirmed (high confidence)?
- Which were falsified (contradicting evidence)?
- Which are inconclusive (insufficient evidence)?
2. Rank confirmed hypotheses by:
- Confidence level (High > Medium > Low)
- Strength of causal chain
- Amount of supporting evidence
- Absence of contradicting evidence
3. Present root cause analysis:
## Debug Report: {error description}
### Root Cause (Most Likely)
**Hypothesis**: {description}
**Confidence**: {High/Medium/Low}
**Evidence**: {summary with file:line citations}
**Causal Chain**: {step-by-step from cause to symptom}
### Recommended Fix
{specific fix with code changes}
### Other Hypotheses
- {hypothesis 2}: {status} — {brief evidence summary}
- {hypothesis 3}: {status} — {brief evidence summary}Phase 6: Cleanup
1. Send `shutdown_request` to all investigators 2. Call `TeamDelete` to remove team resources
Read more
description: "Debug issues using competing hypotheses with parallel investigation by multiple agents" argument-hint: "<error-description-or-file> [--hypotheses N] [--scope files|module|project]"
Team Debug
Debug complex issues using the Analysis of Competing Hypotheses (ACH) methodology. Multiple debugger agents investigate different hypotheses in parallel, gathering evidence to confirm or falsify each one.
Pre-flight Checks
1. Verify `CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1` is set 2. Parse `$ARGUMENTS`:
- `<error-description-or-file>`: description of the bug, error message, or path to a file exhibiting the issue
- `--hypotheses N`: number of hypotheses to generate (default: 3)
- `--scope`: investigation scope — `files` (specific files), `module` (module/package), `project` (entire project)
Phase 1: Initial Triage
1. Analyze the error description or file:
- If file path: read the file, look for obvious issues, collect error context
- If error description: search the codebase for related code, error messages, stack traces
2. Identify the symptom clearly: what is failing, when, and how 3. Gather initial context: recent git changes, related tests, configuration
Phase 2: Hypothesis Generation
Generate N hypotheses about the root cause, covering different failure mode categories:
1. **Logic Error** — Incorrect algorithm, wrong condition, off-by-one, missing edge case 2. **Data Issue** — Invalid input, type mismatch, null/undefined, encoding problem 3. **State Problem** — Race condition, stale cache, incorrect initialization, mutation bug 4. **Integration Failure** — API contract violation, version mismatch, configuration error 5. **Resource Issue** — Memory leak, connection exhaustion, timeout, disk space 6. **Environment** — Missing dependency, wrong version, platform-specific behavior
Present hypotheses to user: "Generated {N} hypotheses. Spawning investigators..."
Phase 3: Investigation
1. Use `TeamCreate` tool to create the team with `team_name: "debug-{timestamp}"` and `description` 2. For each hypothesis, use `Agent` tool to spawn a teammate:
- `name`: `investigator-{n}` (e.g., "investigator-1")
- `subagent_type`: "agent-teams:team-debugger"
- `prompt`: Include the hypothesis, investigation scope, and relevant context
3. Use `TaskCreate` for each investigator's task:
- Subject: "Investigate hypothesis: {hypothesis summary}"
- Description: Full hypothesis statement, scope boundaries, evidence criteria
Phase 4: Evidence Collection
1. Monitor TaskList for completion 2. As investigators complete, collect their evidence reports 3. Track: "{completed}/{total} investigations complete"
Phase 5: Arbitration
1. Compare findings across all investigators:
- Which hypotheses were confirmed (high confidence)?
- Which were falsified (contradicting evidence)?
- Which are inconclusive (insufficient evidence)?
2. Rank confirmed hypotheses by:
- Confidence level (High > Medium > Low)
- Strength of causal chain
- Amount of supporting evidence
- Absence of contradicting evidence
3. Present root cause analysis:
## Debug Report: {error description}
### Root Cause (Most Likely)
**Hypothesis**: {description}
**Confidence**: {High/Medium/Low}
**Evidence**: {summary with file:line citations}
**Causal Chain**: {step-by-step from cause to symptom}
### Recommended Fix
{specific fix with code changes}
### Other Hypotheses
- {hypothesis 2}: {status} — {brief evidence summary}
- {hypothesis 3}: {status} — {brief evidence summary}Phase 6: Cleanup
1. Send `shutdown_request` to all investigators 2. Call `TeamDelete` to remove team resources
Production-ready agentic workflow building blocks: 94 plugins, 203 agents, 175 skills, 109 commands — built for Claude Code and consumed natively by OpenAI Codex CLI, Cursor, OpenCode, Gemini CLI, and GitHub Copilot from a single Markdown source.
Repo: wshobson/agents
Other commands on wshobson-agents.
- /accessibility-audit
You are an accessibility expert specializing in WCAG compliance, inclusive design, and assistive technology compatibility. Conduct comprehensive audits, identify barriers, provide remediation guidance, and ensure digital products are accessible to all users.
Open command - /improve-agent
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
Open command - /multi-agent-optimize
The Multi-Agent Optimization Tool is an advanced AI-driven framework designed to holistically improve system performance through intelligent, coordinated agent-based optimization. Leveraging cutting-edge AI orchestration techniques, this tool provides a comprehensive approach to
Open command - /team-delegate
Task delegation dashboard for managing team workload, assignments, and rebalancing
Open command - /team-feature
Develop features in parallel with multiple agents using file ownership boundaries and dependency management
Open command - /team-review
Launch a multi-reviewer parallel code review with specialized review dimensions
Open command

