- name
- debate-research
- description
- Multi-perspective structured debate for complex topics. Spawn parallel subagents with opposing stances, cross-inject arguments for rebuttal, then synthesize via neutral judge into a consensus report with recommendations and scenario matrix. Use when: (1) user asks for deep comparison, pros/cons, or X vs Y analysis, (2) user asks for multi-angle research on a controversial or complex topic, (3) user explicitly requests debate, dialectical analysis, or adversarial research. NOT for: simple factual lookups, single-perspective deep research (use academic-deep-research), or quick opinion questions.
Debate Research
Input Parameters
Collect from user before starting. Only topic is required; all others have defaults.
| Param | Required | Default | Description |
|---|---|---|---|
topic | yes | — | Debate subject |
roles | no | Proponent + Opponent | 2-4 role objects: {name, stance, model?}. Default: Proponent (argue for) and Opponent (argue against). Model inherits from global. |
goal | no | inferred | What question to answer |
audience | no | "self" | Who reads the report: self / team / public |
decision_type | no | "personal-choice" | personal-choice / team-standardization / market-analysis |
evidence_round | no | "auto" | false / true / auto (enable when topic is fact-dense) |
confirm_plan | no | true | Show plan and wait for user OK before execution |
model | no | inherit | Global subagent model; role-level override takes priority |
output_path | no | null | File path for report; null = return in conversation |
Implicit parameter: language — inferred from the user's topic/conversation language. All subagent prompts output in this language.
Example User Prompt
Claude Code vs OpenCode (gpt-5.4, claude-4.6-sonnet)
Execution Pipeline
Phase 0 — Pre-flight
Step 0a: Model reachability check
Collect all unique models (global + per-role + judge). For each unique model, probe via sessions_spawn with a minimal one-sentence task (e.g. "Reply OK") and model: <target>. Do NOT use curl or external HTTP — all models route through OpenClaw's provider config.
If any probe fails:
- If user explicitly specified the failed model → abort, report failure, suggest alternatives
- If model was default-assigned → warn user, fall back to session default model, continue
Step 0b: Plan presentation (if confirm_plan: true)
Present to user:
- Topic
- Role × model assignment table
- Evidence round: on/off/auto (with rationale if auto)
- Estimated subagent call count
- Goal / audience / decision_type interpretation
[STOP — wait for user confirmation]
If confirm_plan: false, skip directly to Phase 1.
Phase 1 — Stance Investigation (parallel)
Spawn one subagent per role, all in parallel.
Each agent receives a prompt built from:
- Role name + stance
- Topic
- web_search: enabled
Required output format per agent:
Core arguments (3-5):
- [argument] | confidence: 0.0-1.0 | source: [official-docs/community-feedback/personal-blog/academic-paper]
Opponent weaknesses (2-3)
Predicted counter-attacks (1-2)Use sessions_spawn + sessions_yield to wait for all completions.
Error handling:
- Agent timeout → mark output
[INCOMPLETE], continue pipeline
Phase 2 — Cross Rebuttal (parallel)
Spawn one subagent per role, all in parallel.
Each agent receives:
- Its original stance
- All other roles' Phase 1 output (cross-injected)
- web_search: disabled
Required output format per agent:
Rebuttals (one per opponent argument):
- [rebuttal] | confidence: 0.0-1.0
Weakest premise attack:
- Identify opponent's single weakest assumption and challenge it ← Socratic element
New attacks (2):
- [attack]Word limit: 300 × number_of_opponents words per agent.
Error handling:
- Agent timeout → mark
[INCOMPLETE], continue
Phase 2.5 — Evidence Audit (optional)
Triggered when evidence_round: true, or when auto and topic involves measurable claims. Auto-enable heuristic: topic contains performance benchmarks, cost comparisons, security assessments, market data, or quantitative metrics. When in doubt with auto, skip (false positive costs more than false negative).
Spawn 1 subagent as "evidence auditor":
- Input: all Phase 1 + Phase 2 output
- web_search: disabled
- Task: extract every factual claim, tag each as:
[official-docs] [community-feedback] [personal-blog] [no-source] [exaggerated]
- Output: concise fact checklist
Phase 3 — Neutral Judgment
Spawn 1 subagent as neutral judge:
- Input: Phase 1 + Phase 2 + Phase 2.5 (if available)
- web_search: disabled
- Weigh arguments by confidence scores AND source quality tags
Required output structure:
- Strong arguments per side
- Exaggerated claims per side
- Shared limitations (problems neither option solves)
- Core disagreements (value-level, not just factual)
- Consensus points
- Recommendation — explicit directional advice, adapted to
decision_type - Open Questions — unresolved unknowns that could change the conclusion
- Scenario selection matrix (table: scenario × recommendation × rationale)
- One-sentence summary
Phase 4 — Report Assembly
Orchestrator (main conversation) assembles all outputs into Markdown:
# [topic]: Debate Research Report
> **Date**: YYYY-MM-DD
> **Method**: Multi-agent structured debate (debate-research skill)
> **Roles**: [role1 (model)] | [role2 (model)] | ...
> **Audience**: [audience] | **Decision type**: [decision_type]
> **Completion**: [success | degraded-success | aborted]
## Core Arguments by Side
[Phase 1 output, organized by role]
## Cross Rebuttals
[Phase 2 output, organized by role]
## Evidence Audit
[Phase 2.5 output, or "Not requested"]
## Neutral Judgment
[Phase 3 sections 1-5]
## Recommendation
[Phase 3 section 6]
## Open Questions
[Phase 3 section 7]
## Scenario Matrix
[Phase 3 section 8]
> **One-line summary**: [Phase 3 section 9]If output_path specified → write file. Otherwise → return in conversation.
Completion States
| State | Condition | Behavior |
|---|---|---|
success | All phases completed normally | Full report |
degraded-success | 1+ agents timed out or returned [INCOMPLETE] | Report with degradation note |
aborted | Model pre-check failed / user cancelled plan | No report; return error summary |
Prompt Templates
See references/prompts.md for the exact prompt templates used in each phase. Orchestrator builds prompts dynamically from parameters + these templates.