Tencent SkillHub · AI

Roundtable Adaptive

Adaptive multi-model AI roundtable. Runs up to 4 AI models (configurable) in 2 debate rounds with cross-critique and formal consensus scoring. Requires a con...

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Adaptive multi-model AI roundtable. Runs up to 4 AI models (configurable) in 2 debate rounds with cross-critique and formal consensus scoring. Requires a con...

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: README.md, SKILL.md, examples/debate-ai-developer-2026-02-23.md, examples/priorityA-checklist.md, panels.json, prompts/final-synthesis.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 2.9.1

Provenance

Publisher: JimmyClanker
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 27 sections Open source page

Roundtable v2 — Adaptive Multi-Model Orchestrator

Trigger: roundtable [--mode] [prompt] from any channel your agent monitors. Output: Posted to your configured output channel (set ROUNDTABLE_OUTPUT_CHANNEL in your OpenClaw config, or results are posted back to the triggering channel). Panel agents: Persistent sessions (mode="session", thread=true) — stay alive in the Discord thread for follow-up questions. Meta-panel analysts and synthesis agent are one-shot (mode="run"). The orchestrator = COORDINATOR ONLY. Uses your default model unless overridden in panels.json. Never argues a position, never joins the panel. Core principle: the Meta-Panel (4 premium models) designs the optimal WORKFLOW for the task — parallel debate, sequential pipeline, or hybrid — then the right agents execute it.

Configuration

Before using, set your output channel in panels.json (or the triggering channel is used): { "output": { "channel": "discord", "target": "YOUR_CHANNEL_ID_HERE" } } If using Discord threads (optional — creates one thread per roundtable for clean organization): { "output": { "channel": "discord", "target": "YOUR_CHANNEL_ID_HERE", "useThreads": true } } Without this config, results are posted directly to the channel where the command was issued.

Cost transparency

ComponentCost per full runClaude Opus (OAuth)FreeGPT-5.3 Codex (OAuth)FreeGemini 3.1 Pro (Blockrun)~$0.05Grok 4 (Blockrun)~$0.08Total (full panel)~$0.13–$0.50Degraded mode (Claude only)Free --quick flag halves cost (1 round only).

Setup

Minimum (degraded mode — free): Configure anthropic provider in openclaw.json (OAuth or API key) Optionally add openai-codex for GPT-5.3 Codex Done — Grok/Gemini slots fall back to Claude Sonnet Full panel (adds Grok 4 + Gemini 3.1 Pro via Blockrun): Install Blockrun: openclaw plugins install @blockrun/clawrouter then openclaw gateway restart Fund the Blockrun wallet with USDC on Base (~$5-10). Address shown during install. Full panel costs ~$0.13–$0.50/run; Claude and GPT slots remain free via OAuth. Results are saved to {workspace}/memory/roundtables/YYYY-MM-DD-slug.json (created automatically).

Optional: auto-trigger a dedicated channel

You can configure a Discord channel as a roundtable-only channel in your AGENTS.md: Any message in channel [YOUR_CHANNEL_ID] → treat as a roundtable topic automatically. No prefix needed. Message → auto-detect mode → create thread → spawn orchestrator. This is entirely optional — the explicit roundtable command works from any channel.

Explicit trigger (any channel)

roundtable [prompt] — auto-detect mode, full flow roundtable --debate [prompt] — force parallel debate mode roundtable --build [prompt] — force build/coding mode roundtable --redteam [prompt] — force adversarial mode roundtable --vote [prompt] — force decision mode roundtable --quick [prompt] — skip meta-panel, use default panel for mode, 1 round only roundtable --panel model1,model2,model3 [prompt] — manual panel override, skip meta-panel roundtable --validate [prompt] — add Round 3 agent validation of synthesis roundtable --no-search [prompt] — skip web search (use only for purely theoretical/abstract topics)

Step -1: Create a Thread (FIRST ACTION)

Before anything else, create a thread in your configured channel and save the thread ID.

-1a) Dedup check (REQUIRED)

Avoid double-spawn if the same topic is triggered twice. Normalize topic string: lowercase trim collapse multiple spaces remove trailing punctuation List recent threads in the target channel: message(action='thread-list', channel='discord', channelId='[CHANNEL_ID]', limit=25) If an existing active thread title matches normalized topic (+ same mode tag like [[DEBATE]]) created in last 24h: reuse that thread (THREAD_ID = existing_thread_id) post: ♻️ Duplicate topic detected — reusing existing thread. do NOT spawn a new orchestrator/panel If no match: create a new thread.

-1b) Create thread (if no dedup hit)

message( action = 'thread-create', channel = '[your configured channel]', channelId = '[CHANNEL_ID from user config]', threadName = '🎯 [topic — max 8 words] [[MODE]]', message = '**Panel:** [model list]\n**Mode:** [mode] | **Rounds:** [N]\n⏳ Analysis in progress...' ) Save the returned thread ID as THREAD_ID. All subsequent message() calls use target = THREAD_ID, NOT the channel ID. If thread creation fails or channel is not configured: fall back to posting directly in the active channel.

Step 0: Web Search Grounding (always first)

Run a web search on the topic before anything else — meta-panel and all agents will have current context. web_search(query = prompt, count = 5) Timeout policy: If web_search returns no result or errors within ~10s, do NOT block — continue immediately with CURRENT_CONTEXT = "No real-time data available (search failed or timed out).". The roundtable proceeds on model knowledge only. Caching: If re-running the same topic within the same session, reuse the prior CURRENT_CONTEXT block — do not re-search. Summarize results into a CURRENT_CONTEXT block (max 250 words): Key facts, recent developments, relevant data points Date of search If no useful results found: note "No relevant real-time data found" and continue This block is injected into: The meta-panel prompt (so they design the workflow with current context) Every Round 1 agent prompt (so all panelists argue from the same updated baseline)

Step 0b: Meta-Panel — Workflow Design

Skip if: --panel flag used, OR --quick flag used.

Spawn 4 premium meta-analysts in parallel

Read panels.json → meta.models. For each: sessions_spawn( task = filled prompts/meta-panel.md, model = model_id, mode = "run", label = "rt-meta-[A/B/C/D]", runTimeoutSeconds = 90 )

0b. Synthesize workflow from 4 recommendations

After collecting all meta responses, the orchestrator synthesizes the final workflow: Workflow type: majority vote among 4 recommendations Tie → prefer hybrid (more flexible) Stage composition: tally model recommendations per stage For each stage position, pick the most-recommended model If a model is not in agents.defaults.models allowlist → skip, use next If a model is your orchestrator's model → skip (reserved for the orchestrator, never a panelist) Rounds: median of recommendations (round up if tie) — hard cap at 3 max, always Synthesis model: most-recommended premium model not on the main panel Log the decision (include in output header): "Meta-panel designed workflow: [type]. Stages: [N]. Panel: [models]. Synthesis: [model]."

0c. Workflow types explained

parallel_debate — classic roundtable All agents in Stage 1 work independently, same prompt Round 2: cross-critique Best for: debates, opinions, risk analysis, decision-making sequential — output chains between stages Stage 1 agents produce outputs (drafts, code, research) Stage 2 agents receive Stage 1 outputs and review/validate/improve Best for: coding (write → review), research (collect → synthesize), creative (draft → refine) Round 2 within Stage 1 still possible; Stage 2 is a separate pass hybrid — parallel within stages, sequential between Stage 1: N agents work in parallel on different aspects Stage 2: 1-2 premium agents receive ALL Stage 1 outputs and produce integrated output Best for: complex analysis (parallel research → premium synthesis)

0d. Panel degradation rule

If any agent fails and fallback is SAME MODEL FAMILY → log: ⚠️ PANEL DEGRADED — [role] substituted [original] with [fallback] (same family: [family]) Always surface this in META section of final output with actionable guidance: If degraded due to missing blockrun → "Action: Start Blockrun at localhost:8402 for full panel, or use --panel budget for stable 2-model run" If degraded due to model not in allowlist → "Action: Add [model] to agents.defaults.models in openclaw.json" If degraded due to API error → "Action: Check provider API key / quota, then retry"

Step 1: Detect Mode (if no flag given)

ModeKeywordsdebatepros/cons, tradeoff, should we, ethics, compare, opinion, betterbuildimplement, code, architecture, build, design, develop, createredteamattack, vulnerability, failure, risk, break, threat, exploitvotechoose, decide, which one, best option, select, recommend betweendefaultanything else

parallel_debate (standard)

Round 1: Spawn all panel agents in parallel as persistent thread-bound sessions. sessions_spawn( task = filled prompts/round1.md, model = model_id, mode = "session", ← persistent — stays alive in the thread label = "rt-[role]", thread = true ← bound to the thread from Step -1 ) Save session keys: { "attacker": sessionKey, "defender": sessionKey, ... } Each agent writes their full response + SELF-DIGEST (last section) Collect all self-digests ⚠️ Agents stay alive — users can address them directly for follow-up questions Round 2 (if rounds ≥ 2): Send cross-critique prompt to each existing session via sessions_send. Do NOT re-spawn — reuse session keys from Round 1 [SELF_DIGEST] = this agent's own digest from Round 1 [PEER_DIGESTS] = other agents' digests (labeled with role) Extract AGREEMENT SCORES from each response Round 3 (if --validate): See Step 4.

sequential

Stage 1: Spawn agents in parallel as persistent sessions (mode="session", thread=true). Use standard prompts/round1.md. Round 2 cross-critique via sessions_send to existing sessions (no re-spawn). Collect full Stage 1 outputs for Stage 2. Stage 2: Spawn new persistent sessions (mode="session", thread=true). Build prompt: prompts/round1.md base + prepend Stage 1 outputs as context Label: "STAGE 1 OUTPUT from [Role]: [full output]" Stage 2 agents review/validate/improve Stage 1 work and write SELF-DIGESTs

hybrid

Stage 1: Parallel persistent sessions (mode="session", thread=true), each with a different sub-task. Customize Round 1 prompt to specify each agent's sub-task: "Your specific task for this stage: [task from workflow design]" Agents write SELF-DIGESTs Stage 2: 1-2 new persistent sessions (mode="session", thread=true) with all Stage 1 outputs embedded. Build prompt: prompts/round1.md base + "You are integrating and synthesizing the work of multiple agents. Their outputs: [all Stage 1 outputs]" Stage 2 produces the integrated output

Step 3: Consensus Scoring

After Round 2 (parallel_debate) or Stage 2 (sequential/hybrid): Extract AGREEMENT SCORES from each agent's Round 2 response. Build score matrix: { agent_role: { peer_role: score_1_to_5 } } Consensus % = (sum of all scores / (n_scores × 5)) × 100 If no Round 2 scores (quick mode / sequential): omit consensus %, mark as "N/A" Note on Round 3: Round 3 validation uses ACCURATE/PARTIALLY/INACCURATE — this is a separate metric from consensus %. Round 3 checks synthesis fidelity, not inter-agent agreement. Do NOT mix these two metrics. Consensus % comes only from Round 2 scores; Round 3 result appears separately in the META block as Validated: yes/no/partial.

Step 4: Round 3 — Validation (--validate flag only)

When to recommend --validate to the user: Consensus % < 40% (high disagreement — synthesis risks distortion) Redteam mode (adversarial stakes — synthesis must be bulletproof) Build mode with 3+ Stage 2 models (complex integration, easy to misrepresent) User explicitly mentions "high-stakes", "final decision", or "publishing this" When NOT to use it: Quick mode, debate on subjective topics, or when time matters more than precision. Draft synthesis first (Step 5 below), but do NOT post. Spawn validation agents: sessions_spawn( task = filled prompts/round3-validation.md, model = original agent model, label = "rt-r3-validate-[role]", runTimeoutSeconds = 60 ) Tally: 2+ INACCURATE → rewrite synthesis incorporating corrections 1 INACCURATE → note in META: ⚠️ [Role] flagged misrepresentation: [correction summary] All ACCURATE/PARTIAL → mark Validated: yes or Validated: partial in META

Step 5: Synthesis — Spawned Neutral Model

Never write synthesis yourself. sessions_spawn( task = filled prompts/final-synthesis.md, model = [synthesis model from meta-panel recommendation, or anthropic/claude-opus-4-6 as default], label = "rt-synthesis", mode = "run", runTimeoutSeconds = 180 ) Fill prompts/final-synthesis.md placeholders: [ROUND1_SUMMARIES] → all self-digests: "[ROLE] ([model]): [digest]" [ROUND2_SUMMARIES] → critiques: "[ROLE] criticized [peer]'s [claim] because [reason]" [CONSENSUS_SCORES] → full score matrix + calculated % [DISCORD_THREAD_ID] → the THREAD_ID from Step -1 (synthesis agent posts here) Post to Discord using THREAD_ID from Step -1 (not the channel ID). All round outputs and the final synthesis go into the same thread.

Step 6: Persist Results

Save to {workspace}/memory/roundtables/YYYY-MM-DD-[topic-slug].json: { "date": "YYYY-MM-DD", "topic": "[prompt]", "mode": "[mode]", "workflow_type": "parallel_debate|sequential|hybrid", "stages": [{ "model": "...", "role": "...", "task": "..." }], "meta_panel_recommendation": "[summary of meta votes]", "panel_degraded": false, "panel_degradation_notes": "", "consensus_pct": "XX% or N/A", "synthesis_model": "[model]", "validated": "yes|no|partial", "elapsed_time_sec": 0, "synthesis": "[final synthesis text]" } Also append one JSONL line to {workspace}/memory/roundtables/scorecard.jsonl with: ts, topic, mode, workflow_type, elapsed_time_sec, consensus_pct, validated, panel_degraded.

Edge Cases

SituationActionWeb search failsContinue with note "No real-time context available" in all prompts--no-search flagSkip Step 0 web search entirelyMeta-panel all failUse default panel for detected mode, log warning--quickSkip meta-panel + round 2. Always uses parallel_debate workflow. Spawns default panel for detected mode (3 models). Synthesizes after round 1 only.--panel overrideSkip meta-panel, use specified models, default to parallel_debateFallback = same familyContinue + log PANEL DEGRADED warning in METABoth model and fallback failSkip agent, note in META — do not wait, do not blockNo blockrun configuredWarn user: "Blockrun not available. Using budget panel. Full panel requires Blockrun at localhost:8402." Auto-switch to budget profile from panels.json.Agent timeout (any round)FAIL-CONTINUE: treat as absent, mark [TIMEOUT] in META, proceed with surviving agentsAgent fails mid-Round 2Use its Round 1 digest as final position, omit its scores from consensus calculationSynthesis agent failsOrchestrator writes synthesis, note: "Synthesis by orchestrator (bias risk — no neutral model available)"Stage 2 agent failsNote in META, synthesize with Stage 1 only0 agents respondReport failure, suggest retry1 agent respondsSkip Round 2 (no peers), synthesize from Round 1 only, mark consensus "N/A"--context-from SLUGLoad {workspace}/memory/roundtables/[slug].json, extract synthesis field, prepend to CURRENT_CONTEXT as "PRIOR ROUNDTABLE CONTEXT: [synthesis]". If file not found: warn and continue without prior context.

Placeholder Contract

When filling prompt templates, apply this rule for every [PLACEHOLDER]: PlaceholderIf missing/failedAction[CURRENT_CONTEXT]Web search failedInsert: "No real-time context available."[SELF_DIGEST]Agent timed out R1Skip agent entirely from R2[PEER_DIGESTS]All peers failedSkip R2, go to synthesis directly[ROUND1_SUMMARIES]No R1 outputsAbort with error: "0 agents responded"[ROUND2_SUMMARIES]Quick mode / no R2Insert: "No cross-critique (quick mode or single round)"[CONSENSUS_SCORES]No scores extractedInsert: "N/A — scores not available"[SYNTHESIS_DRAFT]Synthesis failedSkip R3, note in META Never leave a [PLACEHOLDER] unfilled in a prompt. Unfilled placeholders confuse models and produce garbage output.

Score Parsing (Round 2)

Agents write scores in free text. Extract scores with this heuristic: Look for the SCORES: block Match pattern: - [Role]: X/5 — extract integer X (1–5) If no clean integer found, scan for digit 1–5 nearest to the role name If still ambiguous → assign 3 (neutral) and note [SCORE INFERRED] in META Do NOT crash the workflow on a malformed score block.

Quick Reference: Default Panels (fallback if meta-panel fails)

debate: [opus-4.6, gpt-5.3-codex, gemini-3.1-pro, grok-4] → Advocate / Devil's Advocate / Analyst / Contrarian build: [opus-4.6, gemini-3.1-pro, grok-4, gpt-5.3-codex] → Architect / Reviewer / Engineer / Implementer redteam: [opus-4.6, gemini-3.1-pro, grok-4, gpt-5.3-codex] → Defender / Analyst / Attacker / Red Teamer vote: [opus-4.6, gemini-3.1-pro, grok-4, gpt-5.3-codex] → 4-way vote panel (all via blockrun/ prefix — see panels.json for exact model IDs and fallbacks)

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

5 Docs1 Config

SKILL.md Primary doc
examples/debate-ai-developer-2026-02-23.md Docs
examples/priorityA-checklist.md Docs
prompts/final-synthesis.md Docs
README.md Docs
panels.json Config