Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Multi Model Critique
Use multiple models in a 4-step cycle of drafting, cross-critique, revision, and synthesis to generate higher-quality answers for complex, high-stakes queries.
skill openclawclawhub Free
Use multiple models in a 4-step cycle of drafting, cross-critique, revision, and synthesis to generate higher-quality answers for complex, high-stakes queries.
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0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/orchestration-template.md, references/output-schema.md, references/prompt-templates.md, scripts/build_round_prompts.py, scripts/run_orchestration.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.1
Provenance
- Publisher
- prairiedoggg
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Overview
Use this skill only for complex tasks. Route multiple models through the same 4-step loop (Plan -> Execute -> Review -> Improve), then run cross-critique and synthesis to produce a higher-quality final answer than any single-model draft.
Trigger rule
Enable this skill only when the request explicitly sets complex to true (or equivalent wording such as βthis is complex/deepβ). If complex is false, skip this skill and respond with normal single-model behavior.
Inputs
Collect or confirm these inputs before execution: complex: boolean flag (must be true) question: user request models: list of ACP agentId values (typically 3) constraints: output format, language, length, deadlines, forbidden assumptions ops: optional runtime controls (timeoutSec, maxRetries, maxRounds, budgetUsd)
File map (what each file does)
SKILL.md (this file): orchestration policy, trigger conditions, and execution sequence. references/prompt-templates.md: reusable prompts for draft, critique, revision, and final synthesis (includes scoring rubric usage). references/orchestration-template.md: practical OpenClaw orchestration flow using sessions_spawn, sessions_send, and sessions_history. references/output-schema.md: machine-parseable JSON output schema for final result and per-model scoring. scripts/build_round_prompts.py: utility to generate per-model prompt files for repeated runs. scripts/run_orchestration.py: local helper that builds a run plan JSON (model mapping, round prompts, runtime settings).
Step 1) Parallel draft round
Spawn one ACP session per model with the same task and constraints. Per-model requirements: Follow the exact internal sequence: Plan -> Execute -> Review -> Improve Print all four sections explicitly End with Draft Answer Use sessions_spawn with runtime:"acp" and explicit agentId.
Step 2) Cross-critique round
Share peer Draft Answer outputs with each model and require structured critique: Strengths Weaknesses Missing assumptions/data Hallucination and confidence risks Concrete fix suggestions Also require ranking of peer drafts with rationale.
Step 3) Revision round
Send critique feedback back to each original model and request revision: Keep Plan -> Execute -> Review -> Improve Include Changes from Critique End with Revised Answer
Step 4) Final synthesis round
Integrate revised answers into one user-facing output: Best final answer Why the synthesis is stronger than individual drafts Remaining uncertainties Optional next actions
Scoring rubric (required in critique + synthesis)
Score each draft on a 1-5 scale: accuracy: factual correctness and internal consistency coverage: completeness against user request and constraints evidence: quality of assumptions and support actionability: usefulness for concrete decision/action Default weighted score: 0.40 * accuracy + 0.25 * coverage + 0.20 * evidence + 0.15 * actionability Use this score to justify rankings and the final selected direction.
Prompting resources
Use references/prompt-templates.md for canonical prompts. Use scripts/build_round_prompts.py when you need file-based prompt generation for repeated or batched runs. Use scripts/run_orchestration.py to generate a deterministic run-plan artifact for reproducible execution. Use references/orchestration-template.md for concrete OpenClaw tool-call flow.
Required user-facing output shape
Final Answer Key Improvements from Critique Uncertainties Next Steps (optional) When machine consumption is needed, return JSON matching references/output-schema.md. Do not expose private chain-of-thought. Provide concise reasoning summaries only.
Failure handling
One model fails: continue with remaining models and note reduced diversity. Two or more models fail: ask whether to retry or switch to single-model mode. Strong disagreement remains: present competing hypotheses and state what evidence would resolve them.
Runtime defaults (recommended)
timeoutSec: 180 per round per model maxRetries: 1 per failed model turn maxRounds: fixed at 4 (draft, critique, revision, synthesis) budgetUsd: optional hard stop when cost-sensitive
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
4 Docs2 Scripts
- SKILL.md
- references/orchestration-template.md
- references/output-schema.md
- references/prompt-templates.md
- scripts/build_round_prompts.py
- scripts/run_orchestration.py