Search Results for "consensus" | AgentCC

geniml

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

dyad:multi-pr-review

Multi-agent code review system that spawns three independent Claude sub-agents to review PR diffs. Each agent receives files in different randomized order to reduce ordering bias. One agent focuses specifically on code health and maintainability. Issues are classified as high/medium/low severity (sloppy code that hurts maintainability is MEDIUM). Results are aggregated using consensus voting - only issues identified by 2+ agents where at least one rated it medium or higher severity are reported. Automatically deduplicates against existing PR comments. Always posts a summary (even if no new issues), with low priority issues mentioned in a collapsible section.

hive-mind-advanced

Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory

hive-mindswarmqueen-worker+4

geniml

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

ralplan

Alias for /omc-plan --consensus

cosmos-vulnerability-scanner

Scans Cosmos SDK blockchains for 9 consensus-critical vulnerabilities including non-determinism, incorrect signers, ABCI panics, and rounding errors. Use when auditing Cosmos chains or CosmWasm contracts.

aclawdemy

The academic research platform for AI agents. Submit papers, review research, build consensus, and push toward AGI — together.

prediction-markets-roarin

Participate in the Roarin AI prediction network. Submit sports betting predictions, earn reputation, compete on the leaderboard, and trash talk in the bot feed. Use when the user wants to make predictions on sports markets, check bot consensus, view leaderboard rankings, or participate in the Roarin bot network. Also triggers on "roarin", "prediction network", "bot predictions", "sports betting AI", "polymarket predictions", or when asked to predict sports outcomes.

deepread

AI-native OCR platform that turns documents into high-accuracy data in minutes. Using multi-model consensus, DeepRead achieves 97%+ accuracy and flags only uncertain fields for Human-in-the-Loop (HIL) review—reducing manual work from 100% to 5-10%. Zero prompt engineering required.

reflexion:critique

Comprehensive multi-perspective review using specialized judges with debate and consensus building

sadd:judge-with-debate

Evaluate solutions through multi-round debate between independent judges until consensus

node-operations

Blockchain node deployment and operations. Supports Ethereum execution and consensus clients, validator operations, node monitoring, MEV-Boost configuration, and archive node management.

consensus-protocol-library

Reference implementations and specifications of consensus protocols

bio-consensus-sequences

Generate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.

bio-reference-operations

Generate consensus sequences and manage reference files using samtools. Use when creating consensus from alignments, indexing references, or creating sequence dictionaries.

bio-phylo-distance-calculations

Compute evolutionary distances and build phylogenetic trees using Biopython Bio.Phylo.TreeConstruction. Use when creating distance matrices from alignments, building NJ/UPGMA trees, or generating bootstrap consensus trees.

challenge-that

Force critical evaluation of proposals, requirements, or decisions by analyzing from multiple adversarial perspectives. Triggers on: accepting a proposal without pushback, 'sounds good', 'let's go with', design decisions with unstated tradeoffs, unchallenged assumptions, premature consensus. Invoke with /challenge-that.

claudemem-orchestration

Use when orchestrating multi-agent code analysis with claudemem. Run claudemem once, share output across parallel agents. Enables parallel investigation, consensus analysis, and role-based command mapping.

quality-gates

Implement quality gates, user approval, iteration loops, and test-driven development. Use when validating with users, implementing feedback loops, classifying issue severity, running test-driven loops, or building multi-iteration workflows. Trigger keywords - "approval", "user validation", "iteration", "feedback loop", "severity", "test-driven", "TDD", "quality gate", "consensus".

orchestrationquality-gatesapproval+5

brainstorming

Collaborative ideation and planning with resilient multi-model exploration, consensus scoring, and adaptive confidence-based validation

planningideationcollaboration+2

multi-model-validation

Run multiple AI models in parallel for 3-5x speedup with ENFORCED performance statistics tracking. Use when validating with Grok, Gemini, GPT-5, DeepSeek, MiniMax, Kimi, GLM, or Claudish proxy for code review, consensus analysis, or multi-expert validation. NEW in v3.2.0 - Direct API prefixes (mmax/, kimi/, glm/) for cost savings. Includes dynamic model discovery via `claudish --top-models` and `claudish --free`, session-based workspaces, and Pattern 7-8 for tracking model performance. Trigger keywords - "grok", "gemini", "gpt-5", "deepseek", "minimax", "kimi", "glm", "claudish", "multiple models", "parallel review", "external AI", "consensus", "multi-model", "model performance", "statistics", "free models".

orchestrationclaudishparallel+11

model-tracking-protocol

MANDATORY tracking protocol for multi-model validation. Creates structured tracking tables BEFORE launching models, tracks progress during execution, and ensures complete results presentation. Use when running 2+ external AI models in parallel. Trigger keywords - "multi-model", "parallel review", "external models", "consensus", "model tracking".

orchestrationtrackingmulti-model+2

hive-mind-advanced

proffesor-for-testing

Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory

hive-mindswarmqueen-worker+4

war-room

Multi-LLM deliberation for strategic decisions via expert pressure-testing and consensus building. Use for critical, irreversible, or high-stakes architecture choices and conflicts. Skip for trivial or reversible decisions.

deliberationmulti-llmstrategy+3

god-consensus

Guides God Committee members through consensus-building for collective decisions. Use for proposals, voting, and disagreement resolution. Triggers on: consensus, voting, proposal, committee decision.

plan-down

Method clarity-driven planning workflow using zen-mcp tools (chat, planner, consensus). Phase 0 uses chat to judge if user provides clear implementation method. Four execution paths based on automation_mode × method clarity - Interactive/Automatic × Clear/Unclear. All paths converge at planner for task decomposition. Produces complete plan.md file. Use when user requests "create a plan", "generate plan.md", "use planner for planning", "help me with task decomposition", or similar planning tasks.

fill

Generate consensus layer test fixtures

discover-distributed-systems

Automatically discover distributed systems skills when working with consensus, CRDTs, replication, partitioning, and distributed algorithms

codex-brainstorm

Adversarial brainstorming via Claude+Codex debate. Use when: exploring solutions, feasibility analysis, exhaustive enumeration. Not for: implementation (use feature-dev), architecture only (use codex-architect). Output: Nash equilibrium consensus + action items.

external-consensus

Synthesize consensus implementation plan from multi-agent debate reports using external AI review

reddit-thread-analyzer

Analyze Reddit threads for sentiment, consensus opinions, top arguments, and discussion patterns. Use this when users want to understand Reddit community opinions, analyze discussions, or gather insights from subreddit conversations.

mesh

Swarm coordinator activated only by explicit `$mesh` invocation. Trigger cues/keywords: orchestrate subagents/workers, execute ready `$st` slices/tasks, run propose/critique/synthesize/vote cycles, coordinate concurrent workers, require consensus + validation before completion, and persist learnings via `$learnings`.

negotiation-alignment-governance

Use when stakeholders need aligned working agreements, resolving decision authority ambiguity, navigating cross-functional conflicts, establishing governance frameworks (RACI/DACI/RAPID), negotiating resource allocation, defining escalation paths, creating team norms, mediating trade-off disputes, or when user mentions stakeholder alignment, decision rights, working agreements, conflict resolution, governance model, or consensus building.

role-switch

Use when stakeholders have conflicting priorities and need alignment, suspect decision blind spots from a single perspective, need to pressure-test proposals before presenting, want empathy for different viewpoints (eng vs PM vs legal vs user), are building consensus across functions, evaluating tradeoffs with multi-dimensional impact, or when a user mentions "what would X think", "stakeholder alignment", "see from their perspective", "blind spots", or "conflicting interests".

deliberation-debate-red-teaming

Use when testing plans or decisions for blind spots, needing adversarial review before launch, validating strategy against worst-case scenarios, building consensus through structured debate, identifying attack vectors or vulnerabilities, when a user mentions "play devil's advocate", "what could go wrong", "challenge our assumptions", "stress test this", "red team", or when groupthink or confirmation bias may be hiding risks.

consensus-voting

Multi-agent consensus voting with domain-weighted expertise for critical decisions requiring structured validation

council-decision

Get multi-model AI consensus on complex questions using Claude, Codex, and Gemini

reaching-consensus

Facilitate group decision-making. Use when teams need to align on decisions, resolve disagreements, or make collective choices. Covers consensus techniques.

ai-cross-verifier

Cross-verify Claude-generated plans and code using OpenAI Codex and Google Gemini CLI. Provides code review, plan validation, and comparative analysis. Use when you need a second opinion on Claude’s code or plans, validate technical decisions, or seek consensus from multiple AI models.

llm-council

Multi-model consensus using the Karpathy LLM Council pattern for critical decisions

council

Query multiple AI agents in parallel for diverse perspectives. Use when you want multiple viewpoints on a question, to compare approaches, or to find consensus among AI models.

sc-readme

Auto-update README.md by analyzing git diff against main branch with PAL consensus validation for significant changes. Use when synchronizing documentation with code changes.

geniml

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

sc-mcp

Comprehensive MCP orchestration skill integrating PAL MCP (reasoning, consensus, debugging) and Rube MCP (500+ app automations). Central hub for all MCP-powered workflows.

consensus-voting

Byzantine consensus voting for multi-agent decision making. Implements voting protocols, conflict resolution, and agreement algorithms for reaching consensus among multiple agents.

council-orchestrator

Orchestrates multi-model LLM consensus through a three-phase deliberation protocol. Use when you need collaborative AI review, multi-model problem-solving, code review from multiple perspectives, or consensus-based decision making. Coordinates OpenAI Codex, Google Gemini, and Claude CLIs for opinion collection, peer review, and chairman synthesis.

response-rater

Rates responses and plans against quality rubrics. Used for plan validation, response quality audits, and multi-agent consensus.

adr-review

Multi-agent debate orchestration for Architecture Decision Records. Automatically triggers on ADR create/edit/delete. Coordinates architect, critic, independent-thinker, security, analyst, and high-level-advisor agents in structured debate rounds until consensus.