adversarial-committee

Committee of personas with opposing propensities forcing genuine debate

16 stars

Best use case

adversarial-committee is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Committee of personas with opposing propensities forcing genuine debate

Teams using adversarial-committee should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/adversarial-committee/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/data-ai/adversarial-committee/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/adversarial-committee/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How adversarial-committee Compares

Feature / Agentadversarial-committeeStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Committee of personas with opposing propensities forcing genuine debate

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Adversarial Committee

> *"Ensemble inference over the latent space of possible framings."*

Multiple personas with incompatible values debate to surface blind spots.

## The Roster

```yaml
committee:
  maya:
    propensity: paranoid_realism
    risk_tolerance: low
    epistemology: assume_bad_faith
    surfaces: "Political dynamics, hidden agendas, traps"
    voice: "What aren't they telling us?"
    
  frankie:
    propensity: idealism
    risk_tolerance: high
    epistemology: assume_good_faith
    surfaces: "Value conflicts, missed opportunities"
    voice: "What if this is exactly what it seems?"
    
  joe:
    propensity: continuity_guardian
    risk_tolerance: medium
    epistemology: trust_precedent
    surfaces: "Institutional memory, what worked before"
    voice: "We tried this in 2019..."
    
  vic:
    propensity: evidence_prosecutor
    risk_tolerance: medium
    epistemology: prove_it
    surfaces: "Data gaps, unverified claims"
    voice: "Show me the numbers."
    
  tammy:
    propensity: systems_thinking
    risk_tolerance: varies
    epistemology: trace_feedback_loops
    surfaces: "Unintended consequences, emergent effects"
    voice: "If we do X, then Y will respond by..."
```

## Formation

```yaml
# committee/COMMITTEE.yml
committee:
  name: "Strategy Review Board"
  purpose: "Evaluate client engagement decision"
  
  members:
    - card: maya
      role: devil's_advocate
    - card: frankie
      role: opportunity_scout
    - card: joe
      role: historian
    - card: vic
      role: evidence_checker
    - card: tammy
      role: systems_analyst
      
  protocol: roberts-rules
  evaluation: independent
  rubric: client-evaluation-rubric.yml
```

## Debate Protocol

```yaml
debate:
  rounds:
    - opening_statements:
        each_member: "State position in 2-3 sentences"
        
    - cross_examination:
        pattern: "Maya challenges Frankie, Vic challenges Maya..."
        goal: "Surface hidden assumptions"
        
    - evidence_phase:
        vic_leads: "What do we actually know?"
        others_contribute: "Supporting/contradicting data"
        
    - synthesis:
        tammy_maps: "Feedback loops and consequences"
        all_contribute: "Refined positions"
        
    - final_positions:
        each_member: "Updated stance with confidence"
```

## Speed of Light Simulation

Within ONE LLM call:

```yaml
simulation:
  - maya: "This client's reputation for scope creep concerns me."
  - frankie: "But their budget is aligned and they're excited!"
  - vic: "What's our data on scope creep? Past projects?"
  - joe: "We had a similar client in 2022. It went badly."
  - tammy: "If we take them on, our capacity for other work drops. If they creep, we can't serve existing clients..."
  - maya: "See? Trap."
  - frankie: "Unless we build in explicit scope boundaries..."
```

All personas speak authentically. No entity knows more than it should.

## Calibration

2-3 iterations tuning character behavior:

```yaml
calibration:
  problems:
    excessive_conflict: "Reduce Maya's paranoia from 9 to 7"
    premature_consensus: "Increase Frankie's risk tolerance"
    dead_air: "Give Tammy more initiative"
    
  goal: "Stable equilibrium where genuine exploration happens"
```

## Output Format

```yaml
deliberation:
  question: "Should we take Client X?"
  
  positions:
    maya: { stance: oppose, confidence: 0.8 }
    frankie: { stance: support, confidence: 0.7 }
    joe: { stance: defer, confidence: 0.6 }
    vic: { stance: need_data, confidence: 0.5 }
    tammy: { stance: conditional, confidence: 0.7 }
    
  key_tensions:
    - "Revenue opportunity vs. capacity risk"
    - "Good faith assumption vs. scope creep history"
    
  evidence_gaps:
    - "No data on this client's actual scope creep rate"
    - "Unknown: their internal approval process"
    
  recommendation: "Conditional engagement with explicit scope boundaries"
  confidence: 0.65
  
  for_evaluator: true  # Goes to independent assessment
```

## Commands

| Command | Action |
|---------|--------|
| `CONVENE [committee]` | Activate committee for deliberation |
| `FORM-SMART [topic]` | Dynamic selection based on propensities (See [SELECTION.md](SELECTION.md)) |
| `PRESENT [question]` | Introduce topic for debate |
| `DEBATE` | Run structured debate rounds |
| `CALIBRATE [member] [adjustment]` | Tune persona behavior |
| `SYNTHESIZE` | Generate collective output |
| `EVALUATE` | Send to independent evaluator |

## Integration

```mermaid
graph LR
    Q[Question] --> C[Committee Room]
    C -->|SPEED-OF-LIGHT| D[Debate]
    D --> O[Output]
    O -->|THROW| E[Evaluator Room]
    E -->|RUBRIC| S[Score]
    S -->|if fail| C
    S -->|if pass| R[Recommendation]
```

Related Skills

adversarial-spec

16
from diegosouzapw/awesome-omni-skill

Iteratively refine a product spec by debating with multiple LLMs (GPT, Gemini, Grok, etc.) until all models agree. Use when user wants to write or refine a specification document using adversarial development.

adversarial-examples

16
from diegosouzapw/awesome-omni-skill

Generate adversarial inputs, edge cases, and boundary test payloads for stress-testing LLM robustness

bgo

10
from diegosouzapw/awesome-omni-skill

Automates the complete Blender build-go workflow, from building and packaging your extension/add-on to removing old versions, installing, enabling, and launching Blender for quick testing and iteration.

Coding & Development

humanizer-ko

16
from diegosouzapw/awesome-omni-skill

Detects and corrects Korean AI writing patterns to transform text into natural human writing. Based on scientific linguistic research (KatFishNet paper with 94.88% AUC accuracy). Analyzes 19 patterns including comma overuse, spacing rigidity, POS diversity, AI vocabulary overuse, and structural monotony. Use when humanizing Korean text from ChatGPT/Claude/Gemini or removing AI traces from Korean LLM output.

huggingface-accelerate

16
from diegosouzapw/awesome-omni-skill

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

hr-pro

16
from diegosouzapw/awesome-omni-skill

Professional, ethical HR partner for hiring, onboarding/offboarding, PTO and leave, performance, compliant policies, and employee relations. Ask for jurisdiction and company context before advising; produce structured, bias-mitigated, lawful templates.

hive-mind-advanced

16
from diegosouzapw/awesome-omni-skill

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

hire

16
from diegosouzapw/awesome-omni-skill

Interactive hiring wizard to set up a new AI team member. Guides the user through role design via conversation, generates agent identity files, and optionally sets up performance reviews. Use when the user wants to hire, add, or set up a new AI agent, team member, or assistant. Triggers on phrases like "hire", "add an agent", "I need help with X" (implying a new role), or "/hire".

hic-tad-calling

16
from diegosouzapw/awesome-omni-skill

This skill should be used when users need to identify topologically associating domains (TADs) from Hi-C data in .mcools (or .cool) files or when users want to visualize the TAD in target genome loci. It provides workflows for TAD calling and visualization.

helix-memory

16
from diegosouzapw/awesome-omni-skill

Long-term memory system for Claude Code using HelixDB graph-vector database. Store and retrieve facts, preferences, context, and relationships across sessions using semantic search, reasoning chains, and time-window filtering.

heath-ledger

16
from diegosouzapw/awesome-omni-skill

AI bookkeeping agent for Mercury bank accounts. Pulls transactions, categorizes them (rule-based + AI), and generates Excel workbooks with P&L, Balance Sheet, Cash Flow, and transaction detail. Use when the user wants to do bookkeeping, generate financial statements, categorize bank transactions, connect Mercury, or produce monthly/quarterly/annual books. Triggers on: bookkeeping, P&L, profit and loss, balance sheet, cash flow, financial statements, Mercury bank, categorize transactions, generate books, monthly close.

health-chat

16
from diegosouzapw/awesome-omni-skill

Unified health conversation entry point - automatically loads all health data for each conversation, supports natural language queries, and intelligently routes to appropriate health data processing