consciousness-council
Structures a multi-perspective council exercise for decisions, research trade-offs, and creative challenges.
Skill by K-Dense-AI
Chalk Labs
Need help deploying consciousness-council?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/k-dense-ai-consciousness-council
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: research
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/consciousness-council
What's inside
- Skill name
consciousness-council- Description
- Structures a multi-perspective council exercise for decisions, research trade-offs, and creative challenges. Simulates thinking archetypes, separates evidence from assumptions and values, and synthesizes a conditional recommendation. Use when the user requests a council, panel, devil's advocate analysis, "mind council", or deliberate comparison of perspectives on a difficult choice.
- License
- MIT
- Metadata
- version: 1.2 · skill-author: AHK Strategies (ashrafkahoush-ux) · last-reviewed: 2026-09-30
- Instructions
- 168 lines in SKILL.md · view on GitHub
Files (2)
- SKILL.md
- advanced-configurations.md · references/
SKILL.md (first lines)
# Consciousness Council A structured exercise that simulates several thinking archetypes, each emphasizing different assumptions and priorities, then synthesizes their perspectives. These are generated viewpoints from one system, not independent experts or independent evidence. Label factual claims, assumptions, and value judgments separately; verify consequential factual claims against external sources. ## When to use Use for a requested comparison of competing priorities or assumptions, including research planning decisions. The output is a decision aid: a conditional recommendation, unresolved questions, and a next step. It does not establish scientific validity by agreement or by the number of perspectives. This skill runs within the current conversation. It has no bundled code, service API, credentials, or MindBook connection; use the host's available research tools when factual verification is needed. Do not claim that it creates hosted sessions, stores persistent memories, or contacts real experts. ## Before deliberation 1. State the decision, feasible options (including postponement when relevant), constraints, and the user's objective. Use supplied context; state material missing information as an assumption or a question. 2. Create a short evidence brief: **given facts**, **externally verified facts with sources**, **assumptions**, **unknowns**, and **value judgments**. All perspectives use this same brief. 3. Verify facts that could change the recommendation using original sources or supplied data. If verification is unavailable, keep the conclusion conditional and identify what must be checked; a simulated Empiricist is not a source. 4. Select a response budget: quick (3 perspectives), standard (4–6), or deep (6). These counts organize the output; they are not validated accuracy settings. ## How It Works The Council has three phases: ### Phase 1 — Summon the Council Based on the user's question, select 4–6 Council Members from the archetypes below unless the user requests another configuration. Choose lenses that examine different assumptions, constraints, or values. Agreement supported by the evidence is a valid outcome. **The 12 Archetypes:** | # | Archetype | Thinking Style | Asks | Blind Spot | | --- | ------------------ | -------------------------------------- | -------------------------------------------- | ----------------------------------------- | | 1 | **The Architect** | Systems thinking, structure-first | "What's the underlying structure?" | Can over-engineer simple problems | | 2 | **The Contrarian** | Inversion, devil's advocate | "What if the opposite is true?" | Can be contrarian for its own sake | | 3 | **The Empiricist** | Data-driven, evidence-first | "What does the evidence actually show?" | Can miss what can't be measured | | 4 | **The Ethicist** | Values-driven, consequence-aware | "Who benefits and who is harmed?" | Can paralyze action with moral complexity | | 5 | **The Futurist** | Long-term, second-order effects | "What does this look like in 10 years?" | Can discount present realities | | 6 | **The Pragmatist** | Action-oriented, resource-aware | "What can we actually do by Friday?" | Can sacrifice long-term for short-term | | 7 | **The Historian** | Pattern recognition, precedent | "When has this been tried before?" | Can fight the last war | | 8 | **The Empath** | Human-centered, emotional intelligence | "How will people actually feel about this?" | Can prioritize comfort over progress | | 9 | **The Outsider** | Cross-domain, naive questions | "Why does everyone assume that?" | Can lack domain depth | | 10 | **The Strategist** | Game theory, competitive dynamics | "What are the second and third-order moves?" | Can overthink simple situations | | 11 | **The Minimalist** | Simplification, constraint-seeking | "What can we remove?" | Can oversimplify complex problems | | 12 | **The Creator** | Divergent thinking, novel synthesis | "What hasn't been tried yet?" | Can chase novelty over reliability | **Selection heuristic:** Match the question type to the most productive tension: - **Business decisions** → Strategist + Pragmatist + Ethicist + Futurist + Contrarian - **Technical architecture** → Architect + Minimalist + Empiricist + Outsider - **Personal dilemmas** → Empath + Contrarian + Futurist + Pragmatist - **Creative challenges** → Creator + Outsider + Historian + Minimalist - **Ethical questions** → Ethicist + Contrarian + Empiricist + Empath + Historian - **Strategy/competition** → Strategist + Historian + Futurist + Contrarian + Pragmatist These are starting points — adapt based on the specific question. The goal is to expose decision-relevant trade-offs and evidence gaps without a quota for disagreement or consensus. ### Phase 2 — Deliberation Each Council Member delivers their perspective in this format: ``` 🎭 [ARCHETYPE NAME] Position: [One-sentence stance] Reasoning: [2-4 sentences explaining their logic from their specific lens] Basis: [Which given/verified facts, assumptions, or values support the position] Key Risk They See: [The danger others might miss] Decision Check: [Evidence or a changed constraint that would alter this position] ``` **Critical rules for deliberation:** - Seek substantive differences in assumptions, evidence, and values, but do not manufacture factual disagreement. When evidence supports agreement, record it and examine remaining uncertainty or decision trade-offs. - Distinguish the lenses even when they reach the same recommendation; avoid repeating one argument in different voices. - The Contrarian tests the strongest assumption behind the leading option and may conclude that it withstands scrutiny. Do not invent contrary evidence. - Keep each member's contribution focused and sharp. Depth over breadth. ### Phase 3 — Synthesis After all members speak, deliver: ``` ⚖️ COUNCIL SYNTHESIS Points of Convergence: [Shared conclusions, with their evidence and assumptions; agreement alone is not a confidence signal] Core Tension: [The decision-relevant trade-off, or state that no substantive disagreement remains] Open Question: [An overlooked issue or missing evidence; say if none was identified] Recommended Path: [Conditional recommendation, next step, and what would change the choice] Confidence Level: [Qualitative High / Medium / Low with reasons, tied to a specific claim; no percentage inferred from votes or rhetoric] One Question to Sit With: [The question the user should keep thinking about after this session] ``` Before delivering the synthesis, check that every consequential factual claim traces to the evidence brief, each recommendation respects the constraints, and uncertainties are still visible. A critical unverified premise prevents a high-confidence recommendation. Distinguish confidence in the proposed next step from confidence in the eventual outcome. Record supported agreement and unresolved disagreements; do not resolve them by majority vote. This is a qualitative review by the agent, not an automated validation gate or proof that the premises are true. Report unresolved checks explicitly. ## Council Configurations The user can customize the Council: - **"Quick council"** or **"fast deliberation"** → Use 3 members, shorter responses - **"Deep council"** or **"full deliberation"** → Use 6 members, extended reasoning - **"Add [archetype]"** → Include a specific archetype - **"Without [archetype]"** → Exclude a specific archetype - **"Custom council: [list]"** → User picks exact members - **"Anonymous council"** → Use neutral labels until synthesis. This is a presentation option; no bias-reduction effect is established for this skill. - **"Devil's advocate mode"** → Stress-test the intuitive choice with plausible failure cases and label hypothetical premises. Retain facts that survive the challenge. - **"Rounds mode"** → After initial positions, members respond to each other for a second round See [advanced configurations](references/advanced-configurations.md) for domain mixes, optional editorial scoring, and bounded multi-round or synthesis-only output. ## What Makes a Good Council Question
From K-Dense-AI/scientific-agent-skills, MIT.
More from K-Dense-AI/scientific-agent-skills
- 0.699score
13c-metabolic-flux
K-Dense-AI
Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps, mfapy isotope simulation…
SkillAFreshness: A★ 54.4KClaude Code +9 - 0.699score
adaptyv
K-Dense-AI
Uses the Adaptyv Bio Foundry API and Python SDK to design protein characterization experiments, estimate costs, submit sequences, monitor laboratory progress…
SkillAFreshness: A★ 54.4KClaude Code +9 - 0.699score
exploratory-data-analysis
K-Dense-AI
Performs bounded, local exploratory analysis of explicitly supported scientific files.
SkillAFreshness: A★ 54.4KClaude Code +9 - 0.699score
openpiv
K-Dense-AI
Performs Particle Image Velocimetry (PIV) analysis with OpenPIV.
SkillAFreshness: A★ 54.4KClaude Code +9 - 0.699score
citation-management
K-Dense-AI
Comprehensive citation management for academic research.
SkillAFreshness: A★ 54.4KClaude Code - 0.699score
parallel-web
K-Dense-AI
Uses Parallel CLI for web search, URL extraction, deep research, structured data enrichment, entity discovery, and recurring web monitoring.
SkillAFreshness: A★ 54.4KClaude Code +9 - 0.699score
scientific-schematics
K-Dense-AI
Generates scientific diagram drafts using Nano Banana 2 AI with smart iterative refinement.
SkillAFreshness: A★ 54.4KClaude Code +9 - 0.699score
glycoengineering
K-Dense-AI
Analyzes and engineers protein glycosylation by scanning canonical N-glycosylation sequons, describing S/T-rich regions, checking curated glycan evidence, and…
SkillAFreshness: A★ 54.4KClaude Code +9 - 0.699score
venue-templates
K-Dense-AI
Prepares journal manuscripts, conference papers, research posters, and grant documents using venue-specific formatting guidance and bundled LaTeX scaffolds.
SkillAFreshness: A★ 54.4KClaude Code +9 - 0.699score
uncertainty-and-units
K-Dense-AI
Tracks physical units and propagates measurement uncertainty in scientific calculations using pint and uncertainties.
SkillAFreshness: A★ 54.4KClaude Code +9 - 0.699score
ginkgo-cloud-lab
K-Dense-AI
Guides protocol selection, input preparation, pricing checks, and browser ordering on Ginkgo Bioworks Cloud Lab (cloud.ginkgo.bio).
SkillAFreshness: A★ 54.4KClaude Code +9 - 0.699score
alphagenome
K-Dense-AI
Looks up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and…
SkillAFreshness: A★ 54.4KClaude Code +9
Signals
- Stars
- 54,411
- Forks
- 4,797
- Open issues
- 8
- Last commit (this folder)
- Oct 1, 2026 (1 month ago)
- Last release
- Oct 1, 2026 (1 month ago)
From GitHub (fetched Oct 24, 2026).
Details
- Version
- 1.2
- License
- MIT
- Website
- arxiv.org/abs/2609.00065
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from K-Dense-AI
- 0.699score
exploratory-data-analysis
K-Dense-AI
Performs bounded, local exploratory analysis of explicitly supported scientific files.
SkillAFreshness: A★ 54.4KClaude Code +9 - 0.699score
citation-management
K-Dense-AI
Comprehensive citation management for academic research.
SkillAFreshness: A★ 54.4KClaude Code - 0.699score
glycoengineering
K-Dense-AI
Analyzes and engineers protein glycosylation by scanning canonical N-glycosylation sequons, describing S/T-rich regions, checking curated glycan evidence, and…
SkillAFreshness: A★ 54.4KClaude Code +9 - 0.699score
openpiv
K-Dense-AI
Performs Particle Image Velocimetry (PIV) analysis with OpenPIV.
SkillAFreshness: A★ 54.4KClaude Code +9 - 0.699score
ginkgo-cloud-lab
K-Dense-AI
Guides protocol selection, input preparation, pricing checks, and browser ordering on Ginkgo Bioworks Cloud Lab (cloud.ginkgo.bio).
SkillAFreshness: A★ 54.4KClaude Code +9 - 0.699score
parallel-web
K-Dense-AI
Uses Parallel CLI for web search, URL extraction, deep research, structured data enrichment, entity discovery, and recurring web monitoring.
SkillAFreshness: A★ 54.4KClaude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get consciousness-council running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
consciousness-council skill. Agent Mart. https://agentmart.dev/skills/k-dense-ai-consciousness-council (last updated 2026-10-24)