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Local Council Execution

Community
hex
local-council-execution

Runs a local council when no external AI providers are configured. Spawns N independent Claude subagents (one per role, blind to each other) that each answer the question from a single assigned lens, then synthesizes their perspectives. Invoked by the ask command via --local, or when the user accepts the local-council offer after no providers are found. This is a same-model (Claude-only) panel, not a cross-vendor council.

Overview

Publisherhex
Repositoryclaude-council
Skill namelocal-council-execution
Stars
866
Forks
106
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by hex on GitHub. Read the source before you install it.

Installation

Install the Local Council Execution AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/hex/claude-council/tree/main/skills/local-council-execution
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/hex/claude-council.git /tmp/claude-council
mkdir -p .claude/skills
cp -r /tmp/claude-council/skills/local-council-execution .claude/skills/local-council-execution
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Local Council Execution in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Local Council Execution on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Local Council Execution is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Local Council Execution

When no external providers are configured, convene a council of independent Claude subagents instead. Each member answers from one role, blind to the others; you then synthesize the spread of perspectives.

Read this first — the honest framing is not optional. Every member is Claude. They share priors and training, so agreement between them is weak signal, not validation. The value here is independent angles and blind-spot coverage, not consensus. The output you produce MUST make this explicit and MUST NOT present member agreement as if it were cross-vendor corroboration.

Step 0: Confirm this is a local-council context (guard)

A local council is the fallback for when no real providers exist — it is not a default way to "use the council". Only proceed if one of these holds:

  1. The user explicitly passed --local, or
  2. There are no configured providers and the user accepted the local-council offer.

If you reached this skill any other way, verify providers are actually absent before spawning anything:

bash
bash ${CLAUDE_PLUGIN_ROOT}/scripts/query-council.sh --list-default 2>&1 | head -1

If that lists one or more providers and the user did not ask for --local, STOP — do not spawn local members. Run the standard cross-vendor flow (council-execution) so the real providers answer, since same-model role-play is strictly weaker than the cross-vendor council the user can actually get.

Step 1: Resolve the roles

Source the libs once:

bash
source ${CLAUDE_PLUGIN_ROOT}/scripts/lib/prompts.sh
source ${CLAUDE_PLUGIN_ROOT}/scripts/lib/roles.sh

If the user passed --roles, it already says both which lenses and how many members — use it directly and skip the size question:

bash
local_council_roles "<--roles value>"

Otherwise, ask the user how many members to convene, then resolve that many:

AskUserQuestion:
  Question: "How many independent perspectives should the local council convene? More members means broader angle coverage, but a longer run and synthesis."
  Header: "Members"
  multiSelect: false
  Options:
    - "4 (Recommended) - balanced breadth"
    - "3 - quick, three sharpest lenses"
    - "5 - thorough"
    - "6 - exhaustive"

Then pass the chosen number as the count:

bash
local_council_roles "" "<chosen number>"

local_council_roles returns a comma-separated role list (e.g. devil,simplicity,security,scalability). Each role becomes one council member; N = number of roles. It draws from a diverse ordering and clamps to the 8 available roles — a user who wants more or specific lenses can pass --roles (e.g. --roles=security,devil,simplicity,scalability,dx,compliance). If you want to be safe, validate_roles "<list>" returns non-zero on an unknown role.

Step 2: Assemble the question + context

Build the text each member will answer:

  • The user's question.
  • Any file context gathered via --file or auto-context (the same context the cross-vendor flow would send). Include it inline so members can reason about it even though they will also read referenced files themselves.

Step 3: Spawn the members in parallel

For each role, build the role-injected prompt and spawn one member. See agent-prompt-template.md for the exact prompt and the build_prompt_with_role call.

Launch all members in a single message (multiple Agent tool calls) with:

  • subagent_type: "general-purpose"
  • run_in_background: true

The member framing (independent, blind to the others, commit to the assigned lens, return format) lives in the prompt itself — see the template. Launching them together runs them concurrently and keeps each one blind to the others.

Step 4: Collect results

You are notified automatically as each background member finishes. Wait for ALL of them before synthesizing. If a member fails or times out, note it and continue with the rest.

Step 5: Display each perspective

Lead with this banner verbatim (it sets honest expectations):

Local council — these perspectives all come from Claude playing different roles, not from different AI vendors. Treat agreement as a shared starting point to pressure-test, not as independent confirmation.

Then, for each member, display its perspective under a header naming the role (use get_role_name "<role>" for the display name):

## 🗳️ {ROLE_NAME}

{member's Position / Key points / Risks & blind spots / Confidence}

Step 6: Synthesize — angles, not consensus

Because members share a model, frame the synthesis around spread and coverage, NOT agreement strength:

  • Shared starting points — where members converged. Explicitly note this is a common prior to stress-test, not corroboration, and ask what they might all be missing for the same reason.
  • Genuine tensions — where the roles pull in different directions, and what the user's specific situation implies about which tension matters most here.
  • Blind spots — risks raised by one member that the others' approaches would walk into. Also flag anything no member covered.
  • Suggested direction — your best synthesized recommendation, naming which roles support it and where the real uncertainty remains.

Step 7: Save output

bash
mkdir -p .claude/council-cache

Write the full output (banner + all member perspectives + synthesis) to .claude/council-cache/local-council-{TIMESTAMP}.md where TIMESTAMP is the current Unix timestamp. Then tell the user:


Local council saved to .claude/council-cache/local-council-{TIMESTAMP}.md For cross-vendor perspectives, configure a provider key or install a CLI (/claude-council:status shows what's available).

Error Handling

  • If a member fails, show the failure and continue with the others.
  • If ALL members fail, report clearly and suggest retrying.
  • If only one role resolved, say so — a one-member "council" is just a single role-played answer, and the user should know that.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Local Council Execution AI skill do?

Runs a local council when no external AI providers are configured. Spawns N independent Claude subagents (one per role, blind to each other) that each answer the question from a single assigned lens, then synthesizes their perspectives. Invoked by the ask command via --local, or when the user accepts the local-council offer after no providers are found. This is a same-model (Claude-only) panel, not a cross-vendor council.

Why use Local Council Execution on TypingMind?

Because you install it once and use it with any model. Local Council Execution is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Local Council Execution in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hex/claude-council/tree/main/skills/local-council-execution. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Local Council Execution?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Local Council Execution?

As many as you like. As long as a model supports skills, you can use Local Council Execution with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Local Council Execution AI skill free?

Yes. It is published on GitHub by hex under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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