A planning process that asks several configured coding agents to create separate implementation plans, then combines and reviews those plans into one. The agents can include Codex, Claude Code, Gemini, OpenCode, or custom tools.

Use it to plan complex implementation work, compare proposed approaches, and produce an auditable final plan saved with its run files.

It reduces dependence on one agent's assumptions when a project needs careful planning. Independent plans and anonymous review make disagreements easier to identify.

llm-council — Skill for Claude Code and Codex by a-tokyo

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add a-tokyo/agent-skills-harness --skill llm-council

Clone the repo

git clone --depth 1 https://github.com/a-tokyo/agent-skills-harness

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/a-tokyo/agent-skills-harness/llm-council/github.svg)](https://agentmods.dev/skills/a-tokyo/agent-skills-harness/llm-council)

Your own site

<a href="https://agentmods.dev/skills/a-tokyo/agent-skills-harness/llm-council"><img src="https://agentmods.dev/badge/skills/a-tokyo/agent-skills-harness/llm-council/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/a-tokyo/agent-skills-harness/llm-council"><img src="https://agentmods.dev/badge/skills/a-tokyo/agent-skills-harness/llm-council.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,168 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00080	$0.01168
Opus 5	$0.00040	$0.00584
Sonnet 5	$0.00016	$0.00234
Haiku 4.5	$0.00008	$0.00117

Measured 12d ago against content hash dc44faafab9b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

llm-council scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 7 executable files (scripts/llm_council.py, scripts/ui_server.py, scripts/ui_state.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

llm-council — 88% identical, 9 lines differ

.agents/skills/llm-council/SKILL.md · 69 lines

How it starts

The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LLM Council Skill

Quick start

Always check for an existing agents config file first ($XDG_CONFIG_HOME/llm-council/agents.json or ~/.config/llm-council/agents.json). If none exists, tell the user to run ./setup.sh to configure or update agents.
The orchestrator must always ask thorough intake questions first, then generates prompts so planners do not ask questions.
- Even if the initial prompt is strong, ask at least a few clarifying questions about ambiguities, constraints, and success criteria.
Tell the user that answering intake questions is optional, but more detail improves the quality of the final plan.
Use python3 scripts/llm_council.py run --spec /path/to/spec.json to run the council.
Plans are produced as Markdown files for auditability.
Run artifacts are saved under ./llm-council/runs/<timestamp> relative to the current working directory.
Configure defaults interactively with python3 scripts/llm_council.py configure (writes $XDG_CONFIG_HOME/llm-council/agents.json or ~/.config/llm-council/agents.json).

Workflow

Load the task spec, and explore the codebase you are in to get a strong sense of the product.
Always ask thorough intake questions to build a clear task brief. Clarify any ambiguities, constraints, and success criteria. Remind the user that answers are optional but improve plan quality.
Build planner prompts (Markdown template) and launch the configured planner agents in parallel background shells.
Collect outputs, validate Markdown structure, and retry up to 2 times on failure. If any agents fails, yield and alert the user to fix the issue.
Anonymize plan contents and randomize order before judging.
Run the judge with the rubric and Markdown template, then save judge.md and final-plan.md.
Session management: DO NOT yield/finish the response until a full 30-minute timer has completed and judge + final-plan.md are confirmed saved; keep the session open during that interval to avoid closing the interface. If you yield while the Council is running, the session will be terminated and you will FAIL to complete the task. The user will escape out when they are ready or after the 30 minutes have elapsed.
- Note on Session Management: Plans can take quite some time to build, do not panic if it seems stuck. You do not need to poll every few seconds. Once every 20-30 seconds is sufficient. Continue to allow them as much time as needed up to the 30-minute mark.

Read the full file on GitHub · 69 lines

Files

What ships with it

21 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

12d ago First seen · 69 lines · 80 tokens per session scan A dc44faafab9b

Subscribe to this mod's changes

llm-council is a skill published in the GitHub repository a-tokyo/agent-skills-harness (10 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 1,168 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Building agents Planning agent-skills agentic-engineering ai ai-loop autoresearch claude

arbor-agent-executor

Executor-dispatch phase for Arbor. Use when implementing an Idea Tree node through RunExecutor or RunExecutorParallel semantics: isolated git worktree, executor prompt construction, eval metadata injection, RunTraining policy, smoke/full evaluation, report parsing, artifact persistence, tree update, and insight…

RUC-NLPIR/Arbor · 63 tokens

arbor-agent-ideate

Strict IDEATE-stage skill for Arbor. Use immediately after TreeView(format="constraints") when drafting Idea Tree nodes, enforcing the ideadrafting and firstprinciplesprobe behavior, depth-aware idea levels, four-line TreeAddNode hypotheses, conflict checks, and self-filtering against shallow tweaks.

RUC-NLPIR/Arbor · 66 tokens

grid-ctf-ops

Operational knowledge for the gridctf scenario including strategy playbook, lessons learned, and resource references. Use when generating, evaluating, coaching, or debugging gridctf strategies.

greyhaven-ai/autocontext · 42 tokens

codex-autoresearch

Run autonomous, measurable experiments in a Git repository: change one hypothesis, verify a numeric metric, keep improvements, and revert failures. Use when the user wants Codex to keep iterating toward a numeric target in the foreground or as a detached background run. Do not use for ordinary one-shot coding…

leo-lilinxiao/codex-autoresearch · 80 tokens

baseline-comparison-audit

Audit whether a paper's baseline comparisons are COMPLETE, FAIR, and SIGNIFICANT: a required recent SOTA baseline is missing while 'best/SOTA' is claimed (HP-MISSING-BASELINE); a baseline is undertuned / given less compute-tuning-data, run at a mismatched config, or the equal-budget ablation-as-baseline is absent…

wanshuiyin/Anti-Autoresearch · 310 tokens

proof-derivation-forensics

Family-G proof & derivation integrity forensics: does a THIRD PARTY's written proof/derivation actually establish its theorem, or does it skip an obligation, assume its own conclusion, take an invalid step, drift a symbol's meaning, or smuggle an unstated assumption? Decides from the WRITTEN proof/derivation …

wanshuiyin/Anti-Autoresearch · 222 tokens