llm-council
Orchestrate a configurable, multi-member CLI planning council (Codex, Claude Code, Gemini, OpenCode, or custom) to produce independent implementation plans, anonymize and randomize them, then judge and merge into one final plan. Use when you need a robust, bias-resistant planning workflow, structured JSON outputs, retries, and failure handling across multiple CLI agents.
83
78%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/llm-council/SKILL.mdQuality
Discovery
85%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong description that clearly articulates a unique, specific workflow involving multi-agent planning councils with anonymization and merging. It includes both 'what' and 'when' clauses with explicit triggers. The main weakness is that trigger terms lean toward specialized jargon that users may not naturally use when requesting this capability.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: orchestrate a multi-member CLI planning council, produce independent implementation plans, anonymize and randomize them, judge and merge into one final plan. Also mentions structured JSON outputs, retries, and failure handling. | 3 / 3 |
Completeness | Clearly answers both 'what' (orchestrate a configurable multi-member CLI planning council to produce, anonymize, judge, and merge plans) and 'when' (explicit 'Use when you need a robust, bias-resistant planning workflow, structured JSON outputs, retries, and failure handling across multiple CLI agents'). | 3 / 3 |
Trigger Term Quality | Includes some relevant keywords like 'planning council', 'Codex', 'Claude Code', 'Gemini', 'OpenCode', 'implementation plans', and 'CLI agents'. However, these are fairly specialized terms; a user might more naturally say 'plan comparison', 'multi-agent planning', or 'council of experts' — some common variations are missing. | 2 / 3 |
Distinctiveness Conflict Risk | Highly distinctive — the concept of a multi-member CLI planning council with anonymization, randomization, and merging is a very specific niche. It names specific tools (Codex, Claude Code, Gemini, OpenCode) and a unique workflow pattern, making it unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid skill with strong actionability through concrete commands and a complete JSON configuration example, and good progressive disclosure via clearly signaled reference files. The main weaknesses are some redundancy between sections (session management, intake questions) and workflow validation gaps—specifically lacking explicit verification of judge/final-plan output quality and concrete criteria for Markdown structure validation.
Suggestions
Remove duplicated guidance: consolidate session management instructions (currently in both Workflow step 7 and Constraints) and intake question instructions (in both Quick Start and Workflow step 2) into single locations.
Add explicit validation criteria for step 4's 'validate Markdown structure'—specify what constitutes valid structure (e.g., required headings, sections) and what specific errors trigger retries vs. user alerts.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient but has some redundancy—session management instructions are repeated in both the Workflow and Constraints sections, and the intake question guidance appears in both Quick Start and Workflow. Some phrasing could be tightened (e.g., 'Tell the user that answering intake questions is optional' is repeated). | 2 / 3 |
Actionability | Provides concrete CLI commands (`python3 scripts/llm_council.py run --spec /path/to/spec.json`, `./setup.sh`, `python3 scripts/llm_council.py configure`), a complete JSON configuration example with multiple agent types, and specific file paths for outputs. The guidance is specific and directly executable. | 3 / 3 |
Workflow Clarity | The 7-step workflow is clearly sequenced and includes retry logic (step 4) and failure handling. However, validation checkpoints are limited—there's no explicit verification that the judge output or final-plan.md are well-formed before declaring success, and the 'validate Markdown structure' step lacks specifics on what constitutes valid structure or how to handle partial failures. | 2 / 3 |
Progressive Disclosure | The skill has a clear overview structure with well-signaled one-level-deep references to architecture, prompt templates, plan templates, CLI notes, and a task spec example. Content is appropriately split between the main skill file and reference documents. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- am-will/codex-skills
- Commit
633b7e0
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.