Agent skill

Panel

by raine in raine/consult-llm

Role-specialized LLM panel analyzes a task from asymmetric expert lenses (architect, security, maintainability, test-strategist by default).

MITAuto-check: notesAgent Workflows

Install Panel

skills CLI
$ npx skills add raine/consult-llm --skill panel -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install raine/consult-llm panel --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/raine/consult-llm.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/panel .claude/skills/panel && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
panel
GitHub stars
139
Token cost
~3.1k tokens
SKILL.md length
1,316 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
MIT

At a glance

Role-specialized LLM panel analyzes a task from asymmetric expert lenses (architect, security, maintainability, test-strategist by default).

  • Works in 6 steps: Load consult-llm skill → Gather shared context → Round 1 — parallel panel → …
  • Agent Workflows work in your project
  • SKILL.md covers Available models, Argument handling, Phase 0: Load consult-llm skill and Phase 1: Gather shared context, plus 6 more sections
  • Calls git

What it does

Panel is an agent skill from raine/consult-llm. Role-specialized LLM panel analyzes a task from asymmetric expert lenses (architect, security, maintainability, test-strategist by default). Agent synthesizes a trade-off resolution.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows. It works with React. The repository describes itself as: Get a second opinion from another AI model. The licence is MIT.

When your agent uses it

  • Agent Workflows work in your project

Example prompts

  • “/panel”

Requirements

  • Pre-approved tools (allowed-tools): Bash, Glob, Grep, Read

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Load consult-llm skill
  2. Gather shared context
  3. Round 1 — parallel panel
  4. React round (only with --react)
  5. Synthesize trade-offs
  6. Save and report

What it can do on your machine

Read from SKILL.md and the folder at commit 69e3ecb. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Glob
    • Grep
    • Read

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Panel loads about 3.1k tokens when it runs. Until then it costs about 47 tokens; SKILL.md has 1,316 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~47
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Glob, Grep, Read

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from raine/consult-llm at commit 69e3ecb, republished under its MIT licence (© raine). 1,316 words, ~3,080 tokens.

Download SKILL.mdSave it as .claude/skills/panel/SKILL.md (or your agent's skills folder).
name
panel
description
Role-specialized LLM panel analyzes a task from asymmetric expert lenses (architect, security, maintainability, test-strategist by default). Agent synthesizes a trade-off resolution.
allowed-tools
Bash, Glob, Grep, Read

Run a role-asymmetric advisory panel. Each role analyzes the same task from a single expert lens; the agent synthesizes a PM-style trade-off resolution. Use this when a decision spans multiple domains that pull in different directions and one of them shouldn't silently win. For peer-style brainstorming with no role separation, use /collab. For multi-model review of an existing diff with identical prompts, use /review-panel.

Load the consult-llm skill before proceeding — it defines the invocation contract (heredocs, timeouts, --run, prompt files, thread IDs). Do not call the CLI without loading it first.

Available models

Selectors resolvable in this environment (depends on configured API keys):

!`consult-llm models`

Argument handling

Arguments: $ARGUMENTS

Check $ARGUMENTS for flags:

Role selection:

  • --roles <comma-separated> — explicit kebab-case role list (e.g. --roles security,api-design,ops-readiness). Trim whitespace; reject duplicates and empty entries.
  • (none) — the agent picks the roles based on the task focus and the Phase 1 context. Default starting point is architect, security, maintainability, test-strategist; swap individual roles to fit the actual work. See "Example role bundles" near the bottom of this file as a reference. Pick 3–5 roles; do not exceed 5.

After picking, show the chosen roles to the user before Round 1 so they can override with --roles if the inference is off (e.g. "Roles: migration-architect, security, data-integrity, rollback-operator. Override with --roles if needed.").

Mode flags:

  • --mode design|review — default design.
    • design is a forward-looking proposal panel. Uses --task plan internally.
    • review is a critical pass on an existing diff. Uses --task review internally and adds --diff-files/--diff-base.
  • --diff-base <ref> — review mode only. Default is auto-detected (see Phase 1). Pass an explicit ref to override.
  • --react — opt in to a second round where each role responds to the agent's draft synthesis on its own thread.

Model assignment: any --<selector> from the Models block selects a backing model. Repeat for multiple. With no selectors, use all listed selectors in their listed order. Roles map to selectors positionally — first role to first selector, etc. This workflow requires each role to end up on a distinct resolved model; enforce that before invoking --run.

Strip all flags; the remainder is the panel focus. In design mode, treat it as the proposal/decision under analysis. In review mode, treat it as optional review focus.

Fail-fast preconditions

Stop before Round 1 with a clear error if any of these fail:

  • Role count > 5 (--run max).
  • Role count exceeds the number of distinct selected/available models.
  • Selectors resolve to duplicate underlying models.

Error format:

panel: <N> roles requested, only <M> distinct models available.
Roles:     <role list>
Selectors: <selector list>
Either pick fewer roles (--roles ...) or pass distinct --<selector> flags.

Phase 0: Load consult-llm skill

Load it now. Follow its invocation contract for every CLI call.

Phase 1: Gather shared context

Build a context summary that every role sees verbatim. Reasonable assumptions only — do not ask the user clarifying questions.

Both modes:

  • Use Glob/Grep/Read to find files, patterns, conventions, constraints related to the focus.
  • Before planning or consulting, do enough research to understand how the requested behavior actually works. Before starting, think about what resources would be useful to obtain first: relevant source files, tests, logs, generated files, config, examples, command output, external docs, or authoritative upstream source. Gather the cheapest useful evidence before forming a plan.
  • Do not stop at the first plausible file, definition, setting, or example. Follow references, callers, related tests, and runtime usage until you can explain the current behavior and the likely impact of changing it.
  • Note compatibility requirements, security boundaries, deployment concerns, prior decisions, known unknowns.
  • Exclude generated files, lockfiles, vendored dependencies unless central.

Design mode — pass relevant source files as shared -f <path> to the panel call.

Review mode — resolve <diff-base> using the same logic as review-panel/SKILL.md Phase 1 (prefer @{upstream}, fall back to merge-base with the detected main branch, fall back to HEAD). Show the resolved base to the user before running the panel.

List changed files:

bash
git diff --name-only --diff-filter=d <diff-base>
git ls-files --others --exclude-standard

Pass tracked changed files as shared --diff-files <path> with --diff-base <ref>. Pass untracked files as -f <path> so roles see full contents. Exclude binary files and lockfiles. If there is no diff, stop and report nothing to review.

Prepare the shared context summary as plain markdown — focus, mode, key files, constraints, known unknowns.

Phase 2: Round 1 — parallel panel

Write one prompt file per role with mktemp. Each file contains the persona framing, the panel focus, and the shared context summary.

Persona prompt — design mode:

You are the {role} specialist on an advisory panel. Speak only from your assigned perspective. Do not assume other roles' responsibilities.

Mode: design — analyze this proposed work before implementation.

Panel focus:
[focus]

Shared context:
[context summary]

Output exactly these sections:
1) Key observations — what stands out from your domain's viewpoint?
2) Risks / opportunities — what could go wrong or be improved?
3) Non-negotiable requirements — what must be true (or must not be true) for the solution to be acceptable from your perspective?
4) Open questions for other roles — what do you need from other specialists?

Acknowledge conflicts with other perspectives but do not resolve them — that is the agent's job.

Persona prompt — review mode:

Same structure, but framing changes to "critical pass on an existing diff". Replace section 1 with "what stands out when reading this diff (cite file:line where possible)" and section 2 with "concrete defects, regressions, safety issues, compatibility breaks, missing tests from your domain". Add: "Do not propose fixes. Do not soften findings."

Invoke consult-llm once with one --run per role. For each run pass model=<selector>,prompt-file=<tempfile>. Add --task plan for design mode or --task review --diff-base <ref> for review mode. Do not pass -m alongside --run.

Save the per-role thread IDs from each section header ([thread_id:<id>]) keyed by role name — needed for --react and the final artifact.

Show full SKILL.md (502 more words)Show less

Phase 3: React round (only with --react)

Draft a provisional synthesis using the Phase 4 structure. This draft is internal; do not show the user yet.

Write one continuation prompt file per role and invoke consult-llm again with one --run per role, passing model=<selector>,thread=<role-thread-id>,prompt-file=<tempfile>. Keep the same shared -f/--diff-* context.

Reaction prompt template:

The agent has drafted this synthesis from the Round 1 panel:

[draft synthesis]

From your assigned {role} perspective:
1) Are your domain concerns adequately addressed? If not, what is missing?
2) What is truly non-negotiable — a hard requirement you would block on?
3) Which trade-offs do you reject? Be specific about why.
4) Any disagreement that should remain unresolved?

Do not resolve cross-role conflicts. Flag them for the agent.

Update the synthesis using the reactions. New non-negotiables and rejected trade-offs go into the final artifact; if a role rejects on a domain-critical point, either resolve it explicitly or preserve it under unresolved disagreements.

Phase 4: Synthesize trade-offs

The agent owns the resolution. The roles advise; you decide.

Resolution rules:

  • Defer to security on safety conflicts: auth, data exposure, integrity, destructive actions, privacy, abuse, supply chain risk. If security (or any equivalent role) flags a concrete risk, the resolution must neutralize it.
  • Prefer simpler when trade-offs balance.
  • Preserve unresolved disagreements explicitly — do not fake consensus. If you cannot resolve a conflict with high confidence, name it as Unresolved and state what input would settle it.
  • If the final recommendation accepts a risk, name the risk and the reason it's acceptable.
  • If the recommendation depends on later validation, make that dependency explicit.

Phase 5: Save and report

Save the synthesis to history/<YYYY-MM-DD>-panel-<topic>.md (the history/ convention from CLAUDE.md). Derive <topic> from the current branch name (sanitized to kebab-case); fall back to a short slug from the panel focus when on the main branch or detached HEAD. Print the saved path.

Artifact template:

markdown
# Panel: <topic>

**Mode:** design|review
**Roles:** <role> (<selector>), <role> (<selector>)
**Diff base:** <ref>   _(review mode only)_

## Panel summary

- **<role>:** one-bullet summary of this role's position.
- **<role>:** one-bullet summary.

## Areas of agreement

- <point of multi-role consensus>

## Conflicts and resolutions

### <short conflict title>
- **<role A>** said: <concise paraphrase or quote>
- **<role B>** said: <position>

**Resolution:** <agent decision>. **Reasoning:** <why this trade-off wins, citing the resolution rules>.

## Unresolved disagreements

- **<issue>:** <roles involved>. <what remains unresolved; what decision or input would resolve it>.
- If none, write `None`.

## Final recommendation

<One paragraph: chosen direction, required constraints, rejected alternatives, validation needed before implementation or merge.>

## Thread map

- **<role>:** `<selector>` / `<thread_id>`

The thread map lets a follow-up --react run or manual consult-llm -t <id> continue any role's conversation later.

Print the saved path and a short final-recommendation summary to the user.

Critical rules

  • Strict asymmetry. Each role gets only its own persona prompt. Never leak other roles' prompts. Roles drifting into general commentary weaken the panel — enforce focus through the persona framing.
  • Distinct models per role. Validate this workflow's distinct-model requirement before invoking --run. Fail fast with the error format above.
  • Mode → task mapping. design ⇒ --task plan. review ⇒ --task review plus --diff-files/--diff-base.
  • --react continues threads. Do not start fresh threads for the reaction round.
  • Defer to security on safety conflicts. Prefer simpler when trade-offs balance.
  • Preserve unresolved disagreements. No fake consensus.
  • Advisory only. The panel produces a report. Do not modify source files or commit as part of this skill — hand the artifact back to the user.

Example role bundles

Reference starting points when the agent is picking roles. Use them as inspiration, not canonical sets — swap individual roles to fit the actual task. Each bundle is 4 roles; trim or extend within the 3–5 range.

DomainRoles
General (default)architect, security, maintainability, test-strategist
Frontendfrontend-architect, accessibility, performance, design-system-maintainer
Backendbackend-architect, security, reliability, data-integrity
Migration / data movemigration-architect, data-integrity, rollback-operator, compatibility
Library / public APIapi-designer, backward-compatibility, documentation, test-strategist
Infra / deployplatform-architect, reliability, observability, cost
ML / data pipelinedata-engineer, ml-architect, reproducibility, evaluation

A role label is just a kebab-case persona handed to the LLM — no validation, no special casing. Coin new ones freely (e.g. i18n, bundle-size, fuzzing-strategist) when the task warrants.

© raine, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/panel of raine/consult-llm.

Open the folder on GitHubat commit 69e3ecb

Compare with similar skills

Panel next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Plate Plugin Creatorudecode/plate17k—~2.3kAutomated safety check: PassCustom licence
Chatgpt App Builderalpic-ai/skybridge2.1k—~1kAutomated safety check: PassMIT
Spec Driven Developzhu1090093659/deepseek-pp1.9k—~6.9kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Panel

What does Panel do?

Role-specialized LLM panel analyzes a task from asymmetric expert lenses (architect, security, maintainability, test-strategist by default). Panel is an agent skill from raine/consult-llm. Role-specialized LLM panel analyzes a task from asymmetric expert lenses (architect, security, maintainability, test-strategist by default).

When should I use Panel?

Panel fits situations like: agent Workflows work in your project.

How do I install Panel in Claude Code?

Run `npx skills add raine/consult-llm --skill panel -a claude-code`. Or copy the skill folder (skills/panel in raine/consult-llm) into .claude/skills/panel in your project. Claude Code loads it when a task matches its description.

How do I install Panel in Codex?

Run `npx skills add raine/consult-llm --skill panel -a codex`. Or copy the skill folder (skills/panel in raine/consult-llm) into .agents/skills/panel in your project. Codex loads it when a task matches its description.

Can I use Panel in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add raine/consult-llm --skill panel -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/panel, .gemini/skills/panel, .github/skills/panel and .opencode/skills/panel in your project.

What does Panel need to run?

Going by SKILL.md and its folder, Panel needs the command-line tools its instructions call (git). Its frontmatter pre-approves these tools: Bash, Glob, Grep, Read.

Does Panel access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Panel safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Panel use?

Panel is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Panel use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Panel?

Skills that share tags, products or a category with Panel: MCP Server Builder with mcp-use (mcp-use/mcp-use, 11k stars), Manage Skills Hub (qufei1993/skills-hub, 1.7k stars), Plate Plugin Creator (udecode/plate, 17k stars) and Chatgpt App Builder (alpic-ai/skybridge, 2.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Panel?

raine (a GitHub user) maintains it in raine/consult-llm, which has 139 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 7, 2026.

Source: raine/consult-llm on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.