/cross-agent-plan
Get an independent implementation plan from a DIFFERENT AI agent over the repowire mesh before you build (e.g. have Gemini or Codex draft an approach for Claude to critique). Use when you want a second perspective on how to approach a task.
$ npx -y skills add prassanna-ravishankar/repowire --skill cross-agent-plan --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/cross-agent-plan
Context preview
The summary Claude sees to decide when to auto-load this skill.
Get an independent implementation plan from a DIFFERENT AI agent over the repowire mesh before you build (e.g. have Gemini or Codex draft an approach for Claude to critique). Use when you want a second perspective on how to approach a task.
SKILL.md
cross-agent-plan.SKILL.mdname: cross-agent-plan
description: Get an independent implementation plan from a DIFFERENT AI agent over the repowire mesh before you build (e.g. have Gemini or Codex draft an approach for Claude to critique). Use when you want a second perspective on how to approach a task.
Cross-agent plan
Ask a peer running a **different** agent backend to draft (or critique) a plan, so the planning perspective is genuinely independent.
Resolve the planner backend
1. **Explicit argument** — if the user named a backend, use it. 2. **Configured default** — else:
repowire config get skills.default_planner_backend
(Empty output means unset.) 3. **Safe fallback** — else pick an online peer on a backend *different from yours*, or ask the user. Never default to your own backend; never hardcode one.
Discover peers/backends: `list_peers()` (MCP) or `repowire peer list` (CLI).
Run the planning round
1. Find (or, with the user's go-ahead, spawn — see `delegate`) a peer on the chosen backend. 2. Send the task + constraints and ask for a concrete step-by-step plan.
- MCP: `ask(peer_name, "Draft an implementation plan for: <task + constraints>")`
- Tracked ask is MCP-only (no CLI lifecycle equivalent — `repowire peer ask` is
a synchronous test utility, not this). 3. `ask` returns a `correlation_id`; the peer replies via `ack(corr_id, <plan>)`. 4. Critique/merge the returned plan with your own; chain follow-ups with `ask(reply_to=corr_id, ...)`.
Keep the brief tight — state the goal, the constraints, and what a good plan must cover, so the cross-agent plan is comparable to your own.
Read more
name: cross-agent-plan description: Get an independent implementation plan from a DIFFERENT AI agent over the repowire mesh before you build (e.g. have Gemini or Codex draft an approach for Claude to critique). Use when you want a second perspective on how to approach a task.
Cross-agent plan
Ask a peer running a **different** agent backend to draft (or critique) a plan, so the planning perspective is genuinely independent.
Resolve the planner backend
1. **Explicit argument** — if the user named a backend, use it. 2. **Configured default** — else:
repowire config get skills.default_planner_backend
(Empty output means unset.) 3. **Safe fallback** — else pick an online peer on a backend *different from yours*, or ask the user. Never default to your own backend; never hardcode one.
Discover peers/backends: `list_peers()` (MCP) or `repowire peer list` (CLI).
Run the planning round
1. Find (or, with the user's go-ahead, spawn — see `delegate`) a peer on the chosen backend. 2. Send the task + constraints and ask for a concrete step-by-step plan.
- MCP: `ask(peer_name, "Draft an implementation plan for: <task + constraints>")`
- Tracked ask is MCP-only (no CLI lifecycle equivalent — `repowire peer ask` is
a synchronous test utility, not this). 3. `ask` returns a `correlation_id`; the peer replies via `ack(corr_id, <plan>)`. 4. Critique/merge the returned plan with your own; chain follow-ups with `ask(reply_to=corr_id, ...)`.
Keep the brief tight — state the goal, the constraints, and what a good plan must cover, so the cross-agent plan is comparable to your own.
May the agents talk . Connect Claude Code, Opencode, Codex, Antigravity, Pi across projects, across machines and with your telegram
Repo: prassanna-ravishankar/repowire
Other skills on repowire.
- /integration-test
Integration test for repowire peer-to-peer messaging. Supports claude-code, opencode, or mixed-agent-type testing with circle boundaries and cross-agent-type communication. Can run all modes in parallel via agent teams.
Open skill - /cross-agent-review
Get a code/PR/plan review from a DIFFERENT AI agent over the repowire mesh (e.g. have Codex review Claude's work). Use when you want an independent second opinion from another backend before merging or committing.
Open skill - /delegate
Hand a task to another AI agent peer over the repowire mesh — reuse an existing peer on the chosen backend, or spawn one if none exists. Use when you want to offload work to a specific agent (e.g. delegate a refactor to a Codex peer) and track it to completion.
Open skill - /repowire-install
Install or update repowire and its skill pack from inside an agent session — ensure the repowire CLI/hooks are set up, install the skills, and verify the mesh works. Use on a fresh machine or to refresh skills to the latest.
Open skill - /repowire-patterns
Reference for how to use the repowire mesh — ask/ack vs notify, broadcast, peer discovery, spawning, and cross-agent workflows. Use when you need to coordinate with other AI agents over repowire and want the right primitive for the job.
Open skill