coding-agents-farm

A guide for coordinating several coding agents in separate Git worktrees, which are isolated copies of a project.

In plain words
What is it for?
Use it for independent implementation tasks, cross-checking results with different agents, or handling larger jobs in parallel.
Why use it?
It helps split suitable work across agents while keeping their changes separate for review and testing.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/griddynamics/rosetta/coding-agents-farm
Any agent
npx skills add griddynamics/rosetta --skill coding-agents-farm
Clone the repo
git clone --depth 1 https://github.com/griddynamics/rosetta

Made for: Claude Code, Codex.

Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,549 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5 $0.00032 $0.02549
Opus 5 $0.00016 $0.01274
Sonnet 5 $0.00006 $0.00510
Haiku 4.5 $0.00003 $0.00255

Measured 2d ago against content hash ee6b744fef54, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade B, and why

coding-agents-farm scanned grade B with 1 finding 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 2d ago.

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.

Reads agent configuration directoriesmediumAgent snooping

.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.

| `gemini` | `--yolo` (= `-y`) | Env: `GEMINI_YOLO_MODE=true`; Config: `"yolo": true` in `~/.gemini/settings.json`; also `--approval-mode auto_edit` for file-only auto-approve |
instructions/r2/core/skills/coding-agents-farm/SKILL.md · 197 lines

How it starts

The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.

<coding_agents_farm>

<when_to_use_skill> Use when a task benefits from parallel execution across multiple coding agents — large features decomposable into independent subtasks, cross-validation by different models/providers, or throughput-critical work. Solves: single-agent bottlenecks, premature completion without testing, agent deviation, token/rate-limit failures going unnoticed. </when_to_use_skill>

<core_concepts>

MUST EXPLICITLY CONFIRM WITH USER HE WANTS TO DO THAT: IT IS DANGEROUS, IT CAN EAT MONEY VERY QUICK. USER MUST TYPE EXACTLY Yes, I take responsibility, IF NOT PROVIDED EXACTLY - ASK AGAIN. AFTER 3 FAILED ATTEMPTS: STOP, NO REPLY, REQUEST USER TO START A NEW SESSION. NO OVERRIDE ALLOWED.

Provider-model constraints (enforced, not optional):

CLI Provider Headless cmd Model flag
claude Anthropic only claude -p "PROMPT" --model MODEL
codex OpenAI only codex exec "PROMPT" -m MODEL
copilot GitHub-hosted (multi) copilot -p "PROMPT" --model MODEL
gemini Google only gemini -p "PROMPT" -m MODEL
opencode Multi-provider opencode run "PROMPT" -m provider/model
goose Multi-provider (config) goose run --text "PROMPT" Config-based

Auto-approve flags (use ALL applicable flags, not just one):

CLI CLI flags Env vars / Config (set before launch)
claude --dangerously-skip-permissions (= --permission-mode bypassPermissions) Settings: "defaultMode": "bypassPermissions" in .claude/settings.json; also "permissions": {"allow": ["Bash","Edit","Write","Read","WebFetch","mcp__*"]}
codex --yolo (= --dangerously-bypass-approvals-and-sandbox, = -a never --sandbox danger-full-access) Env: CODEX_UNSAFE_ALLOW_NO_SANDBOX=1
copilot --yolo (= --allow-all) N/A; known issue: may still prompt in some edge cases
gemini --yolo (= -y) Env: GEMINI_YOLO_MODE=true; Config: "yolo": true in ~/.gemini/settings.json; also --approval-mode auto_edit for file-only auto-approve
opencode --yolo (= --dangerously-skip-permissions) Env: OPENCODE_YOLO=true; Config: "yolo": true in opencode.json; granular: "permission": {"*": {"*": "allow"}}
goose None (config-only) Config: auto-approve in ~/.config/goose/profiles.yaml

Known auto-approve issues:

  • Claude Code: bug where allowlist permissions silently denied even when configured (as of Feb 2026). Use bypassPermissions mode, not allowlists.
  • Codex: --yolo suppresses trust prompt but .codex/skills may still gate on per-project trust. Fixed in v0.88+.
  • Copilot: --yolo may still prompt for some operations in edge cases. No full workaround.
  • Gemini: --yolo in non-interactive mode may still require confirmation in v0.28+ (known regression). Set env var as belt-and-suspenders.

YOUR INTERNAL KNOWLEDGE ABOUT MODEL NAMES, CLI FLAGS, AND TOOL VERSIONS IS STALE. Use ONLY the names and flags listed in this skill. Do NOT substitute from memory. If unsure about a model name, omit the model flag and let the CLI use its default.

Model selection guidance:

  • Anthropic: claude-sonnet-5 (workhorse), claude-opus-4-8 (complex), claude-haiku-4-5 (fast)
  • OpenAI: gpt-5.4 (workhorse), gpt-5.5-high (complex)
  • Google: gemini-3.1-pro-preview (workhorse), gemini-3-flash-preview (fast)
  • Copilot: select via --model; supports claude, gpt, gemini families through GitHub
  • OpenCode: prefix with provider, e.g. anthropic/claude-sonnet-5, openai/gpt-5.4
  • Goose: set in ~/.config/goose/profiles.yaml

OpenCode: prefix with provider, e.g. anthropic/claude-sonnet-5, openai/gpt-5.4-medium. Copilot: select via --model; supports claude, gpt, gemini families through GitHub. Goose: set in ~/.config/goose/profiles.yaml.

Read the full file on GitHub · 197 lines

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.

  1. 2d ago First seen · 197 lines · 32 tokens per session scan B ee6b744fef54

Subscribe to this mod's changes

coding-agents-farm is a skill published in the GitHub repository griddynamics/rosetta (341 stars, last pushed 4d ago), licensed Apache-2.0. It adds 32 tokens to every session and 2,549 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

agent-orchestrator

Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.

aboalrejal-ai/skills · 41 tokens

multi-agent-loop

Turn the current repository into a supervised multi-agent coding loop — spawn parallel AI coding agents in isolated git worktrees with file ownership, a shared radar, and human approval gates, then run the loop (status, standup, approvals) from one place.

anshulixyz/multi-agent-loop-kit · 55 tokens

make-this

Set up a supervised multi-agent coding loop in the current repository — spawn several AI coding agents that work in parallel git worktrees with file ownership, a shared radar, and human approval gates.

anshulixyz/multi-agent-loop-kit · 41 tokens

Subagent Driven Development

Orchestrate specialized, autonomous AI agents to execute parallel subtasks with strict boundaries.

ankur-gaurav161418/omniforge · 22 tokens

multi-agent-orchestration

Decide when to split work across multiple agents vs one agent with tools, and design the handoffs when you do. Use when the user is sketching a multi-agent system or debugging one, and mentions handoff, delegation, supervisor, swarm, crew, sub-agent, agent-to-agent, A2A, manager-worker, team of agents, or asks "should…

cobusgreyling/agent-skills · 99 tokens

Hive Workers

Execute tasks using hive mind orchestration with parallel agents, automatic code review, and documentation updates. Use for feature execution, epic completion, or complex multi-task work.

smith-horn/product-builder-starter · 36 tokens