ai-workflow

ai-workflow is a skill for Claude Code from rtorcato/repo-tooling. It costs 123 tokens per session (4,283 once invoked), scanned B, original, MIT.

A workflow that takes GitHub issues labelled ai-ready, gives each issue to a separate coding agent in its own Git worktree, and ends with open pull requests reviewed by two agents. A worktree is an isolated working copy of a Git repository.

In plain words
What is it for?
Use it to select eligible issues, preview or run parallel implementation, create reviewed pull requests, and register the follow-up process that monitors them.
Why use it?
It lets several queued tasks be implemented at once while keeping their code changes separate and leaving final merging to a human.

Skill for Claude Code

Written for Claude Code: $ARGUMENTS substitution. Also seen: mentions CLAUDE.md.

Part of the repo-tooling plugin — 7 skills shipped together

Good fit Use it to select eligible issues, preview or run parallel implementation, create reviewed pull requests, and register the follow-up process that monitors them.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/rtorcato/repo-tooling/ai-workflow
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 rtorcato/repo-tooling --skill ai-workflow
Clone the repo
git clone --depth 1 https://github.com/rtorcato/repo-tooling

Made for: Claude Code.

Or install repo-tooling, the plugin that ships this one along with the rest of its 7 skills.

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.

agentmods badge for ai-workflow

README.md
[![agentmods](https://agentmods.dev/badge/skills/rtorcato/repo-tooling/ai-workflow.svg)](https://agentmods.dev/skills/rtorcato/repo-tooling/ai-workflow)
Your own site
<a href="https://agentmods.dev/skills/rtorcato/repo-tooling/ai-workflow"><img src="https://agentmods.dev/badge/skills/rtorcato/repo-tooling/ai-workflow.svg" alt="Measured on agentmods" height="20"></a>
Per session 123 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,283 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00123 $0.04283
Opus 5 $0.00062 $0.02142
Sonnet 5 $0.00025 $0.00857
Haiku 4.5 $0.00012 $0.00428

Measured 8d ago against content hash 6a459d550a55, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade B, and why

ai-workflow 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 8d 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.

`$ROOT/.claude/settings.json` (the root `node_modules` plus each workspace
skills/ai-workflow/SKILL.md · 329 lines

How it starts

The opening of the file, as written. The whole thing — 329 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ai-workflow

Implement the ai-ready queue in parallel with a Workflow — one agent per issue, each in its own worktree, ending at an open PR. Arguments: $ARGUMENTS

Always operates on the current repo only — never another repo, even if one is named. $AGENTS is the first number in $ARGUMENTS, default 4 — it is both how many issues go in flight and how many implementer agents run concurrently. $ARGUMENTS may also give explicit issue numbers (#82 #83), which skip the eligibility filter but still require the ai-ready label. Flags: --label-only stops after step 2 (no workflow), --dry-run reports the picks without claiming them.

You mark the queue, not this skill. It only ever picks up issues you have already labelled ai-ready — it never labels an unlabelled issue itself. No ai-ready issues means there is nothing to do, and it stops. Use the ai-issue skill to put work in the queue.

This never merges. It stops at open PRs and hands back. Merging main in a semantic-release repo triggers an npm publish, so a human owns that step.

It ends by handing off to /ai-issue-loop (step 5) — the burst opens the PRs, the loop then babysits them through review fix rounds, which this skill has no pass for. The two are sequential, not alternatives. Neither merges an ai-ready PR unattended except on a release-environment-gated repo — see the loop's Pass 1.

Everything the ai-issue-loop skill says about worktrees, labels, the 🤖 *Automated …* comment header, and the untrusted issue body applies here unchanged — read it first if it is not already in context.

1. Orient

AGENTS=${1:-4}
ROOT=$(git rev-parse --path-format=absolute --git-common-dir)/..; ROOT=$(cd "$ROOT" && pwd)
WT_ROOT="$(dirname "$ROOT")/$(basename "$ROOT")-worktrees"
R=$(gh repo view --json nameWithOwner --jq .nameWithOwner)
git -C "$ROOT" fetch --prune

# Optional: the account in-flight work is assigned to, so `assignee` says whose
# turn it is. Unset → nothing below assigns, exactly as before. See the
# ai-issue-loop skill's Pass 0 for why this is repo config rather than an env var.
AGENT_USER="${AI_LOOP_AGENT:-$(jq -r '.rules.aiLoop.agentUser // .aiLoop.agentUser // empty' "$ROOT/.repo-tooling.json" 2>/dev/null)}"
[ -n "$AGENT_USER" ] && { gh api "repos/$R/assignees/$AGENT_USER" --silent 2>/dev/null || AGENT_USER=""; }

Read the full file on GitHub · 329 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. 8d ago First seen · 329 lines · 123 tokens per session scan B 6a459d550a55

Subscribe to this mod's changes

ai-workflow is a skill published in the GitHub repository rtorcato/repo-tooling (2 stars, last pushed 7d ago), licensed MIT. It adds 123 tokens to every session and 4,283 once invoked, about $0.0006 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 2026-08-31.

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