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.
npx skills add rtorcato/repo-tooling --skill ai-workflowgit clone --depth 1 https://github.com/rtorcato/repo-toolingWrote 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.
[](https://agentmods.dev/skills/rtorcato/repo-tooling/ai-workflow)<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>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.
| Model | Per session | Once 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 | $0.00428 |
Grade B, and why
ai-workflow scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
`$ROOT/.claude/settings.json` (the root `node_modules` plus each workspace 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=""; }
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.
- 8d ago First seen · 329 lines · 123 tokens per session scan B 6a459d550a55
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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