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ai-loop-status

ai-loop-status is a skill for Claude Code from rtorcato/repo-tooling. It costs 93 tokens per session (1,508 once invoked), scanned B, original, MIT.

A read-only view of the current ai-issue-loop work in a GitHub repository, including issues and pull requests marked with workflow labels.

In plain words
What is it for?
Use it to check the loop's current status, inspect pending work, or see what needs attention. It can check the current repository or a specified GitHub repository.
Why use it?
It shows whether automated work is active, blocked, or waiting without changing the repository.

Skill for Claude Code

Written for Claude Code: $ARGUMENTS substitution.

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

not rated 2repo 7d ago B scan 93 tokens original MIT

Good fit Use it to check the loop's current status, inspect pending work, or see what needs attention. It can check the current repository or a specified GitHub repository.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/rtorcato/repo-tooling/ai-loop-status
View source ↗ rtorcato/repo-tooling rtorcato.github.io
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-loop-status
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-loop-status

README.md
[![agentmods](https://agentmods.dev/badge/skills/rtorcato/repo-tooling/ai-loop-status.svg)](https://agentmods.dev/skills/rtorcato/repo-tooling/ai-loop-status)
Your own site
<a href="https://agentmods.dev/skills/rtorcato/repo-tooling/ai-loop-status"><img src="https://agentmods.dev/badge/skills/rtorcato/repo-tooling/ai-loop-status.svg" alt="Measured on agentmods" height="20"></a>
Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,508 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 $0.00093 $0.01508
Opus 5 $0.00046 $0.00754
Sonnet 5 $0.00019 $0.00302
Haiku 4.5 $0.00009 $0.00151

Measured 7d ago against content hash 563e0aea9e26, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade B, and why

ai-loop-status 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 7d 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.

find "$(dirname "$ROOT")/$(basename "$ROOT")-worktrees" "$ROOT/.claude/worktrees" \
skills/ai-loop-status/SKILL.md · 123 lines

How it starts

The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ai-loop-status

Show what the ai-issue-loop pipeline is doing right now. Arguments: $ARGUMENTS

Read-only — this never applies a label, merges a PR, or spawns an agent. To actually advance the pipeline, run /ai-issue-loop. Because it is read-only, it is the one loop tool allowed to point at another repo via an owner/repo argument.

Steps

  1. Resolve the repo — if $ARGUMENTS names one (owner/repo), use it; otherwise the current directory's. GitHub only — bail in one line if the remote is GitLab:

    R=${ARG:-$(gh repo view --json nameWithOwner --jq .nameWithOwner)}

  2. Read the pipeline state from labels. The loop keeps no state anywhere else, so these queries are the ground truth even after a crash, a restart, or a missed tick:

    gh issue list -R "$R" --state open --label ai-wip       --json number,title
gh pr    list -R "$R" --state open --json number,title,labels,autoMergeRequest,assignees
gh issue list -R "$R" --state open --label ai-ready     --json number,title
gh issue list -R "$R" --state open --label ai-blocked   --json number,title
gh issue list -R "$R" --state open --label ai-suggested --json number,title

    Filter the PR list to those carrying an ai-* label — a PR without one is not in the pipeline and the loop will never touch it.

    Read the assignee as "whose turn", when the loop is configured with an agent account (AI_LOOP_AGENT): that account assigned means an agent is working or reviewing, the human assigned means it is waiting on them, and nobody assigned means queued. Say which in the report rather than listing raw logins — "waiting on you" beats "assignee: someone".

  3. Work out each PR's next move from its labels, so the report says what happens rather than just listing state:

    • ai-review alone → waiting on reviewers; name which arm is outstanding (ai-ok-code missing → code-reviewer, ai-ok-sec missing → security-expert), and whether it is claimed (ai-reviewing-code / ai-reviewing-sec mean a reviewer is running right now)
    • merge-ready (or, before the label reaches a repo, both ai-ok-* with no ai-review) → waiting on the human to merge; add "read the comments first" when ai-notes rides along. Only Dependabot PRs — or issue PRs on a repo whose release environment has required_reviewers — auto-merge.
    • autoMergeRequest set → queued; GitHub is holding it for required checks
    • ai-changes → a fix round is due. Count prior rounds, because the 3rd one stops the loop and marks the issue ai-blocked:

Read the full file on GitHub · 123 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. 7d ago First seen · 123 lines · 93 tokens per session scan B 563e0aea9e26

Subscribe to this mod's changes

ai-loop-status is a skill published in the GitHub repository rtorcato/repo-tooling (2 stars, last pushed 7d ago), licensed MIT. It adds 93 tokens to every session and 1,508 once invoked, about $0.0005 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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