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 Abilityai/abilities --skill work-loopgit clone --depth 1 https://github.com/Abilityai/abilitiesWrote 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/abilityai/abilities/work-loop)<a href="https://agentmods.dev/skills/abilityai/abilities/work-loop"><img src="https://agentmods.dev/badge/skills/abilityai/abilities/work-loop/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/abilityai/abilities/work-loop"><img src="https://agentmods.dev/badge/skills/abilityai/abilities/work-loop.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00031 | $0.01409 |
| Opus 5.5 | $0.00012 | $0.00564 |
| Sonnet 5.5 | $0.00006 | $0.00282 |
| Haiku 4.5 | $0.00003 | $0.00141 |
Grade A, and why
work-loop scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Work Loop
ℹ️ First, set expectations: before anything else, print one short line with this skill's version and its most recent change — the top entry of
metadata.changelogabove — e.g.work-loop vX.Y — recent: <summary>. Then proceed.
Autonomous skill that picks one issue from the backlog, executes it, and exits. The scheduler re-invokes it for the next issue — one issue per context window.
Purpose
Run on a schedule (or manually) to autonomously advance the agent's task backlog. Each invocation handles exactly one issue, keeping context focused and noise-free. The cron handles iteration; this skill handles execution.
State Dependencies
| Source | Location | Read | Write | Description |
|---|---|---|---|---|
| GitHub Issues | Current repo | Yes | Yes | Task backlog |
| GitHub Labels | status:, priority:, skill:* | Yes | Yes | Track status and route by skill |
| Agent Skills | .claude/skills/ | Yes | No | Available capabilities |
| CLAUDE.md | ./CLAUDE.md | Yes | No | Agent identity and guidelines |
Prerequisites
ghCLI authenticated- Repository has required labels (status:todo, status:in-progress, priority:*)
- Agent has skills to handle the types of issues in backlog
Process
Step 1: Check for In-Progress Work
gh issue list --label "status:in-progress" --state open --json number,title,body,labels --limit 1
If an issue is in-progress:
- This is the current task
- Parse the issue body for requirements
- Continue working on it (skip to Step 3)
If no in-progress work, proceed to Step 2.
Step 2: Pick Next Task
Find highest priority todo:
# P0 first
gh issue list --label "priority:p0" --label "status:todo" --state open --json number,title,body,labels --limit 1
# Then P1
gh issue list --label "priority:p1" --label "status:todo" --state open --json number,title,body,labels --limit 1
# Then P2
gh issue list --label "priority:p2" --label "status:todo" --state open --json number,title,body,labels --limit 1
# Then any todo
gh issue list --label "status:todo" --state open --json number,title,body,labels --limit 1
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.
- 1mo ago First seen · 165 lines · 31 tokens per session scan A ae01d3ff21e1
work-loop is a skill published in the GitHub repository Abilityai/abilities (12 stars, last pushed 3d ago), licensed MIT. It adds 31 tokens to every session and 1,409 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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