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 agentmods add skills/xyruscode/ai-sync/loopnpx skills add XyrusCode/ai-sync --skill loopgit clone --depth 1 https://github.com/XyrusCode/ai-syncWhat 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 | $0.00028 | $0.00913 |
| Opus 5 | $0.00014 | $0.00456 |
| Sonnet 5 | $0.00006 | $0.00183 |
| Haiku 4.5 | $0.00003 | $0.00091 |
Grade A, and why
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 yesterday.
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.
This is a copy
100% identical to loop — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Loop
Parse
Accept /loop [interval] <prompt>.
- Leading interval:
5m /foo,30s check status,2h run report. - Trailing interval:
check deploy every 5m,run tests every 10 minutes. - No interval: dynamic mode; the agent chooses the next delay after each run.
- Empty prompt: show
Usage: /loop [interval] <prompt>.
Use intervals like 30s, 5m, 2h, 1d. Convert unit words to short units.
Use monitored shell output to wake the agent for recurring local work.
Fixed Schedule
while true; do
sleep <seconds>
echo 'AGENT_LOOP_TICK_<purpose> {"prompt":"<prompt>"}'
done
- Check existing terminals for an already-running matching loop.
- Start one background shell loop with
notify_on_output. - Use a unique sentinel and a regex such as
^AGENT_LOOP_TICK_<purpose>. - Smoke-check once to confirm clean startup.
- Run the prompt once immediately after arming the loop.
- The first sentinel should arrive only after the initial sleep, so startup does not double-run the prompt.
- Track the PID so the agent can stop the loop if asked.
- Briefly confirm: the interval, that the prompt already ran once, when the first tick will arrive, and that the loop will fire on each tick until stopped. On later ticks, give a short update of what changed. On stop, say the loop has stopped and why.
Dynamic Schedule
The user wants the agent to self-pace. Decide what makes the next iteration worth running — a passage of time, or an observable event.
- Run the prompt now.
- If the next run is gated on an event (a git ref advancing, a log line matching, a file changing, a CI check completing), arm a background watcher that emits the sentinel only when the event fires, with
notify_on_outputon^AGENT_LOOP_WAKE_<purpose>. Arm once; skip on later ticks if it's still running. - At the end of the turn, arm a one-shot time-based wake:
sleep <seconds>
echo 'AGENT_LOOP_WAKE_<purpose> {"prompt":"<prompt>"}'
With a watcher armed, this is the fallback heartbeat — lean long so idle ticks aren't pure overhead. Without a watcher, this is the cadence — pick a delay based on when the result is worth checking again.
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.
- yesterday First seen · 74 lines · 28 tokens per session scan A 03f141406a7e
loop is a skill published in the GitHub repository XyrusCode/ai-sync (2 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 913 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to loop, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…