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 wan-huiyan/agent-traffic-control --skill parallel-subagent-fanout-rate-limit-recover-from-diskgit clone --depth 1 https://github.com/wan-huiyan/agent-traffic-controlWrote 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/wan-huiyan/agent-traffic-control/parallel-subagent-fanout-rate-limit-recover-from-disk)<a href="https://agentmods.dev/skills/wan-huiyan/agent-traffic-control/parallel-subagent-fanout-rate-limit-recover-from-disk"><img src="https://agentmods.dev/badge/skills/wan-huiyan/agent-traffic-control/parallel-subagent-fanout-rate-limit-recover-from-disk/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/wan-huiyan/agent-traffic-control/parallel-subagent-fanout-rate-limit-recover-from-disk"><img src="https://agentmods.dev/badge/skills/wan-huiyan/agent-traffic-control/parallel-subagent-fanout-rate-limit-recover-from-disk.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 | $0.00325 | $0.01615 |
| Opus 5 | $0.00162 | $0.00807 |
| Sonnet 5 | $0.00065 | $0.00323 |
| Haiku 4.5 | $0.00032 | $0.00161 |
Grade A, and why
parallel-subagent-fanout-rate-limit-recover-from-disk 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 12d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parallel subagent fan-out: rate limit, and recover from disk not return status
Problem
You dispatch a large fan-out of subagents in one message — each reads a chunk, does work, and writes an output file — to maximize parallelism. Two things go wrong:
- Above a low concurrency ceiling the API returns "Server is temporarily limiting requests (not your usage limit) · Rate limited" for most of the batch.
- When you go to retry the "failed" ones, the agents' return status is misleading: many agents that returned a rate-limit/terminal error had already completed their work and written their output file — the error hit on the final summary turn, after the side effect.
Re-running based on the agent return status re-does work that's already on disk (wasted tokens) or, worse, you trust "20 failed" and redo all 20 when 14 actually succeeded.
Context / Trigger Conditions
- Dispatching >~5-6 concurrent
general-purposeAgent/Task subagents (or a Workflow fan-out) in a single message. - Result array peppered with
API Error: Server is temporarily limiting requests (not your usage limit) · Rate limited. Some agents showtool_uses: 14-24, subagent_tokens: 0— they did work but the final return failed. - Each subagent's contract is to write a file (chunk JSON, transformed doc, etc.) at a known path.
Root cause
- Concurrency, not quota. Too many simultaneous subagent inference streams trip a server-side throttle that is explicitly "not your usage limit." A smaller batch (≤4-5) stays under it.
- Side effect precedes the failing turn. A subagent does Read→…→Write (the deliverable) and then emits a final summary turn. The rate-limit/terminal error lands on that last turn, so the file is already on disk even though the agent "errored."
Solution
- Batch at ≤4-5 concurrent for large fan-outs. Dispatch batch, wait, dispatch next. (One validation batch first to prove the prompt, then scale in small batches.)
- To decide retries, CHECK DISK, not the agent return. Enumerate the expected
output paths; re-dispatch only the chunks whose files are missing or empty:
for n in $(seq -w 1 N); do f="out/.chunk_${n}.json" [ -f "$f" ] && [ -s "$f" ] || echo "MISSING $n" done - Make each subagent write to a deterministic absolute path (so this disk check is possible) and validate the file parses before counting it done.
- If the run is paused/resumed across sessions, commit the produced files (or rely on a persistent working dir) so the expensive partial work survives.
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.
- 12d ago First seen · 118 lines · 325 tokens per session scan A e89024fb7a5d
parallel-subagent-fanout-rate-limit-recover-from-disk is a skill published in the GitHub repository wan-huiyan/agent-traffic-control (3 stars, last pushed today), licensed MIT. It adds 325 tokens to every session and 1,615 once invoked, about $0.0016 per session on Opus 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 2026-08-31.
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