Enforces written dollar caps, iteration bounds, concurrency limits, retry policy, and provider-dashboard hard caps before any paid inference call site. Use when writing or auditing loops, agents, webhooks, or queues that hit billed APIs. Not a production billing meter and not for rewriting after an overage already landed.
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Enforces written dollar caps, iteration bounds, concurrency limits, retry policy, and provider-dashboard hard caps before any paid inference call site. Use when writing or auditing loops, agents, webhooks, or queues that hit billed APIs. Not a production billing meter and not for rewriting after an overage already landed.
runaway-guard — $-Cost is the Third Complexity Dimension
Every loop has time complexity and space complexity. A loop that calls a paid API has a third: dollars per execution. The model tracks the first two automatically. It does not track the third, so it ships code where a single bug — a retry without bound, a stream reconnect storm, an agent that re-queues itself, a webhook that fires the same job twice — silently spends real money.
The canonical incident: developer writes a Fal.ai image-generation loop. Loop "obviously terminates" because it iterates over a fixed list. The list comes from a callback that fires on every Inngest retry. Each retry doubles the list. By morning, the bill is $200. Tests pass. Code review passed. The bug is not in the loop body. The bug is that no one stated the wallet invariant.
runaway-guard fixes this. State the max calls. State the max dollars per run. State the max dollars per day. Set the same caps in the provider dashboard so a code bug cannot bypass them. Then write the code.
Violating the letter of these rules is violating the spirit of the skill. "I'm only testing locally" is the exact rationalization that ships the $200 bill — local code hits the same paid API as production.
When to Use
Use runaway-guard when:
Writing or reviewing code that calls a paid AI / inference API in a loop, queue, retry path, agent step, webhook handler, or background job.
Importing or wrapping any paid-inference SDK: @fal-ai/*, fal-client, @anthropic-ai/sdk, anthropic, openai, replicate, elevenlabs, together-ai, groq-sdk, cohere-ai, @mistralai/*.
Designing an agent loop, fan-out pipeline, retry wrapper, polling job, stream reconnect, or self-rescheduling job that may call a billed endpoint.
Auditing a codebase / PR for unbounded fan-out, unbounded retries, missing idempotency keys, or missing provider-side spend caps.
Diagnosing an unexpected bill, runaway loop incident, or surprise overage.
Prerequisites
Access to the provider dashboard for every paid API you intend to call (Fal.ai, Anthropic, OpenAI, Replicate, ElevenLabs, Together, Groq, Cohere, Mistral, etc.).
Knowledge of the unit cost for each model / endpoint you plan to call (check provider pricing pages).
A queue / job framework configured if using Inngest, BullMQ, Sidekiq, or Cloud Tasks (for concurrency and retry settings).
p-limit (npm) or equivalent concurrency-limiter installed if doing in-process fan-out in Node.js / TypeScript.
Per-environment API keys (dev / staging / prod) — never share a single key across environments.
Procedure
The Iron Law
NO CALL TO A PAID API WITHOUT A WRITTEN $-CAP AT BOTH THE CODE AND PROVIDER LEVEL
A cap only in code can be bypassed by a bug in that code. A cap only at the provider can be hit during normal usage and degrade the product. You need both. If you cannot state both in one sentence each, you have not designed the call site — you have written a wish.
Step 1 — State the cost contract (before writing code)
Before producing code that calls a paid API, your message must contain — in this order:
Provider + unit cost. "Fal flux-pro: $0.05/image, billed per success."
Max calls per run. A literal integer that will appear as a constant in the code.
Max $ per run.max_calls × unit_cost. Compute it, don't estimate.
Max $ per day (provider hard cap). The dashboard setting that backstops the code.
Concurrency limit. In code, at the queue, at the provider.
Retry policy. Max attempts, which error codes retry, idempotency key strategy.
Amplifier audit. Walk the amplifier list in Step 5 below; declare "none apply" or address each that does.
The code — with the cost contract in a comment above the call site.
Self-check. One line: "in the worst case, this code bills $X and the provider cap stops it at $Y."
If any of 1–7 is missing, do not emit code.
Step 2 — Set the provider-side hard cap (out of band, before first request)
Set these before the first deploy. None of them require code changes.
Provider
Where to set the hard cap
Fal.ai
Dashboard → Billing → Spend Limit (e.g. $50/day). Hard stop on exceed.
Anthropic
Console → Workspaces → Workspace Budget with hard limit. Per-workspace, per-month.
OpenAI
Org → Settings → Usage limits (org-level hard limit blocks requests). ⚠️ Per-project monthly budgets are soft thresholds only — they alert but do not block. For a real hard cap use the org-level Usage limit, a billing gateway, or your own fail-closed budget check.
Replicate
Account → Billing → Spend limit. Per account.
ElevenLabs
Workspace → Usage limits per workspace / API key.
Together / Groq / Cohere / Mistral
Each has a billing dashboard with a monthly spend cap — set it before first deploy, not after.
No hard cap, no call site. Set the cap before the first request, not after the first incident.
Step 3 — Write the code with explicit bounds
Every loop calling a paid API gets an explicit iteration bound — a concrete integer in code, not just "eventually terminates":
// ❌ Terminates in theory. Bills $200 in practice.while (job.status !== 'done') {
await fal.run(...);
}
// ✅ Concrete bound — wallet invariant explicit.constMAX_CALLS = 20;
for (let i = 0; i < MAX_CALLS && job.status !== 'done'; i++) {
await fal.run(...);
}
if (job.status !== 'done') thrownewError('exceeded MAX_CALLS budget');
Every call site gets a one-line cost contract comment:
// cost contract:// provider: Fal flux-pro @ $0.05/image// max calls per run: 50// max $ per run: $2.50// provider hard cap: $50/day (set in Fal dashboard 2026-05-22)// concurrency: 3 (Inngest + p-limit, matching)// idempotency: key = `${campaignId}:${sha1(prompt)}` — provider-side dedup window 24h
Step 4 — Bound retries and concurrency
Retry policy:
Max attempts: a small integer (3–5 for transient errors, 1 for 4xx).
Cap counts across the whole pipeline, not just one library — Inngest retries × SDK retries × your own retry wrapper multiply.
4xx errors do not retry. Period. They will not become 2xx; they will just bill again.
Inngest default is 4 retries (up to 5 attempts including the initial — confirm against current Inngest docs). Worst-case wallet math: attempts = 1 + retries, so a default step.run() can bill 5×, not 4×.
Concurrency limit:
State the limit in code, at the queue (Inngest concurrency), and at the provider where supported.
Inngest: concurrency: { limit: N } on the function.
In-process: p-limit, semaphore, or batched Promise.all chunks — never an unbounded Promise.all(items.map(...)) on a paid API.
Idempotency keys:
Every mutating or charging call gets an idempotency key. A webhook that fires twice should bill once. Without an idempotency key, retry policies you cannot see (load balancer, framework, gateway) silently double-charge.
Step 5 — Amplifier audit
Walk this list before shipping. Declare "none apply" or address each that does:
Self-rescheduling jobs. A job that re-enqueues itself with no decrementing measure is an unbounded loop with extra steps.
Webhook handlers that call the API that called the webhook. Cycle detection or it will cycle.
Recursion over LLM output. "Ask the model what to do next" with no depth cap is a depth-unbounded recursion in dollars.
Polling without a deadline.while (!done) await poll() with no maxWaitMs is a wallet leak.
Streaming reconnect storms. A WebSocket / SSE reconnect with no backoff and no attempt cap can hammer a billed endpoint thousands of times per minute.
Cache-miss stampede on a paid call. N concurrent requests for the same uncached key → N billed calls. Use singleflight / request coalescing.
Step 6 — Edge-case enumeration
Enumerate these scenarios before shipping:
Scenario
Expected behavior
Empty input list
0 calls, 0 cost, return early — do not even auth
Input list longer than MAX
reject with NonRetriableError, do not partial-process
All calls fail with 4xx
1 attempt each, no retry, surface error
All calls fail with 5xx
bounded retries, total cost ≤ attempts × unit, alert on full exhaustion
Concurrent invocation of the same job
idempotency key dedups; second invocation costs $0
Network partition mid-batch
partial cost banked; on resume, idempotency key prevents re-charge
Provider rate-limit (429)
respect Retry-After; do not multiply retries inside SDK and outside
Webhook retried by provider
idempotency at the handler boundary
Local dev accidentally pointing at prod key
per-env keys + per-env caps make this cost $0.50, not $50
Cron fires while previous run still executing
concurrency limit = 1 OR explicit overlap-tolerant design
Step 7 — Output discipline
Code you emit must:
Have a // cost contract: block above each call site with the four numbers (unit cost, max calls, max $/run, provider hard cap setting).
Use a named constant (MAX_IMAGES_PER_RUN, MAX_AGENT_STEPS) for the bound — never a magic number inline.
Wrap fan-out in p-limit or equivalent — never raw Promise.all over a paid API.
Pass an idempotency key for every mutating / charging call.
Set queue-level concurrency and retries in the same file or document it.
Reject 4xx retries via NonRetriableError or equivalent.
Reference the provider hard-cap setting by name ("Fal Spend Limit $50/day, set 2026-05-22") so a future reader knows it exists.
Examples
Worked trap — the Inngest + Fal $200 night
This is the canonical case. Observe how each rule would have caught it.
What shipped:
// inngest function: generate images for a campaignexportconst generateCampaign = inngest.createFunction(
{ id: 'gen-campaign' }, // ❌ no concurrency limit
{ event: 'campaign/start' },
async ({ event, step }) => {
const prompts = await step.run('fetch', () =>fetchPrompts(event.data.id));
// ❌ unbounded fan-out, no per-run cap, no idempotencyawaitPromise.all(prompts.map(p => fal.run('fal-ai/flux-pro', { input: { prompt: p } })));
}
);
What went wrong.fetchPrompts had a bug: on a transient DB error it returned the partial list plus the previous run's list appended. Inngest retried the function at its default retry count (multiple attempts in addition to the initial one). Each retry re-ran fetchPrompts, each retry doubled the list (40 → 80 → 160 → 320 prompts). Promise.all fanned all 320 out concurrently. At $0.05/image: $16/retry × triangular growth across overnight retries on the schedule = ~$200 by morning.
Why each rule would have caught it.
Rule
Catch
1. Cost contract
Forces writing "max calls per run". The number prompts.length is not a known integer → rule fails → write a cap.
2. Concrete iteration bound
Promise.all(prompts.map(...)) has no integer bound → rule fails → wrap in chunks with MAX_IMAGES_PER_RUN.
3. Retry policy
Inngest default retries × no idempotency key = double-billed work. Rule forces an idempotency key per (campaignId, promptHash).
4. Concurrency limit
Promise.all is unbounded concurrency. Rule forces p-limit(3) and Inngest concurrency: { limit: 3 }.
5. Provider hard cap
Fal Spend Limit $50/day would have stopped the bleeding at $50 instead of $200.
7. Amplifier audit
"Self-rescheduling jobs" — Inngest's retry IS self-rescheduling. The audit forces you to consider it.
Note: the bug in fetchPrompts is still there. The protocol does not fix that bug — it makes the bug cost $2.50 instead of $200 while you find it. That is the entire point of defense in depth.
Common runaway patterns and their wallet invariants
idempotency key required; cycle if webhook is triggered by the same API
provider rate limit per key
Stream reconnect
attempts ≤ MAX_RECONNECTS, exponential backoff with cap
provider connection cap
Cache miss stampede
singleflight → cost ≤ 1 × unit_cost per key per window
n/a (deduped in code)
Self-scheduling job
recursion depth bounded by ledger row, not by code
scheduler-level dedup + max runs/day
Multi-provider fallback
sum across providers ≤ MAX_$_PER_RUN
hard cap on each provider separately
Pitfalls
Rationalizations to watch for
Excuse
Reality
"I'm only testing locally."
Local hits the same paid endpoint. A retry bug in test code bills the same dollars.
"The list is small, fan-out is fine."
The list is small today. Next week it is fetched from a table that grew 50×. The cap exists for next week.
"Inngest already retries, so I don't need a retry policy."
Inngest retries × your retry wrapper × SDK retries = 27 attempts. Each one bills.
"The API call is cheap, $0.001."
At 10,000 unintended invocations that is $10 — and the count is exactly what you failed to bound.
"I'll set the provider cap later."
The bug ships before "later". Set the cap in the 60 seconds it takes; the code can wait.
"Idempotency is overkill for this."
Webhooks retry. Load balancers retry. Browsers retry. Without an idempotency key, something will duplicate.
"We have monitoring, we'll catch it."
Monitoring catches it after $200 is spent. Caps prevent the $200 from being spent.
"It obviously terminates."
The $200/night incident also "obviously terminated". Write the integer bound.
If any of these sound familiar mid-thought: stop, write the cost contract, set the provider cap, then write the code.
Red flags — STOP and write the cost contract first
About to write await Promise.all(items.map(x => paidApi(x))) with no p-limit.
About to write while (!done) await paidApi(...) with no integer bound.
About to write an agent loop with "the model decides when to stop".
About to write a retry wrapper around a call that is already retried by Inngest / SDK / framework.
About to deploy a paid API key without first setting the provider dashboard cap.
About to commit a paid API key to a .env shared across environments.
About to handle a webhook that calls the API that produced the webhook.
"Just for tonight" — overnight is exactly when runaway loops bill the most.
All of these mean: stop, write the cost contract, set the provider cap, then write the code.
Provider-specific gotchas
OpenAI: Per-project monthly budgets are soft only — they alert but do not block. A runaway can continue past the documented project budget. For a real hard cap, use org-level Usage limits, a billing gateway/proxy, or your own fail-closed budget check in code.
Inngest: Default is 4 retries (up to 5 attempts including the initial — confirm against current Inngest docs). Worst-case wallet math: attempts = 1 + retries, so a default step.run() can bill 5×, not 4×. Always set retries: 2 or lower for paid call functions.
Anthropic: Prompt caching reduces cost ~90% for repeated context; the Workspace Budget cap is on unblended cost so caching extends the budget — but do not assume caching is always active.
Fal.ai: Webhook deliveries are paid; cap retries on your side, not just on the generation call.
ElevenLabs: Voice cloning is billed per character; cap the character count in code AND per key.
Limitations
Not a billing system. runaway-guard enforces intent (caps, contracts, audits) at code-write time. It does not meter spend in production — pair it with the provider's hard cap and observability (LLM-cost dashboards, log alerts) for runtime enforcement.
Provider-side caps may take minutes to enforce. Anthropic Workspace Budgets, OpenAI usage limits, and Fal Spend Limits are reconciled on a delay measured in minutes, not milliseconds. A pathological burst within a single window can still exceed the cap modestly.
No automated cost estimation for novel models. The cost-contract numbers (unit cost, $/run) are inputs the author must look up; the skill does not maintain a per-model price table.
Streaming and per-token pricing. For per-token APIs (Anthropic, OpenAI), max calls is a proxy — the real cap is max input tokens × max output tokens × per-token rate. Adapt the protocol: replace max calls per run with max tokens per run.
Compute-billed providers. For long-running GPU jobs (training, video encoding) billed in seconds, replace "calls" with "GPU-seconds" in the contract; the discipline transfers but the units differ.
Does not replace incident response. When a bill has already arrived, escalate to complexity-cuts for the corrective rewrite — runaway-guard prevents the next one, not the current one.
Verification
Before shipping any code that calls a paid API, check every box:
Cost contract comment exists above each call site with unit cost, max calls/run, max $/run, provider cap.
The iteration / fan-out bound is a named integer constant, not implicit in list length.
Concurrency limit is set in code (p-limit) AND at the queue (Inngest concurrency).
Retry policy is explicit: max attempts, 4xx → no retry, idempotency key per call.
Provider dashboard hard cap is set and the value is documented in the file.
Per-environment API keys; dev keys have lower caps than prod.
Amplifier audit (Step 5) has been performed and either "none apply" or each addressed.
A test exists for: empty input, oversized input rejected, 4xx not retried, idempotency key dedups duplicate invocation.
In the worst case the code bills ≤ MAX_$PER_RUN, and even with a bug the provider cap stops loss at MAX$_PER_DAY.
Cannot check every box? The code is example-correct, not bill-correct. Either fill the gap or do not connect a billing-enabled key.
Quick verification commands
# Check that no paid API key is shared across environments (Windows / PowerShell)
Select-String -Path .\.env* -Pattern "API_KEY|SECRET|TOKEN" | Select-Object Filename, LineNumber, Line
# Verify p-limit is installed if doing in-process fan-out
npm ls p-limit
# Grep for unbounded Promise.all over paid APIs (red flag)
Select-String -Path .\src\*.ts -Pattern "Promise\.all\(.*\.map\(.*paidApi"
Related Skills
invariant-guard — when the loop or recursion calling the paid API has no written termination measure. Termination is a precondition for a wallet cap; invariant-guard establishes it, runaway-guard bounds the cost.
complexity-cuts — when the runaway has already shipped and you are diagnosing an unexpected bill. Treat the unintended fan-out as a complexity bug, write a characterization test, then transform one step at a time.
lemmaly — when designing a new agent loop or batch pipeline that fans out paid calls. Pick the algorithm and data structure first; come back here for the wallet invariant per step.
mathguard — when the cost driver is compute on a sublinear-algorithm problem (vector search, sketching, FFT) rather than per-call billing; mathguard sets the algorithmic floor that determines per-call work.
The thesis, in one line
Time bounds prevent stalls. Space bounds prevent OOMs. Dollar bounds prevent $200 mornings. AI assistants enforce the first two by default and ignore the third. runaway-guard makes them reason about the wallet first.
