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 ericrisco/rsc-harness --skill falgit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/fal)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/fal"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/fal/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/ericrisco/rsc-harness/fal"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/fal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 67 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 158 Data is uploaded to cloud storage (S3 / GCS / Azure Blob). This may be a legitimate backup or exfiltration to an external bucket. Manual review is recommended.Fix: Verify the destination bucket is trusted and owned by you. Never upload credentials, secrets, or workspace contents to external or unverified cloud storage.
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 | $0.00078 | $0.02276 |
| Opus 5.5 | $0.00031 | $0.00910 |
| Sonnet 5.5 | $0.00016 | $0.00455 |
| Haiku 4.5 | $0.00008 | $0.00228 |
Grade A, and why
fal scanned grade A 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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
description: "Use when calling a fal.ai endpoint by id to generate image, audio, or video from JS/Python/curl: subscribe vs submit, queue states, ED25519 webhook signature verification, per-call cost, or migrating off @f How it starts
The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
fal
The wire to fal.ai's fast, pre-warmed media endpoints: call a model by id, control the queue, get the file back. fal is the fast-media path — latency-optimized image (FLUX, Seedream, SD), audio (TTS, music), and video (Veo, Wan, Kling, Hailuo) endpoints you invoke by id with FAL_KEY.
You own the mechanics: auth, call mode, queue states, webhook signatures, file I/O, per-call cost.
When NOT to use
| You want… | Go to |
|---|---|
| Which model / what to generate / art direction / multi-provider media pipeline | ai-media |
The same kind of models on Replicate (replicate.run / predictions) |
replicate — images-specifically replicate-images |
| Renting a raw GPU pod you SSH into to train or custom-serve | runpod |
| Deploying your own Python function as an autoscaling endpoint | modal |
| Cheap hosted LLM text/chat completions | together-fireworks |
| The generic provider-agnostic webhook receiver/verifier pattern | webhooks |
Rule: if you are not invoking a fal endpoint id with FAL_KEY, you are in the wrong skill.
Setup & auth
# JS — current client. NOT @fal-ai/serverless-client (deprecated, migrate).
npm i @fal-ai/client # latest 1.10.1
# Python
pip install fal-client
export FAL_KEY="key_id:key_secret"
import { fal } from "@fal-ai/client";
// Reads FAL_KEY from env automatically; or set it explicitly:
fal.config({ credentials: process.env.FAL_KEY });
Rule: never ship FAL_KEY to a browser bundle. Proxy every call through your own server. Why: a key in client-side JS lets anyone drain your account — fal endpoints bill per call with no per-request cap.
Pick a call mode
All three modes hit the same queue. Choose by how long the job runs and where you call it from.
| Situation | Mode | Why |
|---|---|---|
| Need the result now, can block, single short job (image, short TTS) | subscribe |
Submits + auto-polls until done; feels synchronous, no polling code |
| Long job (video), batch, or running in a serverless/edge handler that can't hold a connection | submit + webhook_url (or poll) |
Returns a request_id instantly; result arrives later, no held connection |
| Trivially short call, you accept no queue control | run |
Direct synchronous call — no status, no logs; drops on long jobs |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 197 lines · 78 tokens per session scan A b67a8b70a8bd
fal is a skill published in the GitHub repository ericrisco/rsc-harness (239 stars, last pushed yesterday), licensed MIT. It adds 78 tokens to every session and 2,276 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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