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 resonatehq/resonate-skills --skill resonate-recursive-fan-out-pattern-pythongit clone --depth 1 https://github.com/resonatehq/resonate-skillsWrote 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/resonatehq/resonate-skills/resonate-recursive-fan-out-pattern-python)<a href="https://agentmods.dev/skills/resonatehq/resonate-skills/resonate-recursive-fan-out-pattern-python"><img src="https://agentmods.dev/badge/skills/resonatehq/resonate-skills/resonate-recursive-fan-out-pattern-python/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/resonatehq/resonate-skills/resonate-recursive-fan-out-pattern-python"><img src="https://agentmods.dev/badge/skills/resonatehq/resonate-skills/resonate-recursive-fan-out-pattern-python.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 | $0.00064 | $0.01952 |
| Opus 5.5 | $0.00026 | $0.00781 |
| Sonnet 5.5 | $0.00013 | $0.00390 |
| Haiku 4.5 | $0.00006 | $0.00195 |
Grade A, and why
resonate-recursive-fan-out-pattern-python 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 2mo 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Resonate Recursive Fan-Out Pattern — Python
Overview
Recursive fan-out is when a durable function spawns child invocations (either of itself or of siblings), optionally waits for them in parallel, and possibly continues recursing. Each child is its own Resonate promise; if a worker crashes, each child resumes independently.
The pattern is expressed in Python by launching multiple ctx.run(...) or ctx.rpc(...) calls before awaiting them — collect the futures first, then await them. This is different from TS's map-over-promises shape only in syntax; the semantics are identical.
When to use
- Batch processing where items are independent
- Web crawling / tree traversal with dynamic depth
- Map-reduce style workflows
- Any fan-out where each leaf is a discrete, retryable unit of work
Don't use for parallel I/O within a single step (use async clients directly inside a ctx.run envelope) or for a tight inner loop (overhead of a promise per item dominates).
Parallel fan-out in the same process
Launch children without blocking; collect futures; await them:
from __future__ import annotations
import asyncio, os, time
from typing import TYPE_CHECKING
from resonate.resonate import Resonate
if TYPE_CHECKING:
from resonate.context import Context
r = Resonate(url=os.environ.get("RESONATE_URL", "http://localhost:8001"))
async def enrich_batch(ctx: Context, order_ids: list[str]) -> list[dict]:
# Launch all children (returns futures immediately)
futures = [ctx.run(enrich_one, oid) for oid in order_ids]
# Await them all; order preserved
results = [await f for f in futures]
return results
async def enrich_one(ctx: Context, order_id: str) -> dict:
# Enrichment logic — this is a leaf
return {"order_id": order_id, "enriched": True}
Each enrich_one call is an independent durable promise. If the parent worker crashes after launching children but before awaiting, the children continue; on parent replay, awaiting the future hits the stored promise value.
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.
- 2mo ago First seen · 192 lines · 64 tokens per session scan A 9571c6614a42
resonate-recursive-fan-out-pattern-python is a skill published in the GitHub repository resonatehq/resonate-skills (7 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 1,952 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
create-pubsub-python
Creates a Dapr pub/sub app in Python with FastAPI (publisher, subscriber or both) and declarative, programmatic or streaming subscriptions. Use for "create a pub/sub app in Python" or "build a FastAPI publisher and subscriber with Dapr". For an existing app use add-pubsub-python.
create-workflow-python
Creates a Dapr workflow app in Python, run locally with dapr run. Use for "create a workflow in Python" or "build a workflow app in Python". For Diagrid Catalyst use the catalyst-ai plugin.
add-pubsub-python
Adds Dapr pub/sub to an existing Python FastAPI app. Keeps its subscription type, shows a plan for approval, then makes targeted edits and checks imports after each. Use for "add pub/sub to my FastAPI app" or "make this Python service subscribe to a Dapr topic". For a new app use create-pubsub-python.
fastapi-pro
Production FastAPI patterns — async endpoints, SQLAlchemy 2.0 async, Pydantic V2, dependency injection, JWT auth, testing. Use for Python 3.11+ FastAPI backends. NOT for Django (→ django-patterns) or Node.js (→ backend-patterns).
stripe-projects
Use after E2B sandbox/API access has been provisioned through Stripe Projects and the user needs to use the resulting E2B API key with the E2B CLI, JavaScript SDK, Python SDK, or Code Interpreter SDK.
azure-mgmt-botservice-py
Azure Bot Service Management SDK for Python. Use for creating, managing, and configuring Azure Bot Service resources. Triggers: "azure-mgmt-botservice", "AzureBotService", "bot management", "conversational AI", "bot channels".