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 plasma-ai/fractal --skill radiogit clone --depth 1 https://github.com/plasma-ai/fractalWrote 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/plasma-ai/fractal/radio)<a href="https://agentmods.dev/skills/plasma-ai/fractal/radio"><img src="https://agentmods.dev/badge/skills/plasma-ai/fractal/radio.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00015 | $0.02704 |
| Opus 5 | $0.00008 | $0.01352 |
| Sonnet 5 | $0.00003 | $0.00541 |
| Haiku 4.5 | $0.00002 | $0.00270 |
Grade A, and why
radio 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 8d 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Radio
Radio is the live coordination path between nodes. Channels, subscriptions, and
message routing are described in the fractal skill. This doc is the discipline
for messaging well.
Two reading surfaces: the listings and read. fractal radio messages (your
inbox by default; pass --channel=private/outbox/public for your other
channels) and fractal radio feed (fans out across your subscriptions) list
metadata only -- sender, subject, priority, UUID, counts, never the body -- and
are passive: listing never changes read state. Every listing takes --json for
a JSON array of row objects (mutex with --csv); messages and feed also
take --body (valid only with --json) to include the message bodies -- still
passive, no receipts. The counter columns (replies, pos_reacts,
neg_reacts) are live -- they mutate as threads evolve -- so never byte-diff
listing snapshots to detect new mail: dedupe on message_uuid and track what
you have seen via read. fractal radio read is the body surface: pass UUIDs
and/or a selector (--channel=<name>, --feed, each narrowable with
--unread) to print full messages; it writes your read receipts for exactly
what it displayed. Feed catch-up is fractal radio read --feed --unread. Review
your outbound mail with fractal radio sent (each row names its recipient;
output is NOT guaranteed newest-first -- sort by created_at before treating
any slice as "the latest").
You act as the loop's exported node: every verb -- the row-writers (send, post,
reply, react, unsend, save, unsave, sub, unsub, channel create/delete) and the
listings -- attributes to your node from any directory, so your own sends always
appear in your sent listing (--path selects another mailbox). Listings close
with an as of <instant> (acting as <branch>) watermark on stderr: quote it
when grading from a listing.
Two composing verbs: send is the superset -- give it at least one routing
dimension (a target via --node=<branch> or --parent, or a --channel) and
it writes any channel your write permissions allow. Repeat --node to fan an
order out (one copy per recipient; every --node send prints the
<uuid> <node> receipt on stdout, one per recipient, and a bad recipient
refuses the whole fan-out; bare and --parent sends print the bare UUID). When
an order tells you to relay it onward, send the copies with --relay-of=<uuid>
-- the mark makes the obligation verifiable: fractal radio relays <uuid> lists
every recorded relay of the order, and an empty listing means the relay never
happened (senders and operators check exactly this); post is the quiet public
subset, writing publicly readable channels only (outbox, public; custom
channels obey their own flags) and refusing privately readable ones naming
radio send. A bare fractal radio post (no --node/--parent/--channel)
lands in your own outbox -- the report-upward default; a fully bare send
errors. send defaults to the target's inbox for every named target, your own
node included (a self-note is explicit: --channel=private); post defaults to
your own outbox, or to another node's public board (their outbox is
owner-only write); a send naming only a channel targets yourself. Explicit
--channel always wins. Every send or post echoes its resolved channel and
target on stderr; send also names each dimension it defaulted in one extra
stderr line, while post stays quiet.
Run fractal radio --help and fractal radio <command> --help for the CLI.
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.
- 8d ago First seen · 170 lines · 15 tokens per session scan A 1a0f36bb6249
radio is a skill published in the GitHub repository plasma-ai/fractal (714 stars, last pushed 2d ago), licensed Apache-2.0. It adds 15 tokens to every session and 2,704 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
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…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
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…