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 receptron/mulmoterminal --skill actionsgit clone --depth 1 https://github.com/receptron/mulmoterminalWrote 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/receptron/mulmoterminal/actions)<a href="https://agentmods.dev/skills/receptron/mulmoterminal/actions"><img src="https://agentmods.dev/badge/skills/receptron/mulmoterminal/actions/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/receptron/mulmoterminal/actions"><img src="https://agentmods.dev/badge/skills/receptron/mulmoterminal/actions.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 | $0.00031 | $0.00977 |
| Opus 5.5 | $0.00012 | $0.00391 |
| Sonnet 5.5 | $0.00006 | $0.00195 |
| Haiku 4.5 | $0.00003 | $0.00098 |
Grade A, and why
blueprint-from-collection-actions 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 15d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Actions and ingests, as features
Read .blueprint/actions.json (written with the spec, and agreed when the spec was approved). If it does not exist, the
source had no actions or ingests: stop. Each entry names an action (books.actions.tidy) or an ingest (books.ingest),
what it was (kind) and what was decided (decision). The original instructions are in
.blueprint/source/collections/<slug>/templates/.
For every feature, build it as below, unless an earlier step already built it because a must-have needed it: then
check it against its entry and give it its test here instead of building it a second time.
- mutate — an API operation and a button on the record's screen. Its
requireis checked at the API's entry; itssetis the change it makes; itsparamsare the inputs the button asks for. - chat / agent — read the template: it says what the agent was asked to do. Build that as the app's own logic.
Where a step genuinely needs a language model (summarising, classifying, drafting text), call the Claude API from the
server (
@anthropic-ai/sdk, the model id in.envwith a sensible default,ANTHROPIC_API_KEYin.env). Before anything goes in.env, make sure.gitignorehas a line.env— add it if not; the check fails otherwise. Never write the key's value yourself: tell the person, in the README, to put their own key there. Everything that does not need a model stays plain code. The screen shows the result and any failure in words. - ingest (rss / atom / http-json) — a job that fetches the source, maps each item as the ingest's
mapsays, and inserts or updates records by theidFromit names, never duplicating. Run it on the schedule the ingest declares, inside the server, and also fromyarn ingest <slug>. An ingest of kindagentis built like an agent action.
Tests in test/actions.test.ts, one per feature, each with the entry's name in the title of its it(…) / test(…)
(it("books.actions.tidy: summarises", …)) — with it / test imported from vitest in that file (import { it } from "vitest"); the check reads titles only from
calls to those imports, so a global it, a helper of the same name, a comment or a title built at run time does not count, and neither does a describe(…) name (it("tidy") inside
describe("books.actions") is not titled books.actions.tidy: write the whole name in every test's own title):
walk the real flow against a temporary database and read back what changed. Never call the network in a test: a model
call goes through a client the test replaces with a stand-in, and an ingest reads a fixture file kept under
test/fixtures/. The tests must pass with no API key set.
For every manual: a section in README.md whose heading names the entry (## books.actions.tidy) and which says,
step by step, what a person does instead. For every drop: nothing to build; the spec already says why.
Done when the check passes: .blueprint/actions.json matches the source, every feature has a test titled with its
name, every manual has a README heading naming it, an .env (if any) is ignored, and yarn build and yarn test
succeed.
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.
- 15d ago Changed · +2 lines 171756afb9ca
- 16d ago Changed · +1 lines 2b8b6ff1fc6f
- 17d ago First seen · 48 lines · 31 tokens per session scan A 6d9fd98ca9dd
blueprint-from-collection-actions is a skill published in the GitHub repository receptron/mulmoterminal (239 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 977 once invoked, about $0.0001 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 2026-09-30.
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