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 Lab2A/metalworks --skill distribution-measuregit clone --depth 1 https://github.com/Lab2A/metalworksWrote 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/lab2a/metalworks/distribution-measure)<a href="https://agentmods.dev/skills/lab2a/metalworks/distribution-measure"><img src="https://agentmods.dev/badge/skills/lab2a/metalworks/distribution-measure/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/lab2a/metalworks/distribution-measure"><img src="https://agentmods.dev/badge/skills/lab2a/metalworks/distribution-measure.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 | $0.00243 | $0.01160 |
| Opus 5.5 | $0.00097 | $0.00464 |
| Sonnet 5.5 | $0.00049 | $0.00232 |
| Haiku 4.5 | $0.00024 | $0.00116 |
Grade A, and why
distribution-measure 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Preamble (run first)
Before any other tool, run the preflight MCP tool (or metalworks preflight on
the CLI). If it reports setup issues or that an update is available, surface that
to the user in one line and help them resolve it (install the missing extra/key,
or pip install -U metalworks) before continuing. Skip only if the user has
already passed preflight this session.
Read the reference; never reverse-engineer the source. The moment you need to know how
metalworks behaves — provider/model resolution, which source/reader runs, config precedence,
an error you hit, or the async run loop — STOP and read docs/operating-metalworks.md
(bundled with this plugin) before opening any file under src/. It is the source of truth;
do not derive behavior from source. (Full docs: https://metalworks.lab2a.ai/docs.) For a
long-running run, poll status with the Monitor tool or a bounded loop — never a blind sleep.
You are closing the distribution loop for one demand report. Everything else in the Distribution pillar PLANS — channels, assets, the sequenced push/stream plan. This is where it learns: you name, per channel, what "worked" means and exactly how to track it, so the human can record a real result and the next push re-ranks on evidence instead of vibes. metalworks can't watch live traffic, so in its lane it defines the metric + the instrument — it does not measure for you.
Steps
-
Get the
report_id. If the user hasn't run a report yet, point them at/demand-reportfirst — the metrics are derived from the report's channel strategy (D2), which needs a finished report to ground on. (Ideally they've also run/distribution-planso they know which push each metric belongs to.) -
Call the
distribution_measureMCP tool with thereport_id(or, on the CLI, runmetalworks distribution measure <report_id>). It routes the report into its channel strategy, then returns oneChannelMetricper selected channel. -
Present each
ChannelMetrichonestly:channel_name+surface_type— which channel this is for.success_metric— what "worked" means for this channel (e.g. a launch platform → top-N + attributed signups in 7d; a marketplace → installs + WAU; a community → qualified replies + click-through; answer-engine GEO → citation appearances).instrumentation— the concrete thing to wire BEFORE the push: a UTM tag, an attributed-signup query, a citation check. Tell the user to set this up first; a push you can't attribute is launch theater.
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 · 70 lines · 0 tokens per session scan A f8e1017ef058
distribution-measure is a skill published in the GitHub repository Lab2A/metalworks (7 stars, last pushed 4mo ago), licensed MIT. It adds 243 tokens to every session and 1,160 once invoked, about $0.0010 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.
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