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 agentmods add skills/lab2a/metalworks/distribution-assetsnpx skills add Lab2A/metalworks --skill distribution-assetsgit 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-assets)<a href="https://agentmods.dev/skills/lab2a/metalworks/distribution-assets"><img src="https://agentmods.dev/badge/skills/lab2a/metalworks/distribution-assets.svg" alt="Measured on agentmods" 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 | $0.00198 | $0.01051 |
| Opus 5 | $0.00099 | $0.00526 |
| Sonnet 5 | $0.00040 | $0.00210 |
| Haiku 4.5 | $0.00020 | $0.00105 |
Grade A, and why
distribution-assets 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 6d 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 — 65 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 turning one demand report into channel-SHAPED launch assets — the actual copy for each channel the strategy selected, drafted in the shape that channel rewards. A Product Hunt post is a tagline + an authentic maker comment + gallery captions; a Show HN is a plain title + a technical first comment; an X launch is a numbered tweet thread; a LinkedIn post is a carousel. You are NOT writing one flat blob and you are NOT forcing a Reddit quote behind every persuasive line.
Steps
-
Get the
report_id. If the user hasn't run a report yet, point them at/demand-reportfirst — assets are drafted off a finished report and its channel strategy. If a positioning wedge exists, pass it; it sharpens the copy. -
Call the
distribution_assetsMCP tool with thereport_id(or, on the CLI, runmetalworks distribution assets <report_id>). It routes the report into its channel strategy, then drafts oneChannelAssetper selected channel — one LLM call per channel — and returns the list. -
Walk each asset honestly:
- Lead with the channel_name + surface_type + funnel_stage.
- Show the channel-shaped parts by role — the maker comment matters more than the tagline on PH; the technical first comment is the asset on Show HN.
- Show the per-channel offer (the CTA / conversion ask).
- Note how many claim_citations ground — these are the DEMAND claims that trace to a real quote; the persuasive hooks deliberately don't.
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.
- 6d ago First seen · 65 lines · 198 tokens per session scan A a137653a7da5
distribution-assets is a skill published in the GitHub repository Lab2A/metalworks (6 stars, last pushed 2mo ago), licensed MIT. It adds 198 tokens to every session and 1,051 once invoked, about $0.0010 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 2026-08-31.
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…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…