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-data-reportnpx skills add Lab2A/metalworks --skill distribution-data-reportgit 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-data-report)<a href="https://agentmods.dev/skills/lab2a/metalworks/distribution-data-report"><img src="https://agentmods.dev/badge/skills/lab2a/metalworks/distribution-data-report.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 | $0.00166 | $0.01124 |
| Opus 5 | $0.00083 | $0.00562 |
| Sonnet 5 | $0.00033 | $0.00225 |
| Haiku 4.5 | $0.00017 | $0.00112 |
Grade A, and why
distribution-data-report 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 4d 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 — 71 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 a data report — the data-as-marketing flagship asset. It stacks every AI-citation driver at once: original research + a ranking (the top AI-cited format) + verbatim quotes + permalinks, over a proprietary Reddit corpus (the #1 AI-cited domain). The defensibility is the corpus others can't reproduce; the credibility is the disclosed method. You are NOT writing a marketing puff piece — every number traces to the corpus, and the survey-fabrication base rate is the exact trap to avoid.
Steps
-
Get the
report_id. If the user hasn't run a report yet, point them at/demand-reportfirst — a data report projects an existing report's clusters, so it needs a finished one to rank. -
Pick the
kindfrom what the user wants:complaint_index— each row is a pain point consumers raised (default).feature_ranking— each row is a feature / capability consumers asked for.state_of— each row is a theme of the overall state of the category.
-
Call the
distribution_data_reportMCP tool with thereport_id+kind(or, on the CLI, runmetalworks distribution data-report <report_id> --kind <kind>). It projects the report'sranked_clustersdeterministically and does one LLM call for the title + per-row labels, returning aDataReportAsset.
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.
- 4d ago First seen · 71 lines · 166 tokens per session scan A ee4037584733
distribution-data-report is a skill published in the GitHub repository Lab2A/metalworks (6 stars, last pushed 2mo ago), licensed MIT. It adds 166 tokens to every session and 1,124 once invoked, about $0.0008 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.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
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…