dataset

name

dataset

description

Search the workflow dataset for best practices from past projects, and help users contribute new run records. Each dataset entry is a real orchestrate-run: domain, stack, architecture, workflows, outcome, lessons. Search it before designing a project so new work starts from accumulated experience. Use when: "search the dataset", "find similar projects", "what worked for X", "contribute to the dataset", "add a dataset entry", "best practices for X".

Dataset

The plugin ships a dataset of real orchestrate-run records under dataset/. This skill searches it and helps users grow it. The richer the dataset, the better every future orchestrate run starts.

Searching — pull best practices

Two interfaces over the same scoring (dataset/lib.ts):

Via the MCP server (preferred)

The plugin registers the dataset-server MCP server. Call its tools:

• dataset_search — args: domain, stack[], archetype, keywords[], limit. Returns ranked entries with their lessons.
• dataset_get — args: id. Returns one full entry.
• dataset_stats — no args. Returns entry/domain/archetype counts.

If the MCP server is not connected, fall back to the CLI.

Via the CLI

bun run dataset/search.ts '{"domain":"rest-api","stack":["bun"],"archetype":"verified-swarm","keywords":["auth"],"limit":5}'

When to search

• Before an architecture interview — search by domain + stack, surface the architecture and lessons of similar past projects to the user.
• Before workflow synthesis — search by domain to see which archetype each phase used and how many rounds it took to converge.
• When a project hits a pitfall — search by keywords for entries whose pitfalls describe the same trap.

Always tell the user which entries informed a recommendation — cite the entry id. Treat lessons as evidence, not law: an entry reflects one project.

Contributing — grow the dataset

Run this after a real project ships (orchestrated or hand-built).

1. Read dataset/README.md and dataset/schema.json.
2. Draft dataset/entries/<id>.json. Required: id, domain, summary, stack, architecture, workflows, outcome, lessons, contributor, contributedAt. Be honest about outcome and pitfalls — a failed run with a clear lesson is as valuable as a clean one.
3. Validate: bun run dataset/validate.ts — must print N/N entries valid.
4. Open a PR with the new file, or file a Dataset contribution GitHub issue (.github/ISSUE_TEMPLATE/dataset-contribution.yml) for a maintainer to add.

To help a user contribute: interview them for each required field, write the JSON file, run the validator, and show them the result before opening the PR.

Notes

• Entry ids are unique and match the file name.
• The dataset feeds the orchestrate skill — Steps 4 (architecture) and 5 (workflow synthesis) should search it first.