| 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).
- Read
dataset/README.md and dataset/schema.json.
- 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.
- Validate:
bun run dataset/validate.ts — must print N/N entries valid.
- 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.
