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.
1---2name: dataset3description: 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".4---56# Dataset78The plugin ships a dataset of real orchestrate-run records under `dataset/`.9This skill searches it and helps users grow it. The richer the dataset, the10better every future `orchestrate` run starts.1112## Searching — pull best practices1314Two interfaces over the same scoring (`dataset/lib.ts`):1516### Via the MCP server (preferred)1718The plugin registers the `dataset-server` MCP server. Call its tools:1920- **`dataset_search`** — args: `domain`, `stack[]`, `archetype`, `keywords[]`,21 `limit`. Returns ranked entries with their `lessons`.22- **`dataset_get`** — args: `id`. Returns one full entry.23- **`dataset_stats`** — no args. Returns entry/domain/archetype counts.2425If the MCP server is not connected, fall back to the CLI.2627### Via the CLI2829```bash30bun run dataset/search.ts '{"domain":"rest-api","stack":["bun"],"archetype":"verified-swarm","keywords":["auth"],"limit":5}'31```3233### When to search3435- **Before an architecture interview** — search by `domain` + `stack`, surface36 the `architecture` and `lessons` of similar past projects to the user.37- **Before workflow synthesis** — search by `domain` to see which archetype each38 phase used and how many rounds it took to converge.39- **When a project hits a pitfall** — search by `keywords` for entries whose40 `pitfalls` describe the same trap.4142Always tell the user *which* entries informed a recommendation — cite the entry43`id`. Treat lessons as evidence, not law: an entry reflects one project.4445## Contributing — grow the dataset4647Run this after a real project ships (orchestrated or hand-built).48491. Read `dataset/README.md` and `dataset/schema.json`.502. Draft `dataset/entries/<id>.json`. Required: `id`, `domain`, `summary`,51 `stack`, `architecture`, `workflows`, `outcome`, `lessons`, `contributor`,52 `contributedAt`. Be honest about `outcome` and `pitfalls` — a failed run with53 a clear lesson is as valuable as a clean one.543. Validate: `bun run dataset/validate.ts` — must print `N/N entries valid`.554. Open a PR with the new file, or file a **Dataset contribution** GitHub issue56 (`.github/ISSUE_TEMPLATE/dataset-contribution.yml`) for a maintainer to add.5758To help a user contribute: interview them for each required field, write the59JSON file, run the validator, and show them the result before opening the PR.6061## Notes6263- Entry ids are unique and match the file name.64- The dataset feeds the `orchestrate` skill — Steps 4 (architecture) and 565 (workflow synthesis) should search it first.
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