翻訳待ち：Show HN: Sufleur-style prompt registry with typed code-generation

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

Uh oh! There was an error while loading. Please reload this page. Notifications You must be signed in to change notification settings Fork 0 Star 1 BranchesTags Open more actions menu Folders and files NameName Last commit message Last commit date Latest commit History 142 Commits 142 Commits .github/workflows .github/workflows cmd/sufleur cmd/sufleur internal internal pkg/errors pkg/errors scripts scripts wrappers wrappers .gitignore .gitignore .goreleaser.yaml .goreleaser.yaml Makefile Makefile README.md README.md RELEASE.md RELEASE.md go.mod go.mod go.sum go.sum Repository files navigation Native Go CLI for Sufleur — the registry where you author, version, and publish LLM prompts. It has two halves: Install published prompts into your project the way npm / pip installs packages — declared in sufleur.yaml, locked to sufleur-lock.yaml, generated into a single typed file. Author prompts from the CLI — full CRUD over workspace prompts, versions, files, and metadata. Designed so a coding agent (Claude Code, Cursor, etc.) can drive the authoring loop on your behalf. The payoff Most codebases keep prompts as raw strings — hand-rolled interpolation, no versioning, and JSON.parse + finger-crossing on the model's output: // before const prompt = You are a support triage engine. Classify this ticket:\n${text}; const llmResponse = await callLLM(prompt); const triage = JSON.parse(llmResponse); // untyped, unvalidated 🤞 Sufleur turns a published prompt into a typed function in your own repo: // after — everything below is generated code, autocomplete included import { getPrompt } from './generated/prompts'; const triage = getPrompt('@sufleur/ticket-triage'); const user = triage.render('userPrompt', { text: 'Checkout has been down for an hour and we are losing orders!', }); // input shape is typed from the template's variables // …send user.prompt to any LLM SDK, then validate the response: const result = triage.parseOutput(llmText); // zod-validated against the prompt's output schema if (result.success) { result.data.priority; // 'urgent' | 'high' | 'medium' | 'low' result.data.category; // 'bug' | 'billing' | … | 'outage' | 'other' } The generated file inlines everything — no vendor SDK, no runtime fetches. Its only runtime deps are mustache and zod (or chevron and pydantic for Python). Quickstart Zero to that typed call in under five minutes. Public prompts need no account or API key: npm i -g @sufleur/cli # or: pip install sufleur-cli sufleur init # scaffold sufleur.yaml — accept the defaults sufleur add @sufleur/ticket-triage # resolve, fetch, and lock the prompt sufleur generate # emit ./generated/prompts.ts (or .py) npm i mustache zod # runtime deps of the generated file # (Python: pip install chevron pydantic) sufleur.yaml declares what you depend on, sufleur-lock.yaml pins what you got, and the generated file is the only thing your code imports: # sufleur.yaml prompts: "@sufleur/ticket-triage": "*" output: language: typescript file: ./generated/prompts.ts That's it — the getPrompt(...) call above now works, with autocomplete on prompt names, entrypoints, and inputs. Browse more public prompts at https://sufleur.com/explore. Install Two prebuilt wrappers ship the same binary — pick the one that matches your project: Node / TypeScript → npm i -g @sufleur/cli · wrapper README · npm Python → pip install sufleur-cli · wrapper README · PyPI Or grab a binary directly from the GitHub releases page. What's in it Consumer side — install and generate: Command Purpose sufleur init Scaffold sufleur.yaml sufleur add @ws/name [range] Add a prompt, fetch it, update the lockfile sufleur add @ws/+collection Add every prompt in a collection (each under its own @ws/name key), then install sufleur install [--frozen] Resolve the manifest, fetch what's missing, refresh the lockfile sufleur update [@ws/name] Re-resolve one or all prompts sufleur generate Regenerate the typed .ts / .py file from the lockfile The generated file inlines every prompt (no runtime fetches) and exposes getPrompt(name) / get_prompt(name) with a typed render(...) plus an optional parseOutput(...) / parse_output(...) for prompts that declare an output schema. Authoring side — login and CRUD: Group Commands Auth login, logout, me Workspaces workspace list Prompts prompt create / get / list / update Versions version draft / list / get / delete / set-metadata / delete-metadata / set-output-schema / set-model-config / set-readme / get-readme / dump Files file create / update / delete / list / set-entrypoint Evals eval get / validate / push / delete / run / runs / show / watch / cases / case Datasets dataset create / get / list / update / dump, plus dataset version / schema / cases subgroups Collections collection create / get / list-prompts / link / set-readme / set-description Local render prompt render --entrypoint --vars '{...}' Every authoring command accepts --json for machine-readable output. See the wrapper READMEs for the full table. Collections A collection is a workspace-scoped group of prompts, referenced as @workspace/+name — the + marks it as a collection (prompt names can never contain +). Collections have no draft→publish workflow; every edit is applied immediately. sufleur add @ws/+name — install the collection: add each member prompt to sufleur.yaml under its own @ws/prompt key (constraint *) and resolve. Prompts already present are reported and skipped (use --force to reset them to *). sufleur collection create @ws/+name [--description ...] — create a new (private) collection. sufleur collection list-prompts @ws/+name — print the member prompts, one @ws/name per line (pipe into version dump / add). sufleur collection link @ws/+name @ws/prompt [--force] — add a prompt to a collection. A prompt belongs to at most one collection, so moving one already in another collection requires --force. sufleur collection set-readme @ws/+name [--content ... | --file ...] / set-description ... — document the collection. sufleur collection get @ws/+name — show metadata + README. Evals An eval scores a prompt version against a dataset — it pins the dataset, the candidate's input mapping, optional LLM judges, CEL assertions over the output, and a passing threshold. (The provider, model, and parameters the candidate and judges run with come from each prompt version's model config — set with sufleur version set-model-config or in the web app.) Evals are authored as YAML on a draft version; the loop mirrors prompt editing: sufleur eval get @ws/name@draft --file ./eval.yaml — dump the current eval YAML (a ready-to-edit skeleton if none exists). sufleur eval validate @ws/name@draft --file ./eval.yaml — parse and type-check; saves nothing. sufleur eval push @ws/name@draft --file ./eval.yaml — validate, then save. sufleur eval run @ws/name@draft [--watch] — enqueue a run; with --watch it streams to completion and exits non-zero on a failing verdict, so it works as a CI gate. sufleur eval runs / show / watch — list, summarise, and follow runs. sufleur eval cases [--failed] / eval case [--prompts] — drill into a succeeded run's per-case results: the pass/fail table, then a single case's inputs, output, assertions, and judges. An eval pins a dataset version through dataset.ref — and those datasets are authored from the CLI too (see below). Full guide: https://sufleur.com/docs/evals. Datasets A dataset is a workspace-scoped, versioned collection of cases (one JSON object per case) plus a JSON Schema describing their shape — the data an eval runs against. Datasets use the same draft→publish lifecycle as prompts, and — like prompts — publishing a version and changing visibility are web-app only. The CLI covers everything up to publish: create, draft, cases, schema, and validation. sufleur dataset create @ws/name [--description ...] — create a dataset and its initial draft (private). sufleur dataset dump @ws/name@draft --to ./ds — write schema.json, cases.jsonl, and dataset.yaml to a directory for local editing. sufleur dataset cases push @ws/name@draft --file ./ds/cases.jsonl — upload cases (JSONL, JSON array, or CSV; format detected from the extension). The schema is inferred on the first upload. sufleur dataset schema set @ws/name@draft --file ./ds/schema.json — refine the inferred JSON Schema. sufleur dataset version validate @ws/name@draft — check every case against the schema (exits non-zero on a violation). Publishing in the app is gated on this passing. sufleur dataset version draft @ws/name — open the next draft once a version has been published in the app. sufleur dataset cases pull @ws/name@version --to cases.jsonl — download a version's cases. Reference a published version from an eval with dataset.ref: "@ws/[email protected]" (raw semver, no v; or the literal draft while iterating). Full guide: https://sufleur.com/docs/datasets. For coding agents sufleur skill prints a markdown skill description — when to use the CLI, FQ-name format (@workspace/name@version), the full command surface, JSON flags — ready to drop into the agent of your choice. The skill ships inside the binary, so it always matches the version on your PATH. # Claude Code (each skill is a directory with a SKILL.md inside) mkdir -p ~/.claude/skills/sufleur && sufleur skill > ~/.claude/skills/sufleur/SKILL.md # Cursor sufleur skill > .cursor/rules/sufleur.md Hand the agent the skill plus sufleur login, and it can drive the whole authoring loop — drafting versions, editing files, setting metadata, rendering locally — without you typing each command. Links Platform — author and manage prompts: https://sufleur.com Node wrapper — wrappers/npm/README.md Python wrapper — wrappers/pip/README.md License MIT. Topics Resources Readme Activity Custom properties Stars 1 star Watchers 0 watching Forks 0 forks Report repository