Skip to main content

agent-team

Spec-driven workflow orchestrator. Walks a task through six stages — intake (Claude revises the user's raw prompt), interview (Claude asks multi-choice questions), research (Claude spawns researcher workers in parallel and synthesizes findings), spec compilation (Claude writes the spec), validation strategy (Claude spawns level-specialist workers in parallel and consolidates a multi-level plan), and execution with streaming validation (Claude implements; per-unit validators run in parallel against the strategy). Persists every artifact under specs/{task_type}/{task_name}/ so any stage is resumable. Invoke when the user wants to start, resume, or run a spec-driven task.

Quellinformationen

Repository
finalde/spec_coding
Letzte Quellaktivität
28. Juli 2026 um 12:48
Erkannte Sprache von SKILL.md
Englisch
Sterne
0
Forks
0

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

Datei-Explorer
4 Dateien

SKILL.md wird angezeigt

SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
agent_team
description
Spec-driven workflow orchestrator. Walks a task through six stages — intake (Claude revises the user's raw prompt), interview (Claude asks multi-choice questions), research (Claude spawns researcher workers in parallel and synthesizes findings), spec compilation (Claude writes the spec), validation strategy (Claude spawns level-specialist workers in parallel and consolidates a multi-level plan), and execution with streaming validation (Claude implements; per-unit validators run in parallel against the strategy). Persists every artifact under specs/{task_type}/{task_name}/ so any stage is resumable. Invoke when the user wants to start, resume, or run a spec-driven task.
# agent_team — spec-driven workflow You drive a task through six stages, persisting every artifact so the user can resume at any stage. Coordinated stages (2, 3, 5, 6) use the parent-direct model — see `CLAUDE.md` § Tool scoping and team coordination for the rationale; this skill only covers the entry-point flow. ## Inputs (collect from the user if not given) - `task_type` — required, enum: `development | ai_video`. Ask if unclear; never invent. - `task_name` — slug, no spaces (e.g., `spec_driven`). - `raw_prompt` — what the user wants to build. Build `task_id = "{task_name}-{YYYYMMDD-HHmmss}"` once at run start (see `CLAUDE.md` § Task ID convention). ## Resuming If `specs/{task_type}/{task_name}/` already exists, ASK which stage to start from. Default to the first stage with missing artifacts: | Stage | Missing if … | |---|---| | 1 Intake | `user_input/revised_prompt.md` doesn't exist | | 2 Interview | `interview/qa.md` doesn't exist | | 3 Research | `findings/dossier.md` doesn't exist | | 4 Spec | `final_specs/spec.md` doesn't exist | | 5 Validation strategy | `validation/strategy.md` doesn't exist | | 6 Execution | output project folder is empty or validation hasn't run end-to-end | When resuming, read `changelog.md` first (if present) so you know which sections were already auto-patched from follow-ups. ## Follow-up prompts (between full runs) Follow-up chat that arrives between runs is handled by `CLAUDE.md` § Follow-up prompt handling — those edits do NOT invoke this skill. ## Stage flow For each coordinated stage, read its playbook AND its agent_refs files (per `CLAUDE.md` § Stage playbooks and reference docs — pre-reading contract). Record `pre_reading_consulted` on the stage's first `events.jsonl` event. ### Stage 1 — Intake 1. Save raw prompt to `specs/{task_type}/{task_name}/user_input/raw_prompt.md`. 2. Revise it: clean grammar, expand abbreviations, surface implicit constraints, structure into goal / context / desired outcome. **Don't invent requirements.** Save to `user_input/revised_prompt.md`. 3. Show to the user; iterate if they object. ### Stage 2 — Interview Run `.claude/skills/agent_team/playbooks/interview.md`. Output: `interview/qa.md`. Confirm with the user before moving on. ### Stage 3 — Research Run `.claude/skills/agent_team/playbooks/research.md`. Output: `findings/dossier.md` + per-angle files. ### Stage 4 — Spec compilation Read `revised_prompt.md` + `qa.md` + `dossier.md`. Produce `final_specs/spec.md` with these sections: - **Goal** (one paragraph) - **Out of scope** (explicit list) - **User roles & primary flows** - **Functional requirements** (numbered, each testable) - **Non-functional requirements** (performance, security, deployment as relevant) - **Acceptance criteria summary** (full criteria belong in stage 5) - **Open questions** (if any survived) Show to the user; iterate until they approve. ### Stage 5 — Validation strategy Run `.claude/skills/agent_team/playbooks/validation.md` (strategy mode). Output: `validation/strategy.md` + per-level files. ### Stage 6 — Execution + streaming validation 1. Decompose the spec into 3–8 work units (e.g., `backend_api`, `frontend_component`, `db_schema`). 2. Initialize `.audit/adhoc_agents/{YYYY-MM-DD}/{task_id}/events.jsonl`. 3. For each unit (sequentially unless explicitly independent): - Append `exec.unit.started`. - Implement into `projects/{task_name}/` or `ai_videos/{task_name}/`. - Append `exec.unit.completed`. - Run `playbooks/validation.md` (runtime mode) against the unit. - On issues: revise, append `exec.revision.applied`, re-validate. Cap per `CLAUDE.md` § Iteration bounds. 4. After all units pass, run a whole-project validation pass for end-to-end checks. For development tasks, emit `validation.requires_manual_walkthrough` after all automated levels pass and surface to the user before declaring done. ## Audit and post-mortem After the run (successful or halted), summarize for the user: stages run, workers spawned per stage, durations, surprises, recommendations.
Auf GitHub ansehen