dev-pipeline

name	dev-pipeline
description	Multi-agent development pipeline (Orchestrator + workers + consensus vote). Use when the user asks to "build a feature", "fix a bug with the pipeline", or "run the dev pipeline". Triggers on "dev pipeline", "multi-agent pipeline", "run pipeline".
allowed-tools	Read, Write, Edit, Bash, Grep, Glob, Task

Development Pipeline Skill

Use this skill when the user asks to build a feature, fix a bug, or implement a plan using the multi-agent development pipeline.

Spec-driven phasing (gated workflow)

The run_dev_pipeline MCP tool implements a 4-phase gated workflow. Do not advance to the next phase until the current one is validated.

SPECIFY ──→ PLAN ──→ TASKS ──→ IMPLEMENT
   │          │        │          │
   ▼          ▼        ▼          ▼
 vote()     vote()   vote()     vote() (per task)

Phase 1 — Specify

Before producing the spec, surface assumptions explicitly:

ASSUMPTIONS I'M MAKING:
1. This runs in the existing nexus-agents MCP server (not a new process)
2. New CLI flag goes through the existing dispatch table (cli-command-catalog.ts)
3. Storage uses the existing FileAuditStorage path (not a new backend)
4. Output schema follows the Result<T, E> pattern per CLAUDE.md
→ Correct me now or I'll proceed with these.

The spec's purpose is to surface misunderstandings before code gets written. Silent assumptions are the most dangerous form of misunderstanding.

Phase 2 — Plan

Convert the spec into a sequenced plan: which files change, in what order, with what tests. Each step references the canonical path (per CLAUDE.md "Canonical Paths") so future readers can navigate.

Phase 3 — Tasks

Break the plan into atomic tasks suitable for fan-out. Each task has:

• A clear acceptance criterion (the test that asserts success)
• A bounded output (no "list all", per .rules/subagent-coordination.md)
• A scope (one directory or one canonical path, not "the codebase")

Phase 4 — Implement

Dispatch tasks per the orchestration-patterns reference (waves of 3-4, output budgets, status lines). Parent context summarizes each subagent result to 2-3 bullets — never inline raw outputs (see context-engineering skill).

When to Use

• User says "use the pipeline to build X"
• User says "run the dev pipeline"
• User provides a plan file or spec and wants multi-agent execution
• Complex tasks that benefit from research→plan→vote→implement→QA flow

How to Use

Call the run_dev_pipeline MCP tool:

run_dev_pipeline({
  task: "Build a health check endpoint",     // Direct instructions
  // OR
  planFile: "/path/to/plan.md",              // Read from file

  repo: "owner/repo",                        // Track progress on GitHub issues
  trackerBackend: "github",                  // or "gitlab" or "json"
  mode: "autonomous",                         // "harness" = stop after decompose, return tasks
  dryRun: false,                             // true = stop after plan+vote
  simulateVotes: false,                      // TESTS ONLY — random output, never use for real decisions
  sessionId: "my-session-id",                // Enable checkpoint/resume (crash recovery)
  maxVoteIterations: 3,                      // plan→vote loop limit
  maxQaIterations: 3,                        // QA review loop limit
  scanTarget: "/path/to/repo",              // security scan directory
})

Pipeline Flow

RESEARCH → research expert gathers context
PLAN → architecture expert creates plan
VOTE → consensus vote (higher_order Bayesian strategy)
  ↳ rejected? feedback → replan → revote (up to 3x)
PM DECOMPOSE → PM expert splits into tasks
PARALLEL IMPLEMENT → code experts work tasks concurrently
QA REVIEW → QA expert reviews each task
  ↳ needs_work? feedback → re-implement (up to 3x)
SECURITY SCAN → SARIF/Semgrep blocks on critical/high
SHIP ✓

Output Format

The tool returns structured JSON:

{
  "completed": true,
  "securityPassed": true,
  "voteIterations": 2,
  "qaIterations": 3,
  "plan": "...",
  "tasks": [
    {
      "id": "task-1",
      "title": "Add endpoint",
      "status": "done",
      "implementation": "export function health() { ... }",
      "feedback": null
    }
  ]
}

After Pipeline Completes

Autonomous mode (default): Implementations are in each task's implementation field.

1. Read the implementation text from each task
2. Use your own tools (Read/Edit/Write) to apply the implementations
3. Run tests to verify
4. Commit and push

Harness mode (mode: "harness"): Pipeline returns decomposed tasks — YOU implement them.

1. Pipeline runs research→plan→vote→decompose and returns the task list
2. Each task has id, title, description, assignedTo — but no implementation
3. Use your own tools (Read/Edit/Write/Bash) to implement each task
4. Run tests, iterate, commit

Tips

• Use dryRun: true first to review the plan before committing to implementation
• Use sessionId to enable crash recovery — pipeline resumes from last completed stage
• simulateVotes: true is for unit tests only — its votes are random and must not be used as a fallback when adapters are missing. If no adapter is available, configure one rather than simulating.
• Provide repo to get GitHub issue tracking of every pipeline stage
• The pipeline uses CompositeRouter for intelligent CLI selection (weather-aware, LinUCB)
• Each expert gets its system prompt (research, architecture, PM, code, QA)
• Vote feedback propagates back to the plan stage for iterative refinement
• Memory integration: prior learnings seed research, QA outcomes write back to SessionMemory
• Outcome store + weather report + trend detection inform the plan stage

Anti-rationalization — Dev pipeline

Excuse	Counter
"Skip the spec, just code"	The spec catches assumption mismatches before they cost a rewrite. Even a 5-line spec is better than zero.
"Skip the vote, I know what's right"	The vote isn't agreement-seeking; it's blind-spot detection. Cheap, fast, valuable.
"We don't need consensus for routine work"	Correct — use quickMode and simple_majority for routine. Skip the vote entirely only for trivial fixes.
"Just dispatch a giant subagent for the whole thing"	Wave-of-3-4 with bounded outputs (.rules/subagent-coordination.md). Giant subagents return giant outputs that flood parent context.
"I'll inline the subagent results into my context"	Summarize to 2-3 bullets per result; re-read the file if details needed. Inlining destroys parent context budget.

Red flags

• Pipeline run with simulateVotes: true for a non-test path
• Subagent prompt > 500 words
• Output budget unstated in subagent prompt
• More than 4 agents in a single wave without explicit reason
• Parent context inlining raw subagent results
• Phase advanced without the gate vote completing

Verification checklist

• Each phase gate (SPECIFY / PLAN / TASKS / IMPLEMENT) recorded its vote
• Subagent prompts under 500 words; output budget stated; status line required
• Wave size ≤ 4 parallel agents; next wave waits for current to finish
• Parent summarized each result to 2-3 bullets before continuing
• Final implementation passes pnpm lint && pnpm typecheck && pnpm test
• Discoveries from subagents re-verified by parent before filing (4-point gate per CLAUDE.md)