Pipeline subagents
Design and execute a custom multi-agent pipeline using Claude Code subagents directly. Spawns researcher, builder, etc. via the Agent tool with native parallel and background execution. Use when the user says 'run a pipeline for X' and factory subagents are available.From its SKILL.md
npx -y skills add akashgit/remote-factory --skill pipeline-subagentsAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
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Pipeline (Subagents) — Dynamic Multi-Agent Orchestrator
You design and execute custom multi-agent pipelines using Claude Code's native Agent tool to spawn factory subagents directly.
The user wants: $ARGUMENTS
Setup
mkdir -p .factory/pipeline
Your Agents
Spawn specialists using the Agent tool with the plugin-namespaced subagent type factory:<role>:
Agent({
description: "<short description>",
prompt: "<detailed task>",
subagent_type: "factory:<role>"
})
| Subagent Type | Purpose |
|---|---|
| factory:researcher | Web research, codebase analysis, domain studies |
| factory:strategist | Generate prioritized hypotheses from observations |
| factory:builder | Implement code changes on a feature branch, open PRs |
| factory:qa | Health checks, code review, adversarial QA verification |
| factory:archivist | Record findings to .factory/archive/ |
| factory:distiller | Refine vague ideas into buildable specs |
Also available: factory:failure_analyst (classify experiment failures by root cause — use when analyzing why a prior experiment failed).
Parallel Execution
Issue multiple Agent tool calls in the same message — they run concurrently:
Agent({ subagent_type: "factory:researcher", prompt: "Research the auth bug..." })
Agent({ subagent_type: "factory:qa", prompt: "Run baseline eval..." })
This is concurrent execution via parallel tool calls, not shell backgrounding. Each Agent call is still individually synchronous.
Background Execution
For non-blocking steps (e.g., archival):
Agent({ subagent_type: "factory:archivist", prompt: "Archive findings...", run_in_background: true })
Phase 1: Design the Pipeline
- Understand the goal — what outcome is desired? Which agents are needed?
- Inspect project state (use Bash tool):
ls .factory/config.json 2>/dev/null && cat .factory/config.json - Write the pipeline plan to
.factory/pipeline/plan.md:
## Pipeline: <goal summary>
### Steps
| Step | Role | Task Summary | Depends On |
|------|------|-------------|-----------|
| S1 | researcher | ... | - |
| S2 | qa | ... | - |
| S3 | strategist | ... | S1, S2 |
| ... | ... | ... | ... |
### Gate Rules
- After S1: PROCEED if ...; REDIRECT if ...
- After S3: PROCEED if ...; ABORT if ...
Design Principles
- Minimize invocations — only agents needed for this goal
- Maximize parallelism — steps whose dependencies are all satisfied and that don't depend on each other can be spawned in the same message
- Mandatory archival — always include at least one archivist step at the end
- Gate rules — define PROCEED/REDIRECT/ABORT criteria for critical transitions
Phase 2: Execute the Pipeline
Process steps in topological order:
- Identify next batch — steps whose dependencies are all complete
- Build prompts — incorporate output from prior steps (agent results are returned directly)
- Emit start event and invoke agents:
Then invoke via Agent tool (single, parallel batch, or background for archival).factory emit agent.started --agent <role> --project "$(pwd)" - Read results — Agent tool returns subagent output directly
- Persist review and emit completion:
Then use the Write tool to save the agent output tomkdir -p .factory/reviews factory emit agent.completed --agent <role> --project "$(pwd)".factory/reviews/<role>-latest.md - Apply gate rule:
- PROCEED: Move to next step
- REDIRECT: Re-invoke with corrections (max 2 per step)
- ABORT: Skip downstream steps, jump to summary
- Repeat until done
Error Recovery
- Agent error: retry once with simpler prompt
- 2 consecutive failures: ABORT pipeline
Final Summary
Write .factory/pipeline/summary.md with goal, status, step results, and key findings.