agentic-workflow

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Repo stars 3,783
Author repo Continuous-Claude-v3

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Agentic Workflow Pattern Standard multi-agent pipeline for implementation tasks. Task(subagenttype="oracle", runin_background=tr…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Architecture Principles”, “Workflow Stages”, “1. Research Agent”, “2. Planning Agent”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name agentic-workflow directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Architecture Principles / Workflow Stages / 1. Research Agent” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read / write; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Agentic Workflow Pattern

Standard multi-agent pipeline for implementation tasks.

Architecture Principles

Use run_in_background: true for all agents to keep main context minimal
Use Task tool (never TaskOutput) to avoid receiving full agent transcripts
Agents write outputs to .claude/cache/agents/<stage>/ for injection into subsequent agents
Main conversation is pure orchestration — no heavy lifting, only coordination

Workflow Stages

1. Research Agent

Task(subagent_type="oracle", run_in_background=true, prompt="""
Query NIA Oracle (via /nia-docs skill) to verify approach and gather best practices.

Output to: .claude/cache/agents/oracle/<task>-research.md
""")

Enforce NIA as the research layer
Output: Research findings

2. Planning Agent

Task(subagent_type="plan-agent", run_in_background=true, prompt="""
Read: .claude/cache/agents/oracle/<task>-research.md
Use RP-CLI to analyze the target codebase section.
Generate implementation plan informed by research.

Output to: .claude/cache/agents/plan-agent/<task>-plan.md
""")

Receives: Research agent output as context
Output: Implementation plan

3. Validation Agent

Task(subagent_type="validate-agent", run_in_background=true, prompt="""
Read: .claude/cache/agents/plan-agent/<task>-plan.md
Read: .claude/cache/agents/oracle/<task>-research.md
Review plan against research findings and best practices.

Output to: .claude/cache/agents/validate-agent/<task>-validated.md
""")

Reviews plan against research
Output: Validated plan with amendments

4. Implementation Agent

Task(subagent_type="agentica-agent", run_in_background=true, prompt="""
Read: .claude/cache/agents/validate-agent/<task>-validated.md
Read: .claude/cache/agents/oracle/<task>-research.md

TDD approach: Write failing tests FIRST, then implement.
Run tests to verify.

Output summary to: .claude/cache/agents/implement-agent/<task>-implementation.md
""")

Receives: Validated plan + research context
TDD: Failing tests first
Output: Implementation + tests

5. Review Agent

Task(subagent_type="review-agent", run_in_background=true, prompt="""
Read: .claude/cache/agents/implement-agent/<task>-implementation.md
Read: .claude/cache/agents/validate-agent/<task>-validated.md
Read: .claude/cache/agents/oracle/<task>-research.md

Cross-reference implementation against plan and research.
Run tests to confirm passing.

Output to: .claude/cache/agents/review-agent/<task>-review.md
""")

Cross-references all artifacts
Confirms tests pass
Output: Review summary

Agent Progress Monitoring

# Watch for system reminders:
# "Agent a42a16e progress: 6 new tools used, 88914 new tokens"

# Poll for output files:
find .claude/cache/agents -name "*.md" -mmin -5

# Check task file size growth:
wc -c /tmp/claude/.../tasks/<id>.output

Stuck detection:

Progress reminders stop arriving
Task output file size stops growing
Expected output file not created after reasonable time

Directory Structure

.claude/cache/agents/
├── oracle/
│   └── <task>-research.md
├── plan-agent/
│   └── <task>-plan.md
├── validate-agent/
│   └── <task>-validated.md
├── implement-agent/
│   └── <task>-implementation.md
└── review-agent/
    └── <task>-review.md

Key Rules

Never use TaskOutput - floods context with 70k+ token transcripts
Always run_in_background=true - isolates agent context
File-based handoff - each agent reads previous agent's output file
Poll, don't block - check file system for outputs, don't wait
TDD in implementation - failing tests first, then make them pass

Source

Session 2026-01-01: SDK Phase 3 implementation using this pattern

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: AI
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 88 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @parcadei · no license declared
Fluxly token estimate: Lean
Fluxly setup estimate: Plug-and-play
External API key: No requirement detected
Detected OS requirements: Unspecified
Runtime requirements: Unspecified
Detected file/system behavior: Read-only
Write / modify
Detected network behavior: Local-only
Install commands: None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview agentic-workflow.preview

The current SKILL.md does not define a fixed output example.

SKILL.md SKILL.md

# Agentic Workflow Pattern

Standard multi-agent pipeline for implementation tasks.

## Architecture Principles

- Use `run_in_background: true` for all agents to keep main context minimal
- Use `Task` tool (never `TaskOutput`) to avoid receiving full agent transcripts
- Agents write outputs to `.claude/cache/agents/<stage>/` for injection into subsequent agents
- Main conversation is pure orchestration — no heavy lifting, only coordination

## Workflow Stages

### 1. Research Agent
```
Task(subagent_type="oracle", run_in_background=true, prompt="""
Query NIA Oracle (via /nia-docs skill) to verify approach and gather best practices.

Output to: .claude/cache/agents/oracle/<task>-research.md
""")
```
- Enforce NIA as the research layer
- Output: Research findings

### 2. Planning Agent
```
Task(subagent_type="plan-agent", run_in_background=true, prompt="""
Read: .claude/cache/agents/oracle/<task>-research.md
Use RP-CLI to analyze the target codebase section.
Generate implementation plan informed by research.

Output to: .claude/cache/agents/plan-agent/<task>-plan.md
""")
```
- Receives: Research agent output as context
- Output: Implementation plan

### 3. Validation Agent
```
Task(subagent_type="validate-agent", run_in_background=true, prompt="""
Read: .claude/cache/agents/plan-agent/<task>-plan.md
Read: .claude/cache/agents/oracle/<task>-research.md
Review plan against research findings and best practices.

Output to: .claude/cache/agents/validate-agent/<task>-validated.md
""")
```
- Reviews plan against research
- Output: Validated plan with amendments

### 4. Implementation Agent
```
Task(subagent_type="agentica-agent", run_in_background=true, prompt="""
Read: .claude/cache/agents/validate-agent/<task>-validated.md
Read: .claude/cache/agents/oracle/<task>-research.md

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> Architecture Principles → Workflow Stages → 1. Research Agent → 2. Planning Agent → 3. Validation Agent → 4. Implementation Agent
terms    -> TDD · Stuck detection · Never use TaskOutput · Always runinbackground=true · File-based handoff · Poll, don't block · TDD in implementation
files/cmd -> runinbackground: true · Task · TaskOutput · .claude/cache/agents/<stage>/
body sha256 -> 9a70e00ebaf3