AgentFlow

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/agentflow

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

AgentFlow

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Core Concepts, Quality Gates, Cost Tracking, Safety and Recovery, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when you need to orchestrate multiple Claude Code workers across a full development lifecycle (build, review, test, integrate)
• Use when you want deterministic quality gates (tsc/eslint/tests) before AI review on AI-generated code
• Use when you want full pipeline visibility from your Kanban board or phone
• Use when running a solo or team project that needs autonomous task dispatch with cost tracking
• Use when you need crash-proof orchestration that survives session restarts
• Use when the request clearly matches the imported source intent: Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost....

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Write Your Spec Create a SPEC.md for your project describing what you want to build.
2. Decompose Into Tasks ` claude -p "/spec-to-board" This reads your SPEC.md, decomposes it into atomic tasks, maps dependencies, and creates them on your Kanban board.
3. Start Workers Open 3-4 terminal windows, each as a worker slot: bash # Terminal 2 — Builder claude -p "/sdlc-worker --slot T2" # Terminal 3 — Builder claude -p "/sdlc-worker --slot T3" # Terminal 4 — Reviewer claude -p "/sdlc-worker --slot T4" # Terminal 5 — Tester claude -p "/sdlc-worker --slot T5" ### 4.
4. Start the Orchestrator bash # Add to crontab (runs every 15 minutes) crontab -e # Add: /15 ~/.claude/sdlc/agentflow-cron.sh >> /tmp/agentflow-orchestrate.log 2>&1 ### 5.
5. Monitor and Intervene Open your Kanban board on your phone.
6. Watch tasks flow through the pipeline.
7. Drag any card to "Needs Human" to intervene.

Imported Workflow Notes

Imported: Step-by-Step Guide

1. Write Your Spec

Create a

SPEC.md

2. Decompose Into Tasks

claude -p "/spec-to-board"

This reads your SPEC.md, decomposes it into atomic tasks, maps dependencies, and creates them on your Kanban board.

3. Start Workers

Open 3-4 terminal windows, each as a worker slot:

# Terminal 2 — Builder
claude -p "/sdlc-worker --slot T2"

# Terminal 3 — Builder
claude -p "/sdlc-worker --slot T3"

# Terminal 4 — Reviewer
claude -p "/sdlc-worker --slot T4"

# Terminal 5 — Tester
claude -p "/sdlc-worker --slot T5"

4. Start the Orchestrator

# Add to crontab (runs every 15 minutes)
crontab -e
# Add: */15 * * * * ~/.claude/sdlc/agentflow-cron.sh >> /tmp/agentflow-orchestrate.log 2>&1

5. Monitor and Intervene

Open your Kanban board on your phone. Watch tasks flow through the pipeline. Drag any card to "Needs Human" to intervene. Run

/sdlc-health

6. Stop the Pipeline

claude -p "/sdlc-stop"

Imported: Installation

# Clone the repo
git clone https://github.com/UrRhb/agentflow.git

# Copy skills and prompts to your Claude Code config
cp -r agentflow/skills/* ~/.claude/skills/
cp -r agentflow/prompts/* ~/.claude/sdlc/prompts/
cp agentflow/conventions.md ~/.claude/sdlc/conventions.md

Or install as a Claude Code plugin:

/plugin marketplace add UrRhb/agentflow
/plugin install agentflow

Imported: Overview

AgentFlow turns your existing Kanban board into a fully autonomous AI development pipeline. Instead of building custom orchestration infrastructure, it treats your project management tool (Asana, GitHub Projects, Linear) as a distributed state machine — tasks move through stages, AI agents read and write state via comments, and humans intervene through the same UI they already use.

The result is complete pipeline observability from your phone, free crash recovery (state lives in your PM tool, not in memory), and human override at any point by dragging a card.

Imported: Core Concepts

7-Stage Kanban Pipeline

Tasks flow through: Backlog, Research, Build, Review, Test, Integrate, Done. Each stage has specific gates. The Kanban board IS the orchestration layer — no separate database, no message queue, no custom infrastructure.

Stateless Orchestrator

A crontab-driven one-shot sweep runs every 15 minutes. No daemon, no session dependency. If it crashes, the next sweep picks up where it left off because all state lives in your PM tool.

Deterministic Before Probabilistic

Hard gates (tsc + eslint + tests) run before any AI review, catching roughly 60% of issues at near-zero cost. AI review comes after, as a second layer.

Adversarial Review

A different AI agent reviews code and must list 3 things wrong before deciding to pass. This prevents rubber-stamp approvals.

Transitive Priority Dispatch

Tasks that unblock the most downstream work get built first, automatically computing the critical path.

Examples

Example 1: Ask for the upstream workflow directly

Use @agentflow to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @agentflow against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @agentflow for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @agentflow using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Skills / Commands

/spec-to-board

Decomposes a SPEC.md into atomic tasks on your Kanban board with dependencies mapped.

/sdlc-orchestrate

Dispatches tasks to workers based on transitive priority and conflict detection. Runs as a crontab sweep.

/sdlc-worker --slot <N>

Runs a worker in a terminal slot that picks up tasks, builds code, and creates PRs. Run 3-4 workers in parallel.

/sdlc-health

Real-time pipeline status dashboard showing current stage, assigned agent, retry count, and accumulated cost for every task.

/sdlc-stop

Graceful shutdown: active workers finish their current task, unstarted tasks return to Backlog.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Do: Write a clear SPEC.md before running /spec-to-board
• Do: Start with 3-4 workers for a typical project
• Do: Monitor from your Kanban board and drag cards to "Needs Human" when needed
• Do: Review LEARNINGS.md periodically — it captures common failure patterns
• Don't: Skip the deterministic quality gates — they catch most issues cheaply
• Don't: Force-push to main — AgentFlow uses git revert for safety
• Don't: Run more workers than your project's parallelism supports

Imported Operating Notes

Imported: Best Practices

• Do: Write a clear SPEC.md before running

  /spec-to-board

• Do: Start with 3-4 workers for a typical project
• Do: Monitor from your Kanban board and drag cards to "Needs Human" when needed
• Do: Review LEARNINGS.md periodically — it captures common failure patterns
• Don't: Skip the deterministic quality gates — they catch most issues cheaply
• Don't: Force-push to main — AgentFlow uses

  git revert

  for safety
• Don't: Run more workers than your project's parallelism supports

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/agentflow

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Imported Troubleshooting Notes

Imported: Troubleshooting

Problem: Worker appears stuck or dead

Symptoms: Task card hasn't moved in 15+ minutes, no new comments Solution: The orchestrator detects dead agents via heartbeat and reassigns after 10 minutes. If the issue persists, run

/sdlc-health

Problem: Cost guardrail triggered

Symptoms: Task moved to "Needs Human" with COST:CRITICAL tag Solution: Review the task's comment thread for accumulated context. Decide whether to increase the budget, simplify the task, or split it into smaller pieces.

Problem: Integration test failure after merge

Symptoms: Task auto-reverted from main Solution: The auto-revert preserves main stability. Check the task's retry context in comments, which carries what was tried and what failed. The next worker assigned will use this context.

Related Skills

• @00-andruia-consultant

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @10-andruia-skill-smith

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @20-andruia-niche-intelligence

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @3d-web-experience

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Additional Resources

• AgentFlow Repository
• Architecture Documentation
• Gap Registry (45 failure modes)
• Getting Started Guide

Imported: Quality Gates

Each stage enforces specific gates before promotion:

• Build to Review:

  tsc

  +

  eslint

  +

  npm test

  must all pass (deterministic)
• Review to Test: Adversarial reviewer must list 3 issues before passing
• Test to Integrate: 80% coverage threshold on new files
• Integrate to Done: Full test suite on main after merge; auto-reverts on failure

Imported: Cost Tracking

Per-task cost tracking with stage ceilings (Sonnet defaults):

• Research: ~$0.10
• Build: ~$0.40
• Review: ~$0.10
• Test: ~$0.05
• Integrate: ~$0.03

Automatic guardrails: warning at $3/$8, hard stop at $10/$20 (Sonnet/Opus) with human escalation.

Imported: Safety and Recovery

• Auto-revert: Integration failures trigger

  git revert

  (new commit, never force-push)
• Blocked tasks: After 2 failed attempts, tasks escalate to human review
• Dead agent detection: Heartbeat every 5 min, reassign after 10 min timeout
• Graceful shutdown:

  /sdlc-stop

  drains workers, returns unstarted tasks to backlog
• Scope creep detection: PR diff files compared against predicted files list
• Spec drift detection: SHA-256 hash comparison catches requirement changes mid-sprint

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.