AgentFlow workflow skill. Use this skill when the user needs 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 tracking, and crash-proof pipeline execution and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
AgentFlow workflow skill. Use this skill when the user needs 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 tracking, and crash-proof pipeline execution and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/agentflow from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
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 the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
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
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
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.
Write Your Spec Create a SPEC.md for your project describing what you want to build.
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.
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.
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.
Monitor and Intervene Open your Kanban board on your phone.
Watch tasks flow through the pipeline.
Drag any card to "Needs Human" to intervene.
Imported Workflow Notes
Imported: Step-by-Step Guide
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:
# 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"
Open your Kanban board on your phone. Watch tasks flow through the pipeline. Drag any card to "Needs Human" to intervene. Run /sdlc-health for a terminal dashboard.
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 configcp -r agentflow/skills/* ~/.claude/skills/
cp -r agentflow/prompts/* ~/.claude/sdlc/prompts/
cp agentflow/conventions.md ~/.claude/sdlc/conventions.md
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, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
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 to check status and manually drag the card back to Backlog.
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.
@00-andruia-consultant-v2 - 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.
@10-andruia-skill-smith-v2 - 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.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
