Install any skill in seconds. Free to start, no credit card required.
Get Started Free →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.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | — | — |
| case-01 | ✗→✓ | ▲ Improved | — | — |
| case-17 | ✗→✓ | ▲ Improved | — | — |
| case-16 | ✗→✓ | ▲ Improved | — | — |
| case-14 | ✗→✓ | ▲ Improved | — | — |
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.
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.
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.
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.
A different AI agent reviews code and must list 3 things wrong before deciding to pass. This prevents rubber-stamp approvals.
Tasks that unblock the most downstream work get built first, automatically computing the critical path.
/spec-to-boardDecomposes a SPEC.md into atomic tasks on your Kanban board with dependencies mapped.
/sdlc-orchestrateDispatches 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-healthReal-time pipeline status dashboard showing current stage, assigned agent, retry count, and accumulated cost for every task.
/sdlc-stopGraceful shutdown: active workers finish their current task, unstarted tasks return to Backlog.
Create a SPEC.md for your project describing what you want to build.
claude -p "/spec-to-board"This reads your SPEC.md, decomposes it into atomic tasks, maps dependencies, and creates them on your Kanban board.
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"
bash# Add to crontab (runs every 15 minutes) crontab -e # Add: */15 * * * * ~/.claude/sdlc/agentflow-cron.sh >> /tmp/agentflow-orchestrate.log 2>&1
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.
claude -p "/sdlc-stop"Each stage enforces specific gates before promotion:
tsc + eslint + npm test must all pass (deterministic)Per-task cost tracking with stage ceilings (Sonnet defaults):
Automatic guardrails: warning at $3/$8, hard stop at $10/$20 (Sonnet/Opus) with human escalation.
git revert (new commit, never force-push)/sdlc-stop drains workers, returns unstarted tasks to backlogbash# 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:
bash/plugin marketplace add UrRhb/agentflow /plugin install agentflow
/spec-to-boardgit revert for safetySymptoms: 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.
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.
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.
@brainstorming - Use before AgentFlow to design your SPEC.md@writing-plans - Complements spec writing for task decomposition@test-driven-development - Works well with AgentFlow's quality gates@subagent-driven-development - Alternative approach to multi-agent coordination| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-01 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-22 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-17 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-21 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-04 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-20 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-07 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-16 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-11 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-14 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-19 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-09 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-10 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-03 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-05 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-06 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-08 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-12 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-13 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-15 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-18 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +59 percentage points is the difference between those two pass rates over the 22 comparable cases.
The per-case answers from this run were removed by the retention sweep, so the case table below shows the verdicts without the text either arm produced. The counts above were recorded at the time and are unaffected. Answers are now kept for 180 days.
Other measured skills in the registry, with their headline benchmark lift.