Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add Peeyushmeher/agent-agile/plugin install agent-agileWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/peeyushmeher/agent-agile/aa-autopilot)<a href="https://agentmods.dev/skills/peeyushmeher/agent-agile/aa-autopilot"><img src="https://agentmods.dev/badge/skills/peeyushmeher/agent-agile/aa-autopilot/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/peeyushmeher/agent-agile/aa-autopilot"><img src="https://agentmods.dev/badge/skills/peeyushmeher/agent-agile/aa-autopilot.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.01334 |
| Opus 5 | $0.00021 | $0.00667 |
| Sonnet 5 | $0.00008 | $0.00267 |
| Haiku 4.5 | $0.00004 | $0.00133 |
Grade A, and why
aa-autopilot scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
aa-autopilot
Resolve the Agent-Agile playbook root: use the first of these that exists — (1) ${CLAUDE_PLUGIN_ROOT}/playbooks, (2) ./.claude/agent-agile/playbooks, (3) ./.agents/agent-agile/playbooks, (4) ~/.claude/agent-agile/playbooks, (5) ~/.agents/agent-agile/playbooks, (6) ./playbooks.
Read playbooks/execution.md sections "Autopilot" (including "The ambiguity protocol"), "Circuit breakers", and "Resume protocol", and follow them exactly; do not re-derive or improvise the loop or soften a circuit breaker.
Wiring
- Gate mode. Parse a
--gate full-auto|checkpointargument. If absent, use thegatefield in.planning/CONFIG.md. If neither is set, the mode is interactive. - Preflight. Run the same collision check and
PREREQS.mdcheck as/aa-execute-epic's pre-flight, before the loop starts at all — an autopilot run never begins on an unchecked epic. - The loop. For each pending epic in
.planning/ROADMAP.md, in order:- Run the
/aa-plan-epicbehavior to slice the next pending epic into worker-ready story cards. - Run the
/aa-execute-epicbehavior for that epic: Wave 0 through Verification. - Re-run the pre-flight prerequisite check for this epic specifically before Wave 1 launches — prerequisites can go stale between epics (a key expires, a quota runs out), so this is deliberate, not redundant with step 2.
- Apply the gate for this epic:
- Full-auto — the verifier's verdict stands in for the human: pass approves, a redo-list triggers the redo path. Demo briefs accumulate so a human can review the whole run's
DEMO.mdfiles at the end. - Checkpoint — the epic runs and verifies automatically; notify the user only between epics, at natural pause points, rather than gating every single one.
- Interactive — stop and present
DEMO.mdand the verifier's verdict to the user at this epic, exactly as a standalone/aa-execute-epicrun would.
- Full-auto — the verifier's verdict stands in for the human: pass approves, a redo-list triggers the redo path. Demo briefs accumulate so a human can review the whole run's
- On approve, the integrator has already written
LEARNINGS.mdand flipped theROADMAP.mdrow (per Wave 2); move to the next pending epic. - On redo or replan, follow "The review gate" in
playbooks/execution.md, then resume the loop from that epic once it re-verifies.
- Run the
- Circuit breakers. Follow the circuit breakers in
playbooks/execution.md"Circuit breakers" exactly as written — never push through or work around one (a panel-refresh BLOCK left unresolved at slicing time also stops the loop, perplaybooks/critics.md"Panel refresh"):- An epic fails verification even after its full redo — stop the autopilot loop entirely. The most an epic ever gets is: fail → scoped redo (if eligible) → fail → full redo → fail → stop.
- A prerequisite goes missing mid-run — stop immediately, reset its
PREREQS.mdstatus topending, and notify whoever needs to resupply it. - Never fake a credential and never silently mock a missing paid service.
- Stopping. Whenever the loop stops — a circuit breaker, an interactive gate, the end of the roadmap, or a context reset — update
.planning/STATE.mdper "Resume protocol": what's in progress, what's next, the pointers the next session needs (at most three files), and the blocker if there is one.STATE.mdmust say exactly where the run stopped and why; the next session reads only this file to resume.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 45 lines · 42 tokens per session scan A 5239f37b8c4e
aa-autopilot is a skill published in the GitHub repository Peeyushmeher/agent-agile (3 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 1,334 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
project-manager
This skill has been upgraded with agentic AI capabilities, OKR/KPI integration patterns, and async-first workflows based on 2026 PM best practices research.
github-project-management
Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning.
team-okrs
An OKR tracking page for a quarter. OKRs, or objectives and key results, are goals paired with measurable results.
product-frameworks
Product management frameworks for business cases, market analysis, strategy, prioritization, OKRs/KPIs, personas, requirements, and user research. Use when building ROI projections, competitive analysis, RICE scoring, OKR trees, user personas, PRDs, or usability testing plans.
gr-product-dev-ops
🇺🇸 Your dev team ships features nobody asked for while user-reported bugs pile up for months. Operations blames engineering for ignoring users; engineering blames operations for not understanding technical constraints. This gives you the complete Product × Engineering × Operations alignment SOP — from unified…
github-project-management
Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning.