ai-training/.cursor/skills/autopilot/SKILL.md

name, description, category, tags, disable-model-invocation

name	description	category	tags	disable-model-invocation
autopilot	Auto-chaining orchestrator that drives the full BUILD-SHIP workflow from problem gathering through deployment. Detects current project state from _docs/ folder, resumes from where it left off, and flows through problem → research → plan → decompose → implement → deploy without manual skill invocation. Maximizes work per conversation by auto-transitioning between skills. Trigger phrases: - "autopilot", "auto", "start", "continue" - "what's next", "where am I", "project status"	meta	orchestrator workflow auto-chain state-machine meta-skill	true

Autopilot Orchestrator

Auto-chaining execution engine that drives the full BUILD → SHIP workflow. Detects project state from _docs/, resumes from where work stopped, and flows through skills automatically. The user invokes /autopilot once — the engine handles sequencing, transitions, and re-entry.

File Index

File	Purpose
flows/greenfield.md	Detection rules, step table, and auto-chain rules for new projects
flows/existing-code.md	Detection rules, step table, and auto-chain rules for existing codebases
state.md	State file format, rules, re-entry protocol, session boundaries
protocols.md	User interaction, Jira MCP auth, choice format, error handling, status summary

On every invocation: read all four files above before executing any logic.

Core Principles

• Auto-chain: when a skill completes, immediately start the next one — no pause between skills
• Only pause at decision points: BLOCKING gates inside sub-skills are the natural pause points; do not add artificial stops between steps
• State from disk: current step is persisted to _docs/_autopilot_state.md and cross-checked against _docs/ folder structure
• Re-entry: on every invocation, read the state file and cross-check against _docs/ folders before continuing
• Delegate, don't duplicate: read and execute each sub-skill's SKILL.md; never inline their logic here
• Sound on pause: follow .cursor/rules/human-attention-sound.mdc — play a notification sound before every pause that requires human input (AskQuestion tool preferred for structured choices; fall back to plain text if unavailable)
• Minimize interruptions: only ask the user when the decision genuinely cannot be resolved automatically
• Single project per workspace: all _docs/ paths are relative to workspace root; for monorepos, each service needs its own Cursor workspace

Flow Resolution

Determine which flow to use:

1. If workspace has source code files and _docs/ does not exist → existing-code flow (Pre-Step detection)
2. If _docs/_autopilot_state.md exists and step >= 2 (i.e. Document already ran) → existing-code flow
3. If _docs/_autopilot_state.md exists and step: done AND workspace contains source code → existing-code flow (completed project re-entry — loops to New Task)
4. Otherwise → greenfield flow

After selecting the flow, apply its detection rules (first match wins) to determine the current step.

Execution Loop

Every invocation follows this sequence:

1. Read _docs/_autopilot_state.md (if exists)
2. Read all File Index files above
3. Cross-check state file against _docs/ folder structure (rules in state.md)
4. Resolve flow (see Flow Resolution above)
5. Resolve current step (detection rules from the active flow file)
6. Present Status Summary (template in active flow file)
7. Execute:
   a. Delegate to current skill (see Skill Delegation below)
   b. If skill returns FAILED → apply Skill Failure Retry Protocol (see protocols.md):
      - Auto-retry the same skill (failure may be caused by missing user input or environment issue)
      - If 3 consecutive auto-retries fail → set status: failed, warn user, stop auto-retry
   c. When skill completes successfully → reset retry counter, update state file (rules in state.md)
   d. Re-detect next step from the active flow's detection rules
   e. If next skill is ready → auto-chain (go to 7a with next skill)
   f. If session boundary reached → update state, suggest new conversation (rules in state.md)
   g. If all steps done → update state → report completion

Skill Delegation

For each step, the delegation pattern is:

1. Update state file: set step to the autopilot step number, status to in_progress, set sub_step to the sub-skill's current internal step/phase, reset retry_count: 0
2. Announce: "Starting [Skill Name]..."
3. Read the skill file: .cursor/skills/[name]/SKILL.md
4. Execute the skill's workflow exactly as written, including all BLOCKING gates, self-verification checklists, save actions, and escalation rules. Update sub_step in state each time the sub-skill advances.
5. If the skill fails: follow the Skill Failure Retry Protocol in protocols.md — increment retry_count, auto-retry up to 3 times, then escalate.
6. When complete (success): reset retry_count: 0, update state file to the next step with status: not_started, return to auto-chain rules (from active flow file)

Do NOT modify, skip, or abbreviate any part of the sub-skill's workflow. The autopilot is a sequencer, not an optimizer.

State File Template

The state file (_docs/_autopilot_state.md) is a minimal pointer — only the current step. Full format rules are in state.md.

# Autopilot State

## Current Step
flow: [greenfield | existing-code]
step: [number or "done"]
name: [step name]
status: [not_started / in_progress / completed / skipped / failed]
sub_step: [0 or N — sub-skill phase name]
retry_count: [0-3]

Trigger Conditions

This skill activates when the user wants to:

• Start a new project from scratch
• Continue an in-progress project
• Check project status
• Let the AI guide them through the full workflow

Keywords: "autopilot", "auto", "start", "continue", "what's next", "where am I", "project status"

Differentiation:

• User wants only research → use /research directly
• User wants only planning → use /plan directly
• User wants to document an existing codebase → use /document directly
• User wants the full guided workflow → use /autopilot

Flow Reference

See flows/greenfield.md and flows/existing-code.md for step tables, detection rules, auto-chain rules, and status summary templates.