ai-build

name	ai-build
description	Canonical implementation gateway: reads approved plan.md, resolves stack from manifest, deterministic-routes each task to its adapter, dispatches the build agent in an isolated worktree, runs TDD self-validation per task, then a single final quality loop with one bounded quality-remediation pass on the full changeset before /ai-pr. Trigger for 'go', 'start building', 'execute the plan', 'implement it', 'lets do this', 'build the plan', 'resume', 'continue'. Not without an approved plan; run /ai-plan first. Not for multi-concern specs needing decomposition; use /ai-autopilot instead. Not for a single function or subcomponent; use /ai-code.
effort	cheap
argument-hint	[spec-NNN | --resume | --no-hitl]
model_tier	haiku
mirror_family	antigravity-skills
generated_by	ai-eng sync
canonical_source	.claude/skills/ai-build/SKILL.md
edit_policy	generated-do-not-edit

Build

Execution engine for approved plans: reads plan.md, dispatches one subagent per task (fresh context, TDD self-validation), then a single final quality loop with one bounded quality-remediation pass on the full changeset before /ai-pr. Requires an approved plan — run /ai-plan first; resume with /ai-build --resume. Route multi-concern plans (≥ 3 concerns or ≥ 10 files) to /ai-autopilot, not /ai-build. If stuck: STOP and re-plan.

Workflow

Principles: §10.5 TDD (per-task RED/GREEN/REFACTOR self-validation), §10.4 DRY (plan patch-hunks unlock cheap-tier execution downstream).

0. Preflight — verify .ai-engineering/specs/plan.md, .agents/skills/_shared/execution-kernel.md, handlers/quality.md, handlers/deliver.md exist. Any missing → STOP and report the exact path(s). Never improvise missing orchestration. 0b. Executor route gate (spec-145) — read plan frontmatter status + execution_route.executor. status ≠ approved → STOP for approval. executor: autopilot → refuse this run, print execution_route.safe_next_command (normally /ai-autopilot), STOP. Route metadata absent → legacy behavior.
1. Board sync (in_progress) — read .ai-engineering/specs/spec.md frontmatter refs; for each ref whose hierarchy rule ≠ never_close (user_stories/tasks/bugs/issues), /ai-board sync in_progress <ref> (fail-open). Then read <spec_id> from spec.md spec: and python .ai-engineering/scripts/spec_lifecycle.py start <spec_id> (APPROVED→IN_PROGRESS; mirrors status: in-progress; fail-open — non-zero exit does NOT block DAG construction).
2. Advise advisory — invoke the ai-advise agent in gate mode before dispatching any build task (fail-open: log + continue, never block dispatch). 2b. Stack routing (D-127-06 / sub-008, spec-128 D-128-01) — per task, call tools.skill_app.deterministic_router.resolve_adapter(task_path, spec_stack) for the overrides dir .ai-engineering/overrides/<stack>/; pass it to the build agent to load conventions.md, tdd_harness.md, security_floor.md, examples/ before writing code. spec_stack precedence: spec frontmatter stack: over file-extension inference. UnknownStackError halts the task ("no overrides for this stack"). 2c. Model dispatch (D-131-08 / sub-003, §10.4 DRY) — per task, inspect the plan.md block:
   • Patch (deterministic): present AND no synthesis hint → model_tier=haiku, effort=cheap (mechanical).
   • patch absent OR synthesis hint present → model_tier=sonnet, effort=mid (judgment).
   • operator --max-effort OR task tagged architecture: true → model_tier=opus, effort=high (deep-architecture override). Log via emit_agent_dispatched(..., metadata={"model_tier": <tier>, "effort": <effort>, "patch_present": <bool>}). Investing in /ai-plan (exhaustive patch-ready output) is what unlocks cheap-tier execution downstream (D-131-08). 2d. No-HITL contract (D-134-03) — if --no-hitl is set, read handlers/no-hitl.md and apply: single-concern gate, NEEDS_CONTEXT → BLOCKED promotion, quality_loop_blocked → exit 78, --no-watch implied for delivery, no auto-retry. Absent flag → default behavior unchanged.
3. Execute kernel — .agents/skills/_shared/execution-kernel.md. Wrap each task (Sub-flow 1 dispatch → 2 build self-validation TDD RED/GREEN/REFACTOR → 3 artifact collection → 4 board sync). As each task reaches a terminal state, update .ai-engineering/specs/plan.md immediately before dispatching the next task. Do not defer checkbox/status writes to the end of the phase or the end of the spec.
4. Quality check — read handlers/quality.md: Verify+Review on the full changeset, single round, fail-loud with ONE bounded quality-remediation pass. Blocker/critical/high findings may be fixed once when scoped to quality-loop evidence; remaining blocker/critical/high findings → STOP + escalate (no second remediation pass).
5. Deliver — read handlers/deliver.md: PR via /ai-pr with quality report. The deliver/quality handlers render the operator report through the value block (value-lens.md).

Resume Protocol

--resume uses observable evidence only — never guess hidden state. The build skill is the canonical implementation gateway (D-127-11).

1. Missing/placeholder plan: plan.md missing or still # No active plan → STOP, run /ai-plan.
2. Incomplete tasks: unchecked checkboxes → resume at the first incomplete phase; skip completed tasks.
3. Quality evidence missing: all tasks checked but no ## Quality Outcome section → resume at Quality Check (handlers/quality.md).
4. Quality evidence present: resume at Deliver; handlers/deliver.md detects an existing open PR and enters watch-and-fix or creates/updates the PR.
5. Conflicting evidence: choose the earliest safe step and log why. Safety over convenience.

Handler Dispatch Table

Phase	Handler	Agent Pattern
Quality Check	handlers/quality.md	Verify + Review parallel
Deliver	handlers/deliver.md	PR pipeline + cleanup
No-HITL	handlers/no-hitl.md	deterministic / single-concern only

Common Mistakes

• Giving subagents the whole codebase — scope tightly.
• Continuing past a BLOCKED task without user input.
• Batch-updating plan.md at the end instead of per task close.
• Modifying RED-phase test files during a GREEN-phase task.
• Invoking --no-hitl on a multi-concern plan — use /ai-autopilot.

Examples

User: "the plan is approved, go"

/ai-build

Reads plan.md, dispatches one agent per task with fresh context; each task self-validates via TDD; the final quality loop verifies the full changeset and may consume one bounded quality-remediation pass before handing off to /ai-pr.

Integration

Called by: user directly post-/ai-plan approval. Calls: ai-build agent, ai-verify, ai-review, /ai-pr, /ai-board sync. Reads: _shared/execution-kernel.md. Transitions to: PR merge, or back to /ai-plan. See also: /ai-autopilot (multi-concern + --backlog), /ai-resolve-conflicts.

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