ai-start

$npx mdskill add arcasilesgroup/ai-engineering/ai-start

Boots a coding session by loading project context and displaying a welcome dashboard

  • Starts a new coding session by fetching project data and presenting a dashboard.
  • Deploys a Python script to load git, sqlite, manifests, and board APIs for quick rendering.
  • Determines the user's needs based on triggers like 'hello', 'start', or 'status' without human-onboarding.
  • Delivers the welcome dashboard with recent activity, board items, and available commands directly to the user.

SKILL.md

.github/skills/ai-startView on GitHub ↗
---
name: ai-start
description: "Bootstraps a coding session: loads project context and displays a welcome dashboard with recent activity, board items, and available commands. Trigger for 'hello', 'lets start', 'good morning', 'whats the status', 'get me up to speed', 'I am back'. Also invokable mid-session to re-bootstrap. Not for human onboarding; use /ai-onboard instead. Not for governance review; use /ai-governance instead."
effort: mid
argument-hint: 
mode: agent
model_tier: sonnet
mirror_family: copilot-skills
generated_by: ai-eng sync
canonical_source: .claude/skills/ai-start/SKILL.md
edit_policy: generated-do-not-edit
---


# Start

## Purpose

Session welcome dashboard. The dashboard is fully rendered by a deterministic
Python script (`session_bootstrap.py`). The IDE agent does no per-field
derivation — it runs **exactly one command**, prints the markdown verbatim,
and stops.

Why this contract exists: re-probing git, sqlite, manifests, board APIs, etc.
from the IDE side blows the latency budget (operator-pain #18b). The script
collects every field, caches the board call (stale-while-revalidate), and
emits ready-to-display markdown. Cold path: < 3 s wall (with board). Warm
path: < 500 ms.

## Process

Run exactly this argv — literal, no flags moved or shells added — and print
its stdout verbatim:

```
uv run python .ai-engineering/scripts/session_bootstrap.py --format=markdown
```

That is the whole skill. The script is enrolled in the trusted-script lane
(`hooks-manifest.json` `trustedArgvs`, D-131-12) so this exact argv bypasses
RTK rewriting and IOC re-evaluation. Any other invocation form (positional
flag order changes, plain `python3`, missing `--format`) falls back to the
full IOC path and degrades latency.

### Hard rules

- Do **not** read the manifest, run `git`, query `sqlite`, hit `gh`, glob
  the skills/agents tree, or count `LESSONS.md` from the agent side. The
  script already did all of that and embedded the result inside the
  markdown payload.
- Do **not** rewrite the markdown the script emits. The format is the
  cross-IDE contract (Claude Code, Codex, Antigravity, Copilot all render
  the same bytes).
- Do **not** invoke `/ai-session-watch` from inside this skill. Observation is
  always-on via the `PreToolUse` + `PostToolUse` hooks (`instinct-
  observe.py`) and consolidated at session end by the `Stop` hook
  (`instinct-extract.py`). The dashboard surfaces an `N to review`
  CTA when the unconsolidated backlog exceeds the
  `observations/meta.json` `deltaThreshold` — operators run
  `/ai-session-watch --review` manually when they see that CTA.

### What the dashboard already contains

Trust the markdown the script emits. It surfaces every field the
operator typically asks for next:

- **Project identity**: the CONSTITUTION mission as the tagline.
- **Stack posture**: `surfaces.enabled` ·
  `gates.mode` — visible in one line so layer drift is obvious.
- **Counts**: skills, agents, lessons, active decisions, accepted
  risks, recent_events_7d.
- **Active work**: spec id + state + title, plan status (including the
  `shipped-pending-pr-merge` exemption per `plan-schema.md`),
  task progress.
- **Recent commits**: last 5 SHA + subject from `git log`.
- **Recent lessons**: last 3 `### ` headers from `LESSONS.md` with a
  gist line (no `**Context**:` prefix noise — stripped server-side).
- **Board**: full per-status breakdown via paginated GraphQL (no
  sample-size truncation).
- **Compatibility**: a `### ⚠ Compatibility` block appears only when
  the manifest deviates from defaults (today: `gates.mode != regulated`).

Do not duplicate or re-render any of these from the agent side.

### Board behaviour

The script handles the `gh project item-list` call with a hard 4 s
subprocess timeout and a stale-while-revalidate cache at
`.ai-engineering/runtime/board-cache.json` (fresh ≤ 60 s, stale-allowed up
to 5 min). On board failure the JSON includes `board_summary.unavailable:
true` and the markdown shows `board unavailable (reason)` — never blocks
the rest of the dashboard.

### When the script is unavailable

If the script exits non-zero or the venv has no `uv`, fall back to a
one-line banner: `ai-start unavailable — repo not bootstrapped, run \`ai-eng
install\`.` Do **not** reconstruct the dashboard by hand.

## Examples

### Example 1 — morning bootstrap

```
/ai-start
```

Runs the script, prints the dashboard, stops. The dashboard already lists
the active spec, last 5 commits, board items by status, project counts,
and the quick-action chips.

### Example 2 — mid-session re-bootstrap after `/clear`

```
/ai-start
```

Same single command. The board cache (if still fresh) makes this nearly
instantaneous.

## Integration

- **Called by**: user directly; IDE instruction files (FIRST ACTION mandate
  per CONSTITUTION).
- **Calls**: `session_bootstrap.py --format=markdown` (only).
- **Does not call**: `/ai-session-watch`, `/ai-board discover`, manifest readers,
  or any other skill. Suggestions (e.g. "no active spec — run
  `/ai-brainstorm`") are embedded inside the markdown the script emits.
- **See also**: `/ai-onboard` (human onboarding, different audience),
  `/ai-branch-cleanup` (pre-start hygiene).

More from arcasilesgroup/ai-engineering

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ai-animationDesigns motion, transitions, and micro-interactions for UI components: spring animations, gestures, easing, staggers — taste-driven detail compounding. Trigger for 'animate this', 'add transitions', 'micro-interactions for', 'gesture design', 'swipe to dismiss', 'easing for this', 'stagger the'. Not for design systems; use /ai-design instead. Not for visual art; use /ai-visual instead. Not for testing animation code; use /ai-test instead.
ai-autopilotDelivers large multi-concern specs and backlog runs autonomously: decomposes specs into sub-specs (or normalizes work items into a backlog DAG), deep-plans with parallel agents, builds a dependency DAG, implements in waves, runs a single final quality loop with one bounded quality-remediation pass (verify+guard+review on full changeset), delivers via PR. Trigger for 'implement spec-NNN end to end', 'autopilot this', 'autonomous delivery', 'decompose and ship', 'run the backlog', 'execute these GitHub issues', 'process the sprint backlog'. Invocation is the approval gate. Not for small or single-concern tasks; use /ai-build instead. Not for ambiguous requirements; use /ai-brainstorm first.
ai-boardOperates the project board (GitHub Projects v2 or Azure DevOps): discovers configuration after install (fields, state mappings, process templates) and syncs work-item state at lifecycle transitions. Trigger for 'set up the board', 'configure our ADO board', 'discover board fields', 'move this issue to in-review', 'update the board', 'mark as in progress', 'sync the work item state'. Two subcommands: `discover` (post-install configuration write) and `sync` (lifecycle state transitions). Auto-invoked via `sync` by /ai-brainstorm, /ai-build, and /ai-pr; fail-open. Not for backlog execution; use /ai-autopilot --backlog instead.
ai-brainstormForces rigorous design interrogation BEFORE any code: explores approaches, surfaces ambiguity, gathers evidence, produces an approved spec that becomes the contract for /ai-plan. Trigger for 'lets add X', 'how should we handle Y', 'whats the best approach', 'I am thinking about', 'what should we build for'. Not for existing approved specs; use /ai-plan instead. Not for execution; use /ai-build instead.
ai-branch-cleanupCleans branches safely: switches to the default branch, prunes merged and squash-merged branches, syncs to remote, sweeps stale specs, rotates `.ai-engineering/runtime/` per retention policy. Trigger for 'tidy up', 'tidy branches', 'sync to main', 'delete old branches', 'start fresh', 'rotate runtime'. Auto-invoked by /ai-pr after merge. Not for committing changes; use /ai-commit instead. Not for code-level dead-code removal; use /ai-simplify instead.
ai-buildCanonical 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.
ai-codeWrites production code that satisfies stack-context standards on the first pass: interface-first design, backward-compatibility checks, lightweight self-review. Trigger for 'implement this', 'write the code for', 'add X to Y', 'build this function', 'make this work'. Not for tests; use /ai-test instead. Not for debugging; use /ai-debug instead. Not for refactoring; use /ai-simplify instead. Not for executing an approved plan end-to-end; use /ai-build (the gateway).
ai-commitRuns the governed commit pipeline: auto-branches from protected, stages selectively, formats and lints, scans for secrets, gates docs, composes a conventional message, pushes. Trigger for 'commit my changes', 'save my work', 'push this to remote', 'stage these files', 'ship it'. Not for opening a PR; use /ai-pr instead. Not for branch hygiene; use /ai-branch-cleanup instead.