AI Assist Slice

Purpose

This page captures the first practical AI integration slice for gig.

The goal is not to turn gig into a generic chat CLI. The goal is to let gig keep owning deterministic release evidence while an AI layer turns that evidence into a more executive, more readable briefing.

Core Rule

gig remains the source of truth.

That means:

  • gig computes ticket scope, branch comparisons, risk signals, dependency status, and verification verdicts
  • AI consumes the resulting bundle
  • AI explains and prioritizes
  • AI does not invent missing branch or commit facts

Why DeerFlow Fits

DeerFlow already has the pieces that are useful on top of gig:

  • a lead agent with tool orchestration
  • optional sub-agents for deeper follow-up work
  • memory and skill support for reusable workflows
  • a simple HTTP surface for thread creation and streaming responses

That makes it a good sidecar for:

  • executive summaries
  • release-manager briefings
  • evidence-gap callouts
  • recommended next commands
  • future multi-source evidence synthesis across SCM, CI, deployments, and issue trackers

Boundary

The integration boundary should stay clean:

  • internal/inspect, internal/plan, and internal/manifest keep their current deterministic responsibilities
  • internal/assistant builds an audit bundle from those services
  • the DeerFlow client receives that bundle and asks for a briefing
  • CLI output renders the final AI summary, but the structured gig result still exists underneath

This keeps the CLI thin and avoids pushing model-specific logic into the release engine.

Current Slices

The current shipped commands are:

gig assist doctor
gig assist setup
gig assist audit --ticket ABC-123 --repo github:owner/name --audience qa
gig ask "what is still blocked?"
gig assist release --release rel-2026-04-09 --path . --audience release-manager
gig assist release --release rel-2026-04-09 --ticket-file tickets.txt --repo github:owner/name --audience release-manager
gig assist resume
gig assist resolve --path . --ticket ABC-123 --audience release-manager

gig assist doctor:

  1. finds the bundled deer-flow/ workspace inside the gig repo
  2. checks whether the local sidecar is configured, startable, and reachable
  3. reports the next onboarding step without writing files

gig assist setup:

  1. finds the bundled deer-flow/ workspace inside the gig repo
  2. creates config.yaml and any available DeerFlow env templates when needed
  3. tells the user the recommended next start command for the local sidecar

gig assist audit:

  1. resolves repo scope and branch context using normal gig rules
  2. builds a deterministic bundle from inspection, planning, verification, and packet highlights
  3. sends that bundle to DeerFlow together with a read-only follow-up bridge for fresh gig inspect, gig verify, and gig packet evidence on the same saved session
  4. prints a concise AI briefing tuned for qa, client, or release-manager

gig ask:

  1. loads the current saved AI session from the last audit, release, or resolve flow
  2. rebuilds the deterministic bundle from the current repo or workarea state
  3. sends the refreshed bundle plus the follow-up question to DeerFlow on the same thread
  4. lets DeerFlow request fresh read-only gig evidence through the saved current-session bridge instead of inventing missing facts
  5. prints a direct answer plus recommended next gig commands

gig assist release:

  1. loads saved ticket snapshots from one named release, or captures a live ticket set from local or remote repositories
  2. builds a deterministic release bundle with release-plan rollups, packet data, and aggregated provider-backed PR, linked issue/work item, deployment, release, check, overlap, delta, and hotspot evidence
  3. sends that bundle to DeerFlow
  4. prints a concise AI release briefing for the selected audience together with deterministic executive and operator summaries from the bundle

gig assist resolve:

  1. loads the deterministic gig resolve status plus the first active supported conflict block
  2. builds a conflict bundle with provenance, risk, scope warnings, and supported resolver actions
  3. sends that bundle to DeerFlow
  4. prints a concise AI conflict briefing for the selected audience

These commands are intentionally experimental. It is an additive explanation layer, not a required step in the main product flow.

What This Slice Solves

This slice helps with the practical pain points that deterministic JSON alone does not solve well:

  • stakeholders want a short summary, not raw repo data
  • release managers want obvious next commands
  • teams want risk phrased in business-readable language
  • people want the same facts explained differently for QA, client review, and release handoff
  • release owners want one release-wide summary across many ticket snapshots or live ticket sets instead of reading each ticket separately
  • developers want help understanding which conflict choice is safest without replacing the deterministic resolver
  • follow-up AI questions need a way to pull fresh deterministic evidence without silently drifting into prompt-only guesses

What This Slice Does Not Do

It does not:

  • replace verify
  • replace packet
  • guess release state without gig evidence
  • require DeerFlow for the main remote audit workflow
  • change gig into a write-enabled automation tool
  • replace gig resolve start as the actual place where conflict choices are applied

Skill Support

The repo now includes project-specific skill docs so DeerFlow or other agents can stay aligned with gig:

  • Agent Skills
  • deer-flow/skills/custom/gig-release-audit/SKILL.md
  • deer-flow/skills/custom/gig-resolve-conflict/SKILL.md
  • deer-flow/skills/custom/gig-product-guardrails/SKILL.md

These skills reinforce the main boundary:

  • use gig as the evidence engine
  • use AI to explain and prioritize
  • do not let prompts replace branch, ticket, or risk reasoning
  • support both saved-snapshot and live remote release bundle workflows
  • keep the DeerFlow bridge read-only and scoped to the current saved gig session

Near-Term Direction

After the current slices, the next high-value steps are:

  • finish parity for the remaining Bitbucket gap after shipping deeper GitLab and Azure DevOps checks plus linked work item evidence
  • make saved AI sessions work even more naturally through workarea-aware resume and repo-scoped memory
  • expand the read-only follow-up bridge beyond the current GitHub and current-session slice, then graduate it into a fuller gig tool or MCP surface
  • add richer prompt templates inside the project skill pack without moving logic into prompts

Product Positioning

The product story becomes stronger with this framing:

gig is a deterministic release-audit engine with an optional AI briefing layer.

That is more professional than a generic AI assistant and more useful than a plain branch-comparison tool.