Dev Workflow

Dev Workflow

An 8-step daily developer workflow that forces multiple passes over the same change from different angles: planning quality, implementor self-checking, repository standards, CI analysis, end-to-end verification, independent review, documentation, and a final commit-and-PR handoff.

The package and skill are named dev-workflow; use /skill:dev-workflow to load the full workflow context.

Workflow Overview

1. Plan Review      → /workflow:plan       → Challenge the plan before code gains momentum
2. Park Reviewer    → [manual]           → Save implementation details, hold reviewer in reserve
3. Self-Review      → /workflow:self       → Implementor rereads own code with fresh eyes
4. Standards Pass   → /workflow:standards  → Lint, types, conventions, pre-commit hygiene
5. CI Check         → /workflow:ci         → Fetch CI status, analyze failures, distinguish ours vs flakes
6. Verify & Loop    → [manual]           → Verify locally, wake reviewer, iterate 2-6 if needed
7. Documentation    → /workflow:docs       → Explain the why and how for future readers
8. Ship             → /workflow:ship       → Verify CI green, discover PR context, atomic commit, open PR

Step Details

Step 1: Review the Plan

Before writing code, challenge the plan itself. Have the AI reread the plan and surrounding code with fresh eyes, looking for bugs, ambiguity, and conflicts. Update the plan based on findings.

Command: /workflow:plan (append extra context like /workflow:plan focus on auth module)

Step 2: Park the Reviewer

When AI finishes implementation and outputs "details of what it did":

1. Copy those details into a reviewer session (or the original planner session)
2. Do not run the reviewer yet — the implementor needs to self-review first

This step is manual orchestration. No command for it.

Step 3: Implementor Self-Review

Force the implementor (same AI session that wrote the code) to reread all new and modified code with fresh eyes. Catch obvious bugs before the independent reviewer spends time on them.

Command: /workflow:self

Step 4: Standards Pass

Run the coding standards and pre-commit cleanup. This is the policy and hygiene pass:

• No local imports (check circular imports)
• No unnecessary getattr(), use hasattr() only if needed
• No overly large try/except blocks
• Structured logging in optimo_ apps
• No hardcoded strings/numbers where structured fields should be used
• Use TypedDict instead of loose dict with Any
• Ruff must be happy with all files
• No string-based type hints
• Never use typing.cast() — it's a code smell
• Don't repeat fixtures in tests
• Use Django ORM reverse relations to avoid unnecessary model imports
• Be pedantic about type hints, avoid Any
• Use ast-grep where helpful

Command: /workflow:standards

Step 5: CI Check

Before manual verification, check CI for the current branch using the separate ci-status pi package when installed. If it is unavailable, use get_ci_status and ci_fetch_job_logs only if the current harness exposes those tools; otherwise ask the user to install ci-status before proceeding.

Primary commands:

• /ci — quick status overview in the widget area
• /ci-detail — interactive TUI view grouped by CI provider and workflow/cycle, Tab and cycle switching, native pickers, in-place refresh, automatic failure focus, detail view, and log access
• /ci-logs <job> — pull failure logs for a specific job

Orchestration command:

• /workflow:ci — guides the AI through the full CI check: run /ci or /ci-detail, analyze each failure (ours vs flake), propose fixes, summarize.

Boundary: /workflow:ci is for remote CI status. If local-ci is on PATH and repo root contains .local-ci.toml, use local-ci later as the repo-owned local validation path; do not replace /ci with local-ci here.

The ci-status extension auto-watches CI on startup and after git pushes. Failure notifications appear automatically. Covers GitHub Actions and CircleCI (set CIRCLECI_TOKEN for CircleCI enrichment).

Command: /workflow:ci (orchestrated) or /ci-detail (direct interactive UI)

Step 6: Verify Locally & Run Reviewer

The engineer verifies everything locally (backend, frontend, etc.). Then wakes the waiting reviewer session. If the reviewer finds issues, paste findings back into the implementor and repeat steps 2-5 until satisfied.

This step is manual orchestration. No command for it.

Step 7: Documentation Pass

Make the outcome legible. Document all updated code, especially new additions, in simple, visual, first-principles-driven language. Explain why changes were made.

Command: /workflow:docs

Step 8: Ship It

Finalize and ship the work:

1. Verify remote CI green — prefer /ci and /ci-detail; fall back to available CI tools if the harness exposes them
2. Run repo-owned local validation — if local-ci is on PATH and repo root contains .local-ci.toml, run local-ci run --no-github (or the repo-local equivalent) before finalizing. If it fails, stop and dig into the failure instead of treating it as a status footnote.
3. Discover context — check the current branch, look for existing GitHub PRs and issues
4. Update existing PR if one already exists for this branch
5. Create new PR if none exists, linking any related GitHub issues
6. Ask questions if uncertain about anything (existing PRs, issue linking, branch targets)
7. Atomic commit — use the atomic commit skill, ensure everything passes (lint, types, tests, pre-commit)
8. PR description — use the PR description writer skill for a reviewer-friendly summary
9. Open the PR on GitHub

For backend release/master deploy flows, a PR-head local-ci run is only a preflight; exact target-branch parity still happens from the clean origin/release or origin/master head. If scripts/deploy/trigger_validated_backend_deploy.sh exists, use it there instead of assuming merge deploys automatically.

Never compromise by excluding files that are part of the change. Everything touched must be improved.

Command: /workflow:ship

Principles

1. Challenge the plan before code gains momentum — weak plans create expensive code
2. Don't spend reviewer attention too early — implementor must self-review first
3. Keep policy checks separate from correctness checks — standards vs. behavior
4. Use repetition on purpose — the loop (steps 2-5) turns review findings into concrete fixes
5. Make future maintenance cheaper — document at the end when the change is final
6. End with a review-ready artifact — atomic commit + clear PR description

Placeholders (repo-specific)

Subagent-Enhanced Workflow (with pi-subagents)

When pi-subagents is installed, the workflow gains dedicated child agents with forked context:

                    ┌──────────────────────────────┐
                    │  pi-subagents builtin agents  │
                    │  scout · oracle · reviewer    │
                    │  planner · worker · delegate  │
                    └──────────────────────────────┘

1. /workflow:scout     → scout agent     → Codebase recon before planning
2. /workflow:plan      → [inline]        → Challenge the plan with fresh eyes
3. /workflow:self      → [inline]        → Implementor self-review
4. /workflow:standards → [inline]        → Coding standards pass
5. /workflow:oracle    → oracle agent    → Second opinion, challenge assumptions
6. /workflow:reviewer  → reviewer agent  → Independent review with forked context
7. /workflow:parallel  → 3× reviewer     → Parallel reviews (correctness, tests, complexity)
8. /workflow:docs      → [inline]        → Documentation pass
9. /workflow:ship      → [inline]        → Smart ship (discover PR context, atomic commit, PR)

Subagent Commands

When Pi is running inside cmux, subagent-style workflow commands default to opening a seeded split pane when they are run directly from an idle parent session, so the review / recon lane gets its own adjacent surface without the engineer having to remember a separate cmux command.

Mental model:

/workflow:reviewer
  ├─ inside cmux + idle parent session -> open seeded split -> run reviewer prompt there
  └─ otherwise                         -> run inline in current session

Queued / follow-up flows keep their normal current-session behavior instead of unexpectedly opening a split later.

The seeded child session carries a small handoff: same cwd, current branch, short git status, a recent parent-session conversation snapshot, and any extra command text.

Inside that child session, the prompt still guides the AI to use the subagent tool when available. If pi-subagents is not installed, it falls back gracefully to inline analysis / review logic in that lane.

Reusable Chains

The project includes a pre-built review pipeline chain:

/run-chain workflow-pipeline -- <task description>

This runs: scout (recon) → reviewer (self-review) → worker (standards) → reviewer (docs) → delegate (ship)

Each step forks context from the parent session. The package source keeps the chain at agents/workflow-pipeline.chain.md; after local install, customize the active copy at .pi/agents/workflow-pipeline.chain.md if needed.

Session Bootstrap & Handoff

Loading Context from Existing PRs

When starting a new pi session and you need to continue work on an existing PR:

/workflow:context

This tells the AI to:

1. Discover PRs on the current branch (gh pr list --head)
2. If none locally, list your open PRs and let you pick
3. Check out remote PRs if needed (gh pr checkout <number>)
4. Deep-read the full diff across all modified files
5. Check CI status, review comments, and unresolved threads
6. Note any submodule pointer changes (important in this monorepo)
7. Ask clarifying questions if unsure which PR to focus on
8. Present a summary and be ready for further instructions

Generating Handoff Messages

When you need to hand work to another engineer or spawn a fresh subagent session:

/workflow:handoff

The AI generates a comprehensive handoff including:

• Task overview, current state, remaining work
• Key files, setup commands, test/verify commands
• Decisions made, risks, success criteria

You can refine it with back-and-forth until satisfied. Paste the result into a new pi session or share with an engineer.

Onboarding Engineers

Generate a message explaining the multi-agent parallel workflow:

/workflow:onboard

Produces a message describing how engineers ("agents") use AI in parallel, readonly-only, with handoff patterns. Ready to share in Slack, email, or docs.

Crafting Sandboxes

Use /workflow:crafting <task> to create, inspect, or update a Crafting instance. It delegates to the marketplace crafting-sandboxes skill (load it separately or through skills-bridge), discovers live templates, verifies checkout SHAs and frontend DS consumption, and separates endpoint readiness from authenticated UI proof. Snapshot restores require explicit approval for the exact source, target, and data replacement. Missing skill/CLI setup is a blocker, not permission to improvise operations.

Usage

# Core workflow (always available)
/workflow:plan              # Step 1 — Review the plan
/workflow:self              # Step 3 — Self-review new/modified code
/workflow:standards         # Step 4 — Coding standards pass
/workflow:ci                # Step 5 — CI check and failure analysis
/workflow:docs              # Step 7 — Documentation pass
/workflow:ship              # Step 8 — Smart ship
/workflow:crafting          # Crafting sandbox creation, inspection, or update

# Session bootstrap & handoff
/workflow:context           # Load context from existing PRs (local or remote)
/workflow:handoff           # Generate handoff for new engineer/subagent
/workflow:onboard           # Generate onboarding message for engineers

# Subagent-enhanced (requires pi-subagents)
/workflow:scout             # Scout codebase before planning
/workflow:oracle            # Second opinion, challenge assumptions
/workflow:reviewer          # Independent review, forked context
/workflow:parallel          # 3 parallel reviewers

# With extra context appended
/workflow:standards also check for missing DB indexes
/workflow:ship target is the staging branch

# See the full flow
/workflow:flow

# Interactive help panel (browse, learn, inject)
/workflow:help

# Run the full pipeline chain (requires pi-subagents)
/run-chain workflow-pipeline -- <task>

# Load this skill for AI context
/skill:dev-workflow