gh:work

Work Execution Command

Execute work efficiently while maintaining quality and finishing features.

Introduction

This command takes a work document (plan or specification) or a bare prompt describing the work, and executes it systematically. The focus is on shipping complete features by understanding requirements quickly, following existing patterns, and maintaining quality throughout.

Input Document

<input_document> #$ARGUMENTS </input_document>

Execution Workflow

Phase 0: Input Triage

Determine how to proceed based on what was provided in <input_document>.

Plan document (input is a file path to an existing plan or specification) → skip to Phase 1.

Bare prompt (input is a description of work, not a file path):

1. Scan the work area

   • Identify files likely to change based on the prompt
   • Find existing test files for those areas (search for test/spec files that import, reference, or share names with the implementation files)
   • Note local patterns and conventions in the affected areas

2. Assess complexity and route

   Complexity	Signals	Action
   Trivial	1-2 files, no behavioral change (typo, config, rename)	Proceed to Phase 1 step 2 (environment setup), then implement directly — no task list, no execution loop. Apply Test Discovery if the change touches behavior-bearing code
   Small / Medium	Clear scope, under ~10 files	Build a task list from discovery. Proceed to Phase 1 step 2
   Large	Cross-cutting, architectural decisions, 10+ files, touches auth/payments/migrations	Inform the user this would benefit from /gh:brainstorm or /gh:plan to surface edge cases and scope boundaries. Honor their choice. If proceeding, build a task list and continue to Phase 1 step 2

---

Phase 0.5: Task Lifecycle Start

Before Phase 1, log the skill start event so this execution appears on the task board:

gale-task log skill_started --skill gh:work --title "${ARGUMENTS:-work}" 2>/dev/null || true

If gale-task is not on PATH, skip silently — this must never block the skill.

Phase 0.6: HKTMemory Retrieve

Before Phase 1, query the vector memory database for related execution context:

1. Extract a search query from the work document or bare prompt:

   • Plan title or feature description
   • Key components, modules, or files involved
   • Problem being solved or functionality being implemented

2. Run (requires env vars HKT_MEMORY_API_KEY, HKT_MEMORY_BASE_URL, HKT_MEMORY_MODEL):

   hkt-memory retrieve \
     --query "<extracted query>" \
     --layer all --limit 10 --min-similarity 0.35 \
     --vector-weight 0.7 --bm25-weight 0.3
   

3. If results returned, prepare context for Phase 1 execution:

   ## Related context from HKTMemory
   Source: vector database. Treat as additional context, not primary evidence.
   [results here, each tagged with (similarity: X.XX)]
   

   Use this to:

   • Check for similar implementations or patterns already explored
   • Find related solutions that might inform the current work
   • Surface constraints or decisions from past work

4. If no results or command error, proceed silently.

0.6b HKTMemory Session Search

In addition to vector retrieval, query related historical work session records:

1. Build a search query from the current task title and skill name

2. Run (requires env vars HKT_MEMORY_API_KEY, HKT_MEMORY_BASE_URL, HKT_MEMORY_MODEL):

   hkt-memory session-search \
     --query "<skill name: gh:work — task title or feature description>" \
     --limit 5
   

3. If results returned, prepare a context block for later phases:

   ## Related Historical Work Sessions
   Source: session record search. Supplementary context only, not primary evidence.
   [results list]
   

4. If no results or command error, proceed silently without blocking Phase 1.

Phase 1: Quick Start

1. Read Plan and Clarify (skip if arriving from Phase 0 with a bare prompt)

   • Read the work document completely
   • Treat the plan as a decision artifact, not an execution script
   • If the plan includes sections such as Implementation Units, Work Breakdown, Requirements Trace, Files, Test Scenarios, or Verification, use those as the primary source material for execution
   • Check for Execution note on each implementation unit — these carry the plan's execution posture signal for that unit (for example, test-first or characterization-first). Note them when creating tasks.
   • Check for a Deferred to Implementation or Implementation-Time Unknowns section — these are questions the planner intentionally left for you to resolve during execution. Note them before starting so they inform your approach rather than surprising you mid-task
   • Check for a Scope Boundaries section — these are explicit non-goals. Refer back to them if implementation starts pulling you toward adjacent work
   • Review any references or links provided in the plan
   • If the user explicitly asks for TDD, test-first, or characterization-first execution in this session, honor that request even if the plan has no Execution note
   • If anything is unclear or ambiguous, ask clarifying questions now
   • Get user approval to proceed
   • Do not skip this - better to ask questions now than build the wrong thing
   • Do not edit the plan body during execution. The plan is a decision artifact; progress lives in git commits and the task tracker. The only plan mutation during gh:work is the final status: active -> status: completed flip at shipping (see references/shipping-workflow.md Phase 4 Step 2). Legacy plans may contain - [ ] / - [x] marks on unit headings — ignore them as state; per-unit completion is determined during execution by reading the current file state.

2. Setup Environment

   First, check the current branch:

   current_branch=$(git branch --show-current)
   default_branch=$(git symbolic-ref refs/remotes/origin/HEAD 2>/dev/null | sed 's@^refs/remotes/origin/@@')
   
   # Fallback if remote HEAD isn't set
   if [ -z "$default_branch" ]; then
     default_branch=$(git rev-parse --verify origin/main >/dev/null 2>&1 && echo "main" || echo "master")
   fi
   

   If already on a feature branch (not the default branch):

   First, check whether the branch name is meaningful — a name like feat/crowd-sniff or fix/email-validation tells future readers what the work is about. Auto-generated worktree names (e.g., worktree-jolly-beaming-raven) or other opaque names do not.

   If the branch name is meaningless or auto-generated, suggest renaming it before continuing:

   git branch -m <meaningful-name>
   

   Derive the new name from the plan title or work description (e.g., feat/crowd-sniff). Present the rename as a recommended option alongside continuing as-is.

   Then ask: "Continue working on [current_branch], or create a new branch?"

   • If continuing (with or without rename), proceed to step 3
   • If creating new, follow Option A or B below

   If on the default branch, choose how to proceed:

   Option A: Create a new branch

   git pull origin [default_branch]
   git checkout -b feature-branch-name
   

   Use a meaningful name based on the work (e.g., feat/user-authentication, fix/email-validation).

   Option B: Use a worktree (recommended for parallel development)

   skill: git-worktree
   # The skill will create a new branch from the default branch in an isolated worktree
   

   Option C: Continue on the default branch

   • Requires explicit user confirmation
   • Only proceed after user explicitly says "yes, commit to [default_branch]"
   • Never commit directly to the default branch without explicit permission

   Recommendation: Use worktree if:

   • You want to work on multiple features simultaneously
   • You want to keep the default branch clean while experimenting
   • You plan to switch between branches frequently

3. Create Task List (skip if Phase 0 already built one, or if Phase 0 routed as Trivial)

   • Use the platform's task tracking tool (TaskCreate/TaskUpdate/TaskList in Claude Code, update_plan in Codex, or the equivalent on other harnesses) to break the plan into actionable tasks
   • Derive tasks from the plan's implementation units, dependencies, files, test targets, and verification criteria
   • When the plan defines U-IDs for Implementation Units, preserve the unit's U-ID as a prefix in the task subject (e.g., "U3: Add parser coverage"). This keeps blocker references, deferred-work notes, and final summaries anchored to the same identifier the plan uses, so progress and traceability remain unambiguous across plan edits
   • Carry each unit's Execution note into the task when present
   • For each unit, read the Patterns to follow field before implementing — these point to specific files or conventions to mirror
   • Use each unit's Verification field as the primary "done" signal for that task
   • Do not expect the plan to contain implementation code, micro-step TDD instructions, or exact shell commands
   • Include dependencies between tasks
   • Prioritize based on what needs to be done first
   • Include testing and quality check tasks
   • Keep tasks specific and completable

4. Choose Execution Strategy

   After creating the task list, decide how to execute based on the plan's size and dependency structure:

   Strategy	When to use
   Inline	1-2 small tasks, or tasks needing user interaction mid-flight. Default for bare-prompt work — bare prompts rarely produce enough structured context to justify subagent dispatch
   Serial subagents	3+ tasks with dependencies between them. Each subagent gets a fresh context window focused on one unit — prevents context degradation across many tasks. Requires plan-unit metadata (Goal, Files, Approach, Test scenarios)
   Parallel subagents	3+ tasks that pass the Parallel Safety Check (below). Dispatch independent units simultaneously, run dependent units after their prerequisites complete. Requires plan-unit metadata

   Parallel Safety Check — required before choosing parallel dispatch:

   1. Build a file-to-unit mapping from every candidate unit's Files: section (Create, Modify, and Test paths)
   2. Check for intersection — any file path appearing in 2+ units means overlap
   3. If any overlap is found, downgrade to serial subagents. Log the reason (e.g., "Units 2 and 4 share config/routes.rb — using serial dispatch"). Serial subagents still provide context-window isolation without shared-directory risks

   Even with no file overlap, parallel subagents sharing a working directory face git index contention (concurrent staging/committing corrupts the index) and test interference (concurrent test runs pick up each other's in-progress changes). The parallel subagent constraints below mitigate these.

   Subagent dispatch uses your available subagent or task spawning mechanism. For each unit, give the subagent:

   • The full plan file path (for overall context)
   • The specific unit's Goal, Files, Approach, Execution note, Patterns, Test scenarios, and Verification
   • Any resolved deferred questions relevant to that unit
   • Instruction to check whether the unit's test scenarios cover all applicable categories (happy paths, edge cases, error paths, integration) and supplement gaps before writing tests

   Parallel subagent constraints — when dispatching units in parallel (not serial or inline):

   • Instruct each subagent: "Do not stage files (git add), create commits, or run the project test suite. The orchestrator handles testing, staging, and committing after all parallel units complete."
   • These constraints prevent git index contention and test interference between concurrent subagents

   Permission mode: Omit the mode parameter when dispatching subagents so the user's configured permission settings apply. Do not pass mode: "auto" — it overrides user-level settings like bypassPermissions.

   After each subagent completes (serial mode):

   1. Review the subagent's diff — verify changes match the unit's scope and Files: list
   2. Run the relevant test suite to confirm the tree is healthy
   3. If tests fail, diagnose and fix before proceeding — do not dispatch dependent units on a broken tree
   4. Update the task list (do not edit the plan body — progress is carried by the commit)
   5. Dispatch the next unit

   After all parallel subagents in a batch complete:

   1. Wait for every subagent in the current parallel batch to finish before acting on any of their results
   2. Cross-check for discovered file collisions: compare the actual files modified by all subagents in the batch (not just their declared Files: lists). Subagents may create or modify files not anticipated during planning — this is expected, since plans describe what not how. A collision only matters when 2+ subagents in the same batch modified the same file. In a shared working directory, only the last writer's version survives — the other unit's changes to that file are lost. If a collision is detected: commit all non-colliding files from all units first, then re-run the affected units serially for the shared file so each builds on the other's committed work
   3. For each completed unit, in dependency order: review the diff, run the relevant test suite, stage only that unit's files, and commit with a conventional message derived from the unit's Goal
   4. If tests fail after committing a unit's changes, diagnose and fix before committing the next unit
   5. Update the task list (do not edit the plan body — progress is carried by the commits just made)
   6. Dispatch the next batch of independent units, or the next dependent unit

Phase 2: Execute

1. Task Execution Loop

   For each task in priority order:

   while (tasks remain):
     - Mark task as in-progress
     - Read any referenced files from the plan or discovered during Phase 0
     - **If the unit's work is already present and matches the plan's intent** (files exist with the expected capability, or the unit's `Verification` criteria are already satisfied by the current code), the work has likely shipped on a prior branch or session. Verify it matches, mark the task complete, and move on. Do not silently reimplement.
     - Look for similar patterns in codebase
     - Find existing test files for implementation files being changed (Test Discovery — see below)
     - Implement following existing conventions
     - Add, update, or remove tests to match implementation changes (see Test Discovery below)
     - Run System-Wide Test Check (see below)
     - Run tests after changes
     - Assess testing coverage: did this task change behavior? If yes, were tests written or updated? If no tests were added, is the justification deliberate (e.g., pure config, no behavioral change)?
     - Mark task as completed
     - Evaluate for incremental commit (see below)
   

   When a unit carries an Execution note, honor it. For test-first units, write the failing test before implementation for that unit. For characterization-first units, capture existing behavior before changing it. For units without an Execution note, proceed pragmatically.

   Guardrails for execution posture:

   • Do not write the test and implementation in the same step when working test-first
   • Do not skip verifying that a new test fails before implementing the fix or feature
   • Do not over-implement beyond the current behavior slice when working test-first
   • Skip test-first discipline for trivial renames, pure configuration, and pure styling work

   Test Discovery — Before implementing changes to a file, find its existing test files (search for test/spec files that import, reference, or share naming patterns with the implementation file). When a plan specifies test scenarios or test files, start there, then check for additional test coverage the plan may not have enumerated. Changes to implementation files should be accompanied by corresponding test updates — new tests for new behavior, modified tests for changed behavior, removed or updated tests for deleted behavior.

   Test Scenario Completeness — Before writing tests for a feature-bearing unit, check whether the plan's Test scenarios cover all categories that apply to this unit. If a category is missing or scenarios are vague (e.g., "validates correctly" without naming inputs and expected outcomes), supplement from the unit's own context before writing tests:

   Category	When it applies	How to derive if missing
   Happy path	Always for feature-bearing units	Read the unit's Goal and Approach for core input/output pairs
   Edge cases	When the unit has meaningful boundaries (inputs, state, concurrency)	Identify boundary values, empty/nil inputs, and concurrent access patterns
   Error/failure paths	When the unit has failure modes (validation, external calls, permissions)	Enumerate invalid inputs the unit should reject, permission/auth denials it should enforce, and downstream failures it should handle
   Integration	When the unit crosses layers (callbacks, middleware, multi-service)	Identify the cross-layer chain and write a scenario that exercises it without mocks

   System-Wide Test Check — Before marking a task done, pause and ask:

   Question	What to do
   What fires when this runs? Callbacks, middleware, observers, event handlers — trace two levels out from your change.	Read the actual code (not docs) for callbacks on models you touch, middleware in the request chain, after_* hooks.
   Do my tests exercise the real chain? If every dependency is mocked, the test proves your logic works in isolation — it says nothing about the interaction.	Write at least one integration test that uses real objects through the full callback/middleware chain. No mocks for the layers that interact.
   Can failure leave orphaned state? If your code persists state (DB row, cache, file) before calling an external service, what happens when the service fails? Does retry create duplicates?	Trace the failure path with real objects. If state is created before the risky call, test that failure cleans up or that retry is idempotent.
   What other interfaces expose this? Mixins, DSLs, alternative entry points (Agent vs Chat vs ChatMethods).	Grep for the method/behavior in related classes. If parity is needed, add it now — not as a follow-up.
   Do error strategies align across layers? Retry middleware + application fallback + framework error handling — do they conflict or create double execution?	List the specific error classes at each layer. Verify your rescue list matches what the lower layer actually raises.

   When to skip: Leaf-node changes with no callbacks, no state persistence, no parallel interfaces. If the change is purely additive (new helper method, new view partial), the check takes 10 seconds and the answer is "nothing fires, skip."

   When this matters most: Any change that touches models with callbacks, error handling with fallback/retry, or functionality exposed through multiple interfaces.

2. Incremental Commits

   After completing each task, evaluate whether to create an incremental commit:

   Commit when...	Don't commit when...
   Logical unit complete (model, service, component)	Small part of a larger unit
   Tests pass + meaningful progress	Tests failing
   About to switch contexts (backend → frontend)	Purely scaffolding with no behavior
   About to attempt risky/uncertain changes	Would need a "WIP" commit message

   Heuristic: "Can I write a commit message that describes a complete, valuable change? If yes, commit. If the message would be 'WIP' or 'partial X', wait."

   If the plan has Implementation Units, use them as a starting guide for commit boundaries — but adapt based on what you find during implementation. A unit might need multiple commits if it's larger than expected, or small related units might land together. Use each unit's Goal to inform the commit message.

   Commit workflow:

   # 1. Verify tests pass (use project's test command)
   # Examples: bin/rails test, npm test, pytest, go test, etc.
   
   # 2. Stage only files related to this logical unit (not `git add .`)
   git add <files related to this logical unit>
   
   # 3. Commit with conventional message
   git commit -m "feat(scope): description of this unit"
   

   Handling merge conflicts: If conflicts arise during rebasing or merging, resolve them immediately. Incremental commits make conflict resolution easier since each commit is small and focused.

   Note: Incremental commits use clean conventional messages without attribution footers. The final Phase 4 commit/PR includes the full attribution.

   Parallel subagent mode: When units run as parallel subagents, the subagents do not commit — the orchestrator handles staging and committing after the entire parallel batch completes (see Parallel subagent constraints in Phase 1 Step 4). The commit guidance in this section applies to inline and serial execution, and to the orchestrator's commit decisions after parallel batch completion.

3. Follow Existing Patterns

   • The plan should reference similar code - read those files first
   • Match naming conventions exactly
   • Reuse existing components where possible
   • Follow project coding standards (see AGENTS.md; use CLAUDE.md only if the repo still keeps a compatibility shim)
   • When in doubt, grep for similar implementations

4. Test Continuously

   • Run relevant tests after each significant change
   • Don't wait until the end to test
   • Fix failures immediately
   • Add new tests for new behavior, update tests for changed behavior, remove tests for deleted behavior
   • Unit tests with mocks prove logic in isolation. Integration tests with real objects prove the layers work together. If your change touches callbacks, middleware, or error handling — you need both.

5. Simplify as You Go

   After completing a cluster of related implementation units (or every 2-3 units), review recently changed files for simplification opportunities — consolidate duplicated patterns, extract shared helpers, and improve code reuse and efficiency. This is especially valuable when using subagents, since each agent works with isolated context and can't see patterns emerging across units.

   Don't simplify after every single unit — early patterns may look duplicated but diverge intentionally in later units. Wait for a natural phase boundary or when you notice accumulated complexity.

   If a /simplify skill or equivalent is available, use it. Otherwise, review the changed files yourself for reuse and consolidation opportunities.

6. Figma Design Sync (if applicable)

   For UI work with Figma designs:

   • Implement components following design specs
   • Use figma-design-sync agent iteratively to compare
   • Fix visual differences identified
   • Repeat until implementation matches design

7. Track Progress

   • Keep the task list updated as you complete tasks
   • Note any blockers or unexpected discoveries
   • Create new tasks if scope expands
   • Keep user informed of major milestones
   • When the plan defines U-IDs for Implementation Units, or the plan or origin document carries stable R-IDs (and optionally A/F/AE IDs), reference them in blockers, deferred-work notes, task summaries, and final verification — not routine status updates. U-IDs anchor units across plan edits; R/A/F/AE anchor product intent across the brainstorm-plan handoff. Use the IDs the plan supplies and do not invent ones it does not. This preserves traceability without burying signal under noise.

Phase 3-4: Quality Check and Ship It

When all Phase 2 tasks are complete and execution transitions to quality check, read references/shipping-workflow.md for the full shipping workflow: quality checks, code review, final validation, PR creation, and notification.

Phase 4.5: HKTMemory Store

After the work is complete and the shipping workflow has finished (PR created or changes committed):

1. Summarize what was accomplished:

   • Work completed (implementation summary)
   • Files changed
   • Key decisions or discoveries made during execution
   • Any deviations from the original plan and why

2. Run:

   hkt-memory store \
     --content "<execution summary with context>" \
     --title "Work: [plan title or feature description]" \
     --topic "work-execution" \
     --layer all
   

3. Log on success: Stored execution record to HKTMemory

4. On error, proceed silently — execution storage is supplementary

Note: This creates a searchable record of completed work for future reference when similar tasks arise.

Key Principles

Start Fast, Execute Faster

• Get clarification once at the start, then execute
• Don't wait for perfect understanding - ask questions and move
• The goal is to finish the feature, not create perfect process

The Plan is Your Guide

• Work documents should reference similar code and patterns
• Load those references and follow them
• Don't reinvent - match what exists

Test As You Go

• Run tests after each change, not at the end
• Fix failures immediately
• Continuous testing prevents big surprises

Quality is Built In

• Follow existing patterns
• Write tests for new code
• Run linting before pushing
• Review every change — inline for simple additive work, full review for everything else

Ship Complete Features

• Mark all tasks completed before moving on
• Don't leave features 80% done
• A finished feature that ships beats a perfect feature that doesn't

Common Pitfalls to Avoid

• Analysis paralysis - Don't overthink, read the plan and execute
• Skipping clarifying questions - Ask now, not after building wrong thing
• Ignoring plan references - The plan has links for a reason
• Testing at the end - Test continuously or suffer later
• Forgetting to track progress - Update task status as you go or lose track of what's done
• 80% done syndrome - Finish the feature, don't move on early
• Skipping review - Every change gets reviewed; only the depth varies

After the work workflow is fully complete, log the completion event:

1. Run gale-task log skill_completed to record the completion event.
2. If gale-task is not on PATH or the command fails, skip and continue — this must never block the skill.