implement-delta

name	implement-delta
description	Implement a delta following its plan
argument-hint	[DELTA-ID]

Implementation Workflow

Implement a delta following its plan.

Input

Delta ID: $ARGUMENTS (e.g., "DLT-001")

Context

You must load the following skills and read the following files before proceeding.

Skills

• katachi:framework-core - Workflow principles
• katachi:working-on-delta - Per-feature workflow
• katachi:research-docs - Documentation research for correct API usage

Delta inventory

• docs/planning/DELTAS.md - Delta definitions

Delta documents

• docs/delta-specs/$ARGUMENTS.md - What to build (requirements)
• docs/delta-designs/$ARGUMENTS.md - Why/how (design rationale)
• docs/delta-plans/$ARGUMENTS.md - Implementation plan with batches

Project decisions

• docs/architecture/README.md - Architecture decisions (ADRs)
• docs/design/README.md - Design patterns (DES)

Feature documentation (for acceptance criteria and architecture)

• Read affected feature specs from delta-spec (use as acceptance criteria source)
• Read affected feature designs from delta-design (use as architecture guidance)
• Feature specs define what behavior to implement
• Feature designs define architectural patterns to follow

Pre-Check

Verify all documentation exists:

• If spec/design/plan missing, suggest running appropriate commands first
• Plan is the primary guide for implementation

Update status:

python ${CLAUDE_PLUGIN_ROOT}/scripts/deltas.py status set $ARGUMENTS "⧗ Implementation"

Working with Batches

The implementation plan is organized into batches — scoped groups of steps with their own context and dependencies. Before implementing, understand how to work with them:

Context & Research entries

Each batch lists the documents, code files, and research pointers it needs. These are a starting point, not a contract:

• Read the listed entries before starting the batch
• Adapt as you go: remove entries that turn out to be irrelevant, add files you discover during implementation, update references if earlier batches produced different output than planned
• Follow research pointers: when a context entry points to library docs or an existing pattern, look it up. For any library or framework referenced in the batch, follow the katachi:research-docs skill guidance to verify current API signatures before writing code — do not rely on training data for library APIs
• Write findings back: when research reveals important information (API behavior, library patterns, design constraints), write those findings to the design doc (docs/delta-designs/$ARGUMENTS.md) for later reconciliation. This is critical — implementation knowledge must flow back to documentation

Batch status tracking

Update each batch's status marker in the plan file (docs/delta-plans/$ARGUMENTS.md) as you progress:

• ⧗ Pending — not yet started
• ⧗ Implementing — currently in progress
• ✓ Done — all steps completed and verified

Living documents

If a different approach is needed for valid reasons during implementation:

• Update the relevant document (spec/design/plan) immediately
• Then proceed with the new approach
• This applies to batch context entries too — update them to reflect reality

Execution Mode

Read the implementation plan and analyze batch dependencies (Depends on: fields).

• If all batches are sequential (each depends on the previous) → execute batches one at a time as the single agent
• If independent batches exist (batches with no cross-dependencies) → spawn parallel agents, one per independent batch

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Sequential Execution

Used when batches form a dependency chain. The lead agent implements everything directly.

1. Process Each Batch

For each batch in dependency order:

1. Mark batch as ⧗ Implementing in the plan file
2. Load context: Read the batch's Context & Research entries. For ADRs/DES, read the full documents, not just indexes
3. Research: Follow research pointers — for any libraries/frameworks involved, follow the katachi:research-docs skill guidance to verify current APIs. Investigate patterns. Write important findings to the design doc
4. Implement steps: Work through the batch's steps autonomously
   • Follow relevant decisions (ADRs/DES)
   • Add code comments referencing decisions when the choice would be unclear without context (// See ADR-003 for why we use X instead of Y)
   • Verify each step works before proceeding
5. Mark batch as ✓ Done in the plan file
6. Adapt next batches: If this batch's output differs from what was planned, update context entries in subsequent batches before starting them

Use scratchpad /tmp/implement-$ARGUMENTS-state.md to track:

• Current batch and step
• Issues encountered
• Patterns detected
• Deviations from plan
• Research findings

2. Verify Acceptance Criteria

After all batches are done:

• Run all tests
• Run linting and type checking (fix any issues)
• Perform manual checks against spec
• Exercise the running system: launch the delta's feature and drive its real runtime path — hit the HTTP endpoint (e.g. curl), invoke the CLI, or drive the UI, as fits the project — and confirm the observed behavior matches each acceptance criterion, not just that tests pass. Infer the run command from the project (package.json/Makefile/etc.) or ask if it's unclear. Stay within the local/dev environment: never touch production or other real shared services, and never run destructive or irreversible commands that could affect anything beyond the local setup — reading/writing local files and data and driving local/dev services is fine. Skip only when the change has no runtime surface to drive (a pure library or internal refactor), and say so.
• Ensure all acceptance criteria are met

3. Present for User Review

Show complete implementation to user:

• Summarize what was implemented
• Highlight any deviations from plan (with rationale)
• Note any emergent patterns detected
• Note any research findings written back to design doc

Invite feedback: "What needs adjustment in this implementation?"

4. Iterate Based on User Feedback

Apply user corrections or changes. Re-test after changes. When user rejects code changes: Update documents consistently. Repeat until user approves.

5. Finalize

Update status:

python ${CLAUDE_PLUGIN_ROOT}/scripts/deltas.py status set $ARGUMENTS "✓ Implementation"

Present summary:

"Delta implementation complete for $ARGUMENTS.

[Brief explanation of what was implemented]

Files changed:
- [file]: [what changed]
- ..."

Offer to commit: "Ready to commit this implementation?"

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Parallel Execution

Used when the plan contains independent batches (batches without cross-dependencies). The lead agent orchestrates agents rather than implementing directly.

1. Analyze Batch Dependencies

Read all batches and their Depends on: fields. Identify:

• Independent batches: No dependencies on each other — can run in parallel
• Dependent batches: Must wait for their dependencies to complete

Group into execution waves:

• Wave 1: All batches with no dependencies
• Wave 2: Batches that depend only on Wave 1 batches
• Continue until all batches are scheduled

2. Set Up Team and Tasks

1. Create team with TeamCreate (name: dlt-<delta-id>-implementation)
2. Create one task per batch using the batch scope as the task description
3. Set up task dependencies matching the batch Depends on: fields
4. Assign independent batches to separate agents

3. Spawn Agents for Independent Batches

Spawn agents in parallel (single message, multiple Agent tool calls) with:

• subagent_type: "general-purpose"
• model: "sonnet" (unless user specifies otherwise)
• mode: "bypassPermissions"
• team_name: the team name from step 2
• run_in_background: true

Each agent's prompt must include:

• Their batch assignment (e.g., "You are implementing Batch 2: Search Endpoint for DLT-054")
• Instructions to:
  • Read the plan (docs/delta-plans/$ARGUMENTS.md) — specifically their batch section
  • Read the spec and design documents
  • Load their batch's Context & Research entries (read full ADR/DES documents, not just indexes)
  • Follow research pointers and write findings to the design doc
  • Implement their batch's steps autonomously
  • Mark batch as ✓ Done in the plan file when complete
  • Message team-lead for any decisions not covered by the documentation
  • Run verification for their area (test, typecheck, lint)
• The project's coding style guidelines (copy relevant sections from CLAUDE.md)

4. Monitor, Coordinate, and Execute Dependent Batches

After spawning agents:

• Respond to agent questions about decisions not covered by documentation
• Monitor task progress via TaskList
• When independent batches complete, spawn agents for newly unblocked dependent batches
• Does NOT implement code directly — delegate mode

5. Verify and Review

Once all batches are complete:

• Run full test suites for all affected areas
• Run linting and type checking across the whole project
• Fix any integration issues between agent outputs
• Exercise the running system: launch the delta's feature and drive its real runtime path — hit the HTTP endpoint (e.g. curl), invoke the CLI, or drive the UI, as fits the project — and confirm the observed behavior matches each acceptance criterion, not just that tests pass. Infer the run command from the project (package.json/Makefile/etc.) or ask if it's unclear. Stay within the local/dev environment: never touch production or other real shared services, and never run destructive or irreversible commands that could affect anything beyond the local setup — reading/writing local files and data and driving local/dev services is fine. Skip only when the change has no runtime surface to drive (a pure library or internal refactor), and say so.

Then follow steps 3-5 from Sequential Execution (present to user, iterate, finalize).

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Working with Decisions

Using ADRs and DES:

• Read all decisions referenced in the current batch's context
• Follow constraints unless there's good reason to deviate
• If deviation needed: discuss with user, consider updating decision

Referencing in Code:

• Add comments ONLY when choice would be unclear without context
• Format: // See [DECISION-ID]: [brief reason]
• Don't reference obvious patterns

Pattern Detection:

• Watch for repeated code structures
• Notice cross-cutting concerns
• Suggest documentation when patterns should be consistent

Workflow

Batch-by-batch implementation:

• Read plan and analyze batch dependencies
• For each batch: load context, research, implement steps, mark done
• If independent batches exist: spawn parallel agents per batch
• After all batches: verify acceptance criteria (tests, lint, types, manual checks, plus exercising the running system against local/dev only)
• Present to user for final review
• Iterate based on feedback
• Commit when approved