AILANG Sprint Executor Skill

AILANG Sprint Executor

Execute an approved sprint plan with continuous progress tracking, testing, and documentation updates. Supports parallel execution of independent milestones using Task sub-agents for faster sprints.

Current State

Branch: !'git branch --show-current'
Working directory: !'git status --short | head -5'
Active sprints: !'ls .ailang/state/sprints/ 2>/dev/null | head -5 || echo "none"'
Sprint progress: !'for f in .ailang/state/sprints/sprint_*.json; do [ -f "$f" ] && echo "$(basename $f): status=$(jq -r .status $f 2>/dev/null), features=$(jq "[.features[] | select(.passes==true)] | length" $f 2>/dev/null)/$(jq ".features | length" $f 2>/dev/null)"; done 2>/dev/null || echo "no sprint files"'
Tests: !'make test 2>&1 | tail -1'
Uncommitted changes: !'git diff --stat HEAD 2>/dev/null | tail -1 || echo "clean"'

Use the data above first. Only re-run these commands manually if the injected context is empty or you need to refresh after making changes.

Quick Start

Sequential execution (default):

# User says: "Execute the sprint plan in design_docs/20251019/M-S1.md"
# This skill will:
# 1. Validate prerequisites (tests pass, linting clean)
# 2. Create TodoWrite tasks for all milestones
# 3. Execute each milestone with test-driven development
# 4. Run checkpoint after each milestone (tests + lint)
# 5. Update CHANGELOG and sprint plan progressively
# 6. Pause after each milestone for user review

Parallel execution (for independent milestones):

# User says: "Execute sprint plan at docs/sprint-plans/M-FOO.md in parallel"
# This skill will:
# 1. Read sprint plan and identify all milestones
# 2. Analyze dependencies — group independent milestones for parallel execution
# 3. Spawn Task sub-agents per milestone (branch, TDD, implement, commit)
# 4. Act as integration agent — merge branches, run full test suite
# 5. Write sprint retrospective with timing and friction analysis

When to Use This Skill

Invoke this skill when:

User says "execute sprint", "start sprint", "begin implementation"
User has an approved sprint plan ready to implement
User wants guided execution with built-in quality checks
User needs progress tracking and pause points

Choose parallel mode when:

Sprint has 3+ milestones with independent work
Milestones touch different files/packages (low merge conflict risk)
User says "execute in parallel", "parallel sprint", or "use sub-agents"

Choose sequential mode when:

Milestones have strict dependencies (M2 depends on M1's output)
All milestones touch the same files
Sprint is small (1-2 milestones)

Coordinator Integration

When invoked by the AILANG Coordinator (detected by GitHub issue reference in the prompt), you MUST output these markers at the end of your response:

IMPLEMENTATION_COMPLETE: true
BRANCH_NAME: coordinator/task-XXXX
FILES_CREATED: file1.go, file2.go
FILES_MODIFIED: file3.go, file4.go

Why? The coordinator uses these markers to:

Track implementation completion
Post updates to GitHub issues
Trigger the merge approval workflow

Example completion:

## Implementation Complete

All milestones have been completed and tests pass.

**IMPLEMENTATION_COMPLETE**: true
**BRANCH_NAME**: `coordinator/task-abc123`
**FILES_CREATED**: `internal/new_file.go`, `internal/new_test.go`
**FILES_MODIFIED**: `internal/existing.go`

Core Principles

Test-Driven: All code must pass tests before moving to next milestone
Lint-Clean: All code must pass linting before moving to next milestone
Document as You Go: Update CHANGELOG.md and sprint plan progressively
Pause for Breath: Stop at natural breakpoints for review and approval
Track Everything: Use TodoWrite to maintain visible progress
DX-First: Improve AILANG development experience as we go - make it easier next time
Parallelize When Possible: Independent milestones run as concurrent Task sub-agents for speed
Failing Tests First: Sub-agents MUST write failing tests before implementation — no exceptions
Determinism Verified: Pure builtins (IsPure: true) MUST be tested with -count=20 — single-pass tests hide Go map iteration nondeterminism. Use realistic inputs, not toy examples. See milestone_checklist.md for details.
Windows-Proof Every New Test (added 2026-07-14 after THREE same-class round-2/red-dev cycles: iter-29 exampleRunPath backslash paths failed both Windows CI jobs → round-1 FAIL 81/100; iter-30 TestDocsSearch_StillDesignDocSimHash asserted design_docs/ against native-separator output → both Windows jobs red; M-SMT-CALLEE-SORT-GATE e2e tests invoked z3 which the Windows runner lacks → dev itself red). The local rig is macOS; CI runs test-windows + Build windows-latest you never exercise. Before declaring ANY milestone done, scan its new/changed tests for: (a) path assertions — output containing paths must be normalized (filepath.ToSlash / replace \ with /) before strings.Contains-style checks; (b) external binaries — anything invoking z3 or other non-Go tools must guard with the repo's availability convention (smt.Z3Available() → t.Skip; ubuntu CI installs z3 at ci.yml, Windows does NOT); (c) golden files vs native rendering — goldens comparing rendered paths/line endings need normalization. Two seconds of scan beats a 20-minute CI round-trip.

Multi-Session Continuity (NEW)

Sprint execution can now span multiple Claude Code sessions!

Based on Anthropic's long-running agent patterns, sprint-executor implements the "Coding Agent" pattern:

Session Startup Routine: Every session starts with session_start.sh
- Checks working directory
- Reads JSON progress file (.ailang/state/sprints/sprint_<id>.json)
- Reviews recent git commits
- Validates tests pass
- Prints "Here's where we left off" summary
Structured Progress Tracking: JSON file tracks state
- Features with passes: true/false/null (follows "constrained modification" pattern)
- Velocity metrics updated automatically
- Clear checkpoint messages
- Session timestamps
Pause and Resume: Work can be interrupted at any time
- Status saved to JSON: not_started, in_progress, paused, completed
- Next session picks up exactly where you left off
- No loss of context or progress

For JSON schema details, see resources/json_progress_schema.md

Available Scripts

`scripts/session_start.sh <sprint_id>` NEW

Resume sprint execution across multiple sessions.

When to use: ALWAYS at the start of EVERY session continuing a sprint.

What it does:

Syncs GitHub issues via ailang messages import-github
Loads sprint JSON progress file
Shows linked GitHub issues with titles from local messages
Displays feature progress summary
Shows velocity metrics
Runs tests to verify clean state
Prints "Here's where we left off" summary

`scripts/validate_prerequisites.sh`

Validate prerequisites before starting sprint execution.

What it checks:

Syncs GitHub issues via ailang messages import-github
Working directory status (clean/uncommitted changes)
Current branch (dev or main)
Test suite passes
Linting passes
Shows unread messages (potential issues/feedback)

`scripts/validate_sprint_json.sh <sprint_id>` NEW

REQUIRED before starting any sprint. Validates that sprint JSON has real milestones (not placeholders).

What it checks:

No placeholder milestone IDs (MILESTONE_ID)
No placeholder acceptance criteria (Criterion 1/2)
At least 2 milestones defined
All milestones have custom values (not defaults)
Dependencies reference valid milestone IDs

Exit codes:

0 - Valid JSON, ready for execution
1 - Invalid JSON or placeholders detected (sprint-planner must fix)

`scripts/milestone_checkpoint.sh <milestone_name> [sprint_id]`

Run checkpoint after completing a milestone.

CRITICAL: Tests passing ≠ Feature working! This script verifies with REAL DATA.

What it does:

Runs make test and make lint
Shows git diff of changed files
Checks file sizes (AI-friendly codebase guidelines)
Runs milestone-specific verification (database queries, API calls, etc.)
Shows JSON update reminder with current milestone statuses

Sprint-specific verification (e.g., M-TASK-HIERARCHY):

M1: Checks observatory_sync.go exists, tasks in DB with non-empty IDs
M2: Checks OTEL_RESOURCE_ATTRIBUTES in executor, spans with task attributes
M3: Checks spans have task_id linked
M4: Checks task aggregates (span_count, tokens) are populated
M5: Checks hierarchy API returns data for existing tasks
M6: Checks TaskHierarchy component imported AND connected in UI
M7: Checks backfill command exists and responds to --help

Example:

# Verify M1 (should pass if entity sync works)
.claude/skills/sprint-executor/scripts/milestone_checkpoint.sh M1 M-TASK-HIERARCHY

# Verify M2 (will fail if OTEL attributes not propagated)
.claude/skills/sprint-executor/scripts/milestone_checkpoint.sh M2 M-TASK-HIERARCHY

Exit codes:

0 - Checkpoint passed (all verifications succeeded)
1 - Checkpoint FAILED (DO NOT mark milestone complete)

`scripts/acceptance_test.sh <milestone_id> <test_type>` NEW

Run end-to-end acceptance tests (parser, builtin, examples, REPL, e2e).

`scripts/finalize_sprint.sh <sprint_id> [version]` NEW

Finalize a completed sprint by moving design docs and updating status.

What it does:

Moves design doc from planned/ to implemented/<version>/
Moves sprint plan markdown to implemented/<version>/
Updates design doc status to "IMPLEMENTED"
Updates sprint JSON status to "completed"
Updates file paths in sprint JSON

When to use: After all milestones pass and sprint is complete.

Example:

.claude/skills/sprint-executor/scripts/finalize_sprint.sh M-BUG-RECORD-UPDATE-INFERENCE v0_4_9

Execution Flow

Phase 0: Session Resumption (for continuing sprints)

If this is NOT the first session for this sprint:

# ALWAYS run session_start.sh first!
.claude/skills/sprint-executor/scripts/session_start.sh <sprint-id>

This prints "Here's where we left off" summary. Then skip to Phase 2 to continue with the next milestone.

Phase 1: Initialize Sprint (first session only)

Validate Sprint JSON - Run validate_sprint_json.sh <sprint-id> REQUIRED FIRST
- If validation fails, STOP and notify user that sprint-planner must fix the JSON
- Do NOT proceed with placeholder milestones
Read Sprint Plan - Parse markdown + load JSON progress file (.ailang/state/sprints/sprint_<id>.json)
Validate Prerequisites - Run validate_prerequisites.sh (tests, linting, git status)
Create Todo List - Use TodoWrite to track all milestones
Initial Status Update - Mark sprint as "🔄 In Progress"
Initial DX Review - Consider tools/helpers that would make sprint easier (see resources/dx_improvement_patterns.md)

Phase 2: Choose Execution Mode

After Phase 1 initialization, choose between sequential or parallel execution based on milestone dependencies.

Decision criteria:

If milestones form a strict chain (each depends on previous) → Phase 2A: Sequential
If 2+ milestones are independent (no shared file overlap) → Phase 2B: Parallel
Mixed dependencies → Execute dependent chains sequentially, independent groups in parallel

Phase 2A: Sequential Execution (default)

For each milestone:

Pre-Implementation - Mark milestone as in_progress in TodoWrite
Implement - Write code with DX awareness (helper functions, debug flags, better errors)
Write Tests - TDD recommended for complex logic, comprehensive coverage required
Verify Quality - Run milestone_checkpoint.sh <milestone-name> (tests + lint must pass)
Write Documentation (evaluator verifies completeness):
- CHANGELOG entry in changelogs/ — find active file with ls changelogs/ | grep current
- Example files for new language features: examples/runnable/<feature>.ail
- Update examples/manifest.json if adding examples
- Mark milestone as ✅ in sprint plan markdown
Update Sprint JSON:
- Set passes: true/false in .ailang/state/sprints/sprint_<id>.json
- Set completed: "<ISO timestamp>"
- Add notes: "<summary of what was done>"
DX Reflection - Identify and implement quick wins (<15 min), defer larger improvements
Pause for Breath - Show progress, ask user if ready to continue

After all milestones complete, proceed to Phase 4: Finalize Sprint.

Completion gate — sprint artifacts (MANDATORY, added 2026-07-11 after 2 recorded frictions: mission iterations 8 + 9 both shipped code with plan checkboxes unticked and the sprint JSON stale/absent, forcing the evaluator to reconstruct bookkeeping from commit history). The final milestone commit is not "done" until, IN THE SAME TREE the code was written in (i.e., the worktree, so it rides the same branch/PR):

Every acceptance-criteria checkbox in the sprint-plan markdown is ticked (or annotated why not).
.ailang/state/sprints/sprint_<id>.json has status: "completed" and per-milestone passes/completed/notes filled. If the file is absent from the worktree (the planner created it in the main tree but didn't commit it — the iteration-9 root cause), CREATE it there from the sprint plan rather than skipping the step.
Both are committed. Artifacts that only exist in an uncommitted main-tree file do not exist as far as the branch, the PR, or the evaluator are concerned.

Quick tips:

Use parser test helpers from internal/parser/test_helpers.go
Use DEBUG_PARSER=1 for token flow tracing
Use make doc PKG=<package> for API discovery
See resources/parser_patterns.md for parser/pattern matching
See resources/codegen_patterns.md for Go code generation (builtins, expressions)
See resources/api_patterns.md for common API patterns
See resources/dx_improvement_patterns.md for DX opportunities

Phase 2B: Parallel Milestone Execution

Use this mode when independent milestones can be developed concurrently using Task sub-agents.

Step 1: Dependency Analysis

Read the sprint plan and build a dependency graph from the milestones:

For each milestone M:
  - Parse M.dependencies[] from sprint JSON
  - Identify files M will touch (use Grep to scope relevant files)
  - Check for file-level conflicts between milestones

Group milestones into parallelizable waves:

Wave 1: [M1, M3, M5]  ← no dependencies, no shared files
Wave 2: [M2, M4]       ← depend on Wave 1 results
Wave 3: [M6]           ← depends on Wave 2 results

Report the dependency graph to the user before proceeding:

Dependency Analysis:
  Wave 1 (parallel): M1 (parser), M3 (stdlib), M5 (docs)
  Wave 2 (parallel): M2 (depends on M1), M4 (depends on M3)
  Wave 3 (sequential): M6 (depends on M2 + M4)

Estimated speedup: ~2.5x over sequential execution
Proceed with parallel execution? [y/n]

Step 2: Spawn Task Sub-Agents Per Milestone

For each parallelizable wave, spawn one Task sub-agent per milestone in a single message (so they execute concurrently). Each sub-agent receives a detailed prompt:

# Example: spawning Wave 1 in parallel (all in ONE message with multiple Task calls)
Task(
    description=f"Sprint milestone {milestone.id}",
    subagent_type="general-purpose",
    # Per-role model pin (m-mission-agentic-provider-routing M1): never inherit the controller
    # session model. Mission runs export MISSION_EXECUTOR_MODEL; standalone runs default to Opus.
    model=os.environ.get("MISSION_EXECUTOR_MODEL", "opus"),  # Agent-tool alias (opus|sonnet|fable), NOT a full model ID
    prompt=f"""
    You are executing milestone {milestone.id}: {milestone.description}
    Sprint plan: {plan_path}

    ## Your Scope
    Branch: sprint/{milestone_slug}
    Relevant files (from dependency analysis): {milestone.relevant_files}

    ## MANDATORY: Test-Driven Development

    **Step 1: Create branch**
    ```bash
    git checkout -b sprint/{milestone_slug}
    ```

    **Step 2: Write FAILING tests FIRST**
    For each acceptance criterion in the milestone:
    - Write a test that captures the expected behavior
    - Run `make test` — confirm the new tests FAIL
    - Do NOT proceed to implementation until you have failing tests

    Acceptance criteria:
    {milestone.acceptance_criteria}

    **Step 3: Implement until all tests pass**
    - Write the minimum code to make each failing test pass
    - Run `make test` after each change
    - Run `make lint` — fix any issues immediately
    - Keep functions small and focused (<50 lines)

    **Step 4: Commit with milestone reference**
    ```bash
    git add <specific-files>
    git commit -m "Complete {milestone.id}: {milestone.description}

    Acceptance criteria met:
    {acceptance_criteria_checklist}

    Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>"
    ```

    **Step 5: Report back**
    At the END of your response, output this EXACT format:
    ```
    MILESTONE_REPORT:
    milestone_id: {milestone.id}
    status: success|failure
    tests_passed: <count>
    tests_failed: <count>
    files_changed: <comma-separated list>
    branch: sprint/{milestone_slug}
    notes: <brief summary of what was done>
    ```

    ## RULES
    - Do NOT skip the failing-test-first step
    - Do NOT commit with failing tests
    - Do NOT modify files outside your scope: {milestone.relevant_files}
    - If blocked, report status: failure with clear explanation
    """
)

Critical rules for sub-agent spawning:

All sub-agents in the same wave MUST be launched in a single message with multiple Task calls
Each sub-agent works on its own branch (sprint/<milestone-slug>)
Sub-agents must NOT touch files outside their scoped set
Sub-agents must use Grep to verify they're only reading relevant files before editing

Step 3: Collect Sub-Agent Results

After all sub-agents in a wave complete, parse their MILESTONE_REPORT blocks:

{
  "wave": 1,
  "results": [
    {"milestone_id": "M1", "status": "success", "tests_passed": 12, "tests_failed": 0, "branch": "sprint/m1-parser-fix"},
    {"milestone_id": "M3", "status": "success", "tests_passed": 8, "tests_failed": 0, "branch": "sprint/m3-stdlib-list"},
    {"milestone_id": "M5", "status": "failure", "tests_passed": 3, "tests_failed": 2, "branch": "sprint/m5-docs"}
  ]
}

If any milestone in a wave failed:

Do NOT proceed to the next wave
Report the failure to the user with the sub-agent's notes
Options: (a) retry the failed milestone, (b) fix manually, (c) skip and continue
Failed milestones block any downstream milestones that depend on them

If all milestones in a wave succeeded:

Proceed to Phase 3: Integration for this wave
Then continue with the next wave

Step 4: Repeat for Each Wave

Execute waves sequentially (Wave 1 → integrate → Wave 2 → integrate → ...). Within each wave, milestones run in parallel.

Phase 3: Integration Agent

After parallel milestones complete, act as the integration agent.

This phase merges all milestone branches and verifies the combined result. The executor (you) performs this directly — do NOT delegate integration to sub-agents.

Step 3.1: Create Integration Branch

# Start from the base branch (dev or main)
git checkout dev
git checkout -b sprint/integration

Step 3.2: Merge Milestone Branches

Merge each successful milestone branch into sprint/integration, one at a time:

# Merge in dependency order (Wave 1 first, then Wave 2, etc.)
git merge sprint/m1-parser-fix --no-ff -m "Integrate M1: parser fix"
git merge sprint/m3-stdlib-list --no-ff -m "Integrate M3: stdlib list"
git merge sprint/m5-docs --no-ff -m "Integrate M5: docs"

If a merge conflict occurs:

Identify which milestones conflict (check the conflicting files)
Analyze the conflict — determine which milestone's changes take priority
Resolve the conflict, preserving both milestones' functionality
Run make test after resolution to verify nothing broke
Document the conflict and resolution in the sprint retrospective

Step 3.3: Run Full Test Suite

make test              # ALL tests must pass
make lint              # ALL linting must pass
make fmt-check         # Go formatting must be clean
make check-file-sizes  # NO file >800 lines (CI-only gate; make test/lint DON'T cover it)

Match CI locally before declaring done — make test/lint are NARROWER than CI (added 2026-07-21 after iter-72 shipped TWO CI-only reds from the same gap — the local done-gate missing remote checks): (a) make check-file-sizes — a file already near 800 lines (iter-72: claude.go was at 799; a 30-line addition tipped it to 829, red) trips this gate invisibly to make test/lint; fix by extracting a cohesive block to a sibling file, not by shaving one line. (b) Windows — test-windows/Build windows-latest run the Go suite on Windows you never exercise locally; re-scan new tests for the rule-#10 path/binary/golden traps (iter-72: a strings.Contains of settings JSON vs an unescaped hook path passed on Unix, failed on Windows where JSON escapes \ to \\). Two seconds of local scan beats a 15-minute CI round-trip per red.

If integration tests fail:

Identify which milestone introduced the failure using git bisect or selective reverts:

# Revert last merge to isolate
git revert -m 1 HEAD
make test
# If tests pass now, the reverted milestone caused the failure

Fix the conflict between milestones
Re-run full test suite
Do NOT proceed until all tests pass

Step 3.4: Update Sprint Progress JSON

After successful integration, update .ailang/state/sprints/sprint_<id>.json:

Set passes: true for each integrated milestone
Update actual_loc from git diff stats
Set completed timestamps
Update velocity metrics
Set sprint status: "in_progress" (or "completed" if all waves done)

Phase 3.5: Sprint Retrospective

After ALL waves are integrated and tests pass, write a retrospective.

Create docs/sprint-retros/<sprint-id>-retro.md with:

# Sprint Retrospective: <sprint-id>

## Summary
- **Sprint**: <sprint-id>
- **Duration**: <actual days> (estimated: <planned days>)
- **Execution mode**: Parallel (Wave count: N)
- **Total milestones**: N (passed: X, failed: Y)

## Milestone Timing

| Milestone | Estimated LOC | Actual LOC | Time Taken | Status |
|-----------|--------------|------------|------------|--------|
| M1        | 200          | 185        | 45 min     | ✅     |
| M2        | 150          | 210        | 1h 10min   | ✅     |
| ...       |              |            |            |        |

## Parallelization Results
- **Waves executed**: 3
- **Max parallelism**: 3 agents (Wave 1)
- **Speedup vs sequential**: ~2.1x
- **Integration conflicts**: 1 (M1 + M3 shared `types.go`)

## Friction Encountered
- <Description of any blockers, unexpected complexity, or tooling gaps>
- <Merge conflicts and how they were resolved>
- <Sub-agent failures and root causes>

## Recommendations for Next Sprint
- <Suggestions for improving parallelization>
- <Files that should NOT be parallelized (high conflict risk)>
- <DX improvements identified during execution>

Phase 4: Finalize Sprint

Final Commit - Git commit with sprint summary (milestones, LOC, velocity)
Summary Report - Compare planned vs actual (LOC, time, velocity)
DX Impact Summary - Document improvements made during sprint

Note: Documentation verification, design doc moves, and quality checks are handled by the sprint-evaluator in Phase 5. The executor focuses on implementation — the evaluator judges it.

Phase 5: Hand Off to sprint-evaluator

CRITICAL: After finalizing a sprint, ALWAYS hand off to sprint-evaluator for independent quality assessment.

Based on Anthropic's generator-evaluator architecture — separating the agent doing the work from the agent judging it is "a strong lever" for quality.

This is the standard workflow:

sprint-executor (this skill): Implements the plan with TDD
sprint-evaluator (quality judge): Independently evaluates implementation against design doc and acceptance criteria

Send handoff message:

ailang messages send sprint-evaluator '{
  "type": "implementation_complete",
  "correlation_id": "eval_<sprint-id>_<date>",
  "sprint_id": "<sprint-id>",
  "branch_name": "<branch>",
  "sprint_json_path": ".ailang/state/sprints/sprint_<id>.json",
  "design_doc_path": "<path to design doc>",
  "files_created": [...],
  "files_modified": [...],
  "evaluation_round": 1
}' --title "Sprint <sprint-id> ready for evaluation" --from "sprint-executor"

Why separate evaluation?

Agents doing work struggle with self-evaluation ("confidently praise work even when mediocre")
Concrete scoring rubric (100 points, 70 to pass) catches issues executor may miss
Feedback loop drives quality: fail → specific feedback → retry (max 3 rounds)

Handling Evaluation Feedback

If session starts with an evaluation_feedback message:

Read the feedback: ailang messages read MSG_ID
Parse the specific issues (files, criteria, suggestions)
Address each issue in priority order (high severity first)
Re-run milestone_checkpoint.sh for affected milestones
Re-run finalize_sprint.sh
Hand off to sprint-evaluator again (increment evaluation_round)

Key Features

Continuous Testing

Run make test after every file change
Never proceed if tests fail
Track test count increase

Continuous Linting

Run make lint after implementation
Fix linting issues immediately
Use make fmt for formatting

Progress Tracking

TodoWrite shows real-time progress
Sprint plan updated at each milestone
CHANGELOG.md grows incrementally
JSON file tracks structured state (NEW)
Git commits create audit trail

GitHub Issue Integration (NEW)

Uses ailang messages for GitHub sync and issue tracking!

Automatic sync:

session_start.sh and validate_prerequisites.sh run ailang messages import-github first
Linked issues shown with titles from local message database

If github_issues is set in sprint JSON:

validate_sprint_json.sh shows linked issues
session_start.sh displays issue titles from messages
milestone_checkpoint.sh reminds you to include Refs #... in commits
finalize_sprint.sh suggests commit message with issue references

Commit message format:

# During development - use "refs" to LINK without closing
git commit -m "Complete M1: Parser foundation, refs #17"

# Final sprint commit - use "Fixes" to AUTO-CLOSE issues on merge
git commit -m "Finalize sprint M-BUG-FIX

Fixes #17
Fixes #42"

Important: "refs" vs "Fixes"

refs #17 - Links commit to issue (NO auto-close)
Fixes #17, Closes #17, Resolves #17 - AUTO-CLOSES issue when merged

Workflow:

Sprint JSON has github_issues: [17, 42] (set by sprint-planner, deduplicated)
During development: Use refs #17 to link commits without closing
Final commit: Use Fixes #17 to auto-close issues on merge
No duplicates: ailang messages import-github checks existing issues before importing

Pause Points

After each milestone completion
When tests or linting fail (fix before continuing)
When user requests "pause"
When encountering unexpected issues

Error Handling

If tests fail: Show output, ask how to fix, don't proceed
If linting fails: Show output, ask how to fix, don't proceed
If implementation unclear: Ask for clarification, don't guess
If milestone takes much longer than estimated: Pause and reassess

Resources

Multi-Session State

JSON Schema: resources/json_progress_schema.md - Sprint progress format
Session Startup: Use session_start.sh ALWAYS for continuing sprints

Development Tools

Parser & Patterns: resources/parser_patterns.md - Parser development + pattern matching pipeline
Codegen Patterns: resources/codegen_patterns.md - Go code generation for new features/builtins
API Patterns: resources/api_patterns.md - Common constructor signatures and API gotchas
Developer Tools: resources/developer_tools.md - Make targets, ailang commands, workflows
DX Improvements: resources/dx_improvement_patterns.md - Identifying and implementing DX wins
DX Quick Reference: resources/dx_quick_reference.md - ROI calculator, decision matrix
Milestone Checklist: resources/milestone_checklist.md - Step-by-step per milestone

External Documentation

Parser Guide: docs/guides/parser_development.md
Website: https://ailang.sunholo.com/

Progressive Disclosure

This skill loads information progressively:

Always loaded: This SKILL.md file (YAML frontmatter + execution workflow) - ~250 lines
Execute as needed: Scripts in scripts/ directory (validation, checkpoints, testing)
Load on demand: Resources in resources/ directory (detailed guides, patterns, references)

Prerequisites

Working directory clean (or only sprint-related changes)
Current branch dev (or specified in sprint plan)
All existing tests pass
All existing linting passes
Sprint plan approved and documented
JSON progress file created AND POPULATED by sprint-planner (not just template!)
JSON must pass validation: scripts/validate_sprint_json.sh <sprint-id>

Failure Recovery

If Tests Fail During Sprint

Show test failure output
Ask user: "Tests failing. Options: (a) fix now, (b) revert change, (c) pause sprint"
Don't proceed until tests pass

If Linting Fails During Sprint

Show linting output
Try auto-fix: make fmt
If still failing, ask user for guidance
Don't proceed until linting passes

If Implementation Blocked

Show what's blocking progress
Ask user for guidance or clarification
Consider simplifying the approach
Document the blocker in sprint plan

If Velocity Much Lower Than Expected

Pause and reassess after 2-3 milestones
Calculate actual velocity
Propose: (a) continue as-is, (b) reduce scope, (c) extend timeline
Update sprint plan with revised estimates

If Sub-Agent Fails (Parallel Mode)

Parse the MILESTONE_REPORT from the failed sub-agent
Show failure notes and any test output to the user
Options: (a) retry milestone with a new sub-agent, (b) fix manually on the milestone branch, (c) skip milestone and continue
Do NOT merge a failed milestone's branch into sprint/integration
Do NOT proceed to next wave if a failed milestone blocks downstream milestones

If Integration Merge Conflicts (Parallel Mode)

Identify conflicting files between milestone branches
Merge non-conflicting branches first
For conflicts: resolve preserving both milestones' functionality
Run make test after each conflict resolution
Document all conflicts in the sprint retrospective
If resolution is unclear, ask the user before proceeding

Coordinator Integration (v0.6.2+)

The sprint-executor skill integrates with the AILANG Coordinator for automated workflows.

Autonomous Workflow

When configured in ~/.ailang/config.yaml, the sprint-executor agent:

Receives handoff messages from sprint-planner
Executes sprint plans with TDD and continuous linting
Creates approval request for code review before merge

coordinator:
  agents:
    - id: sprint-executor
      inbox: sprint-executor
      workspace: /path/to/ailang
      capabilities: [code, test, docs]
      trigger_on_complete: [sprint-evaluator]  # Evaluator judges implementation
      auto_approve_handoffs: false
      auto_merge: false
      session_continuity: true
      max_concurrent_tasks: 1

Receiving Handoffs from sprint-planner

The sprint-executor receives:

{
  "type": "plan_ready",
  "correlation_id": "sprint_M-CACHE_20251231",
  "sprint_id": "M-CACHE",
  "plan_path": "design_docs/planned/v0_6_3/m-cache-sprint-plan.md",
  "progress_path": ".ailang/state/sprints/sprint_M-CACHE.json",
  "session_id": "claude-session-xyz",
  "estimated_duration": "3 days",
  "total_loc_estimate": 650
}

Session Continuity

With session_continuity: true:

Receives session_id from sprint-planner handoff
Uses --resume SESSION_ID for Claude Code CLI
Preserves full conversation context from design → planning → execution
Maintains understanding of design decisions and rationale

Human-in-the-Loop

With auto_merge: false:

Sprint is executed in isolated worktree
All milestones completed with tests passing
Approval request shows:
- Git diff of all changes
- Test results
- CHANGELOG updates
Human reviews implementation quality
Approve → Changes merged to main branch
Reject → Worktree preserved for fixes

End of Pipeline

As the last agent in the chain:

trigger_on_complete: [] means no automatic handoff
Final approval merges directly to main branch
Sprint JSON updated to status: completed
Design docs moved to implemented/ directory

Notes

This skill is long-running - expect it to take hours or days
Pause points are built in - you're not locked into finishing
Sprint plan is the source of truth - but reality may require adjustments
Git commits create a reversible audit trail
TodoWrite provides real-time visibility into progress
Test-driven development is non-negotiable - tests must pass
Multi-session continuity - Sprint can span multiple Claude Code sessions
JSON state tracking - Structured progress in .ailang/state/sprints/sprint_<id>.json
Parallel execution - Independent milestones run as concurrent Task sub-agents (Phase 2B)
Failing tests first - Sub-agents MUST write failing tests before implementation — no exceptions
No merging failing branches - Only branches with all tests passing enter sprint/integration
Sprint retrospectives - Written to docs/sprint-retros/ after parallel sprints complete

Agent Skills: AILANG Sprint Executor

Install this agent skill to your local

Skill Files