Task Planning and Execution
SkillProductivityPlan and track execution of a task. Use when starting implementation of a task defined in a milestone plan.
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Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Task Planning and Execution skill
What this skill tells your AI
The instructions your AI receives, as published by mattolson/agent-sandbox in .agents/skills/plan-task/SKILL.md and read by ahel’s review.
Part of the three-tier planning system. See /plan for an overview.
This skill guides implementation of a single task, from initial planning through completion. It maintains an execution log that captures problems, decisions, and learnings along the way.
Inputs
Before starting, read:
- The milestone plan containing this task
docs/plan/learnings.md- to incorporate lessons from previous work- Any relevant decision documents in
docs/plan/decisions/
Output
Two files in docs/plan/milestones/{milestone}/tasks/{task}/:
task.md- The plan and final outcome (living document, update as things change)execution-log.md- Running log updated during implementation (captures the journey)
The task plan is a living document. Update scope, approach, and checklist as things evolve. The execution log preserves history, so there's no need to treat the plan as a snapshot.
Lifecycle
A task moves through three phases:
Phase 1: Planning
Before writing code, understand what we're building and how. Planning is iterative - expect to refine the approach through discussion before it's finalized.
This phase requires your approval before proceeding to execution.
1.1 Context Review
- Review the task's summary, scope, and acceptance criteria from the milestone plan
- Review applicable learnings from previous tasks
- Confirm understanding of what "done" looks like
1.2 Codebase Exploration
- Identify the files and systems involved
- Understand existing patterns and conventions
- Note integration points with other code
1.3 Approach Design
- Outline the implementation approach
- Identify changes needed (new files, modifications, deletions)
- Consider edge cases and error handling
- Flag uncertainties that need resolution
Capture the plan in the task document. Review and iterate until the approach is solid, then get explicit approval before proceeding to implementation.
Phase 2: Execution
During implementation:
- Keep the implementation steps checklist in
task.mdcurrent as steps are completed - Maintain the execution log in
execution-log.md
When to Update the Log
Update the execution log whenever:
- A new issue is encountered - describe the issue and its solution
- A key decision is made - capture the choice and rationale
- A lesson is learned - note insights that apply to future work
- 10 minutes have passed since the last update - review and capture any salient information
Do not let updates accumulate. Frequent, small updates are more valuable than infrequent summaries.
What to Capture
- Issues and solutions: Problems encountered and how they were resolved
- Decisions: Technical choices and their rationale
- Scope changes: Anything that changed from the original plan and why
- Observations: Things noticed that might be relevant later
Checkpoints
At natural breakpoints, review progress:
- Is the approach still sound?
- Have we discovered anything that changes the plan?
- Are there decisions that should be recorded formally in
docs/plan/decisions/? - Does the implementation steps checklist need updating (new steps, changed steps, removed steps)?
Phase 3: Completion
When the task is done:
3.1 Acceptance Verification
- Verify each acceptance criterion is met
- Confirm tests are passing
- Ensure the PR is ready for review
3.2 Learning Capture
Review the execution log and extract learnings from this task:
- What would we do differently next time?
- What worked well that we should repeat?
- What did we learn that applies to future tasks?
Consolidate and distill insights from the execution log entries into docs/plan/learnings.md.
3.3 Cleanup
- Update the milestone plan if this task revealed new information
- Create decision documents for any significant decisions made
- Note if downstream tasks are affected
Task Document Template
Create at docs/plan/milestones/{milestone}/tasks/{task}/task.md:
# Task: {identifier} - {name}
## Summary
{From milestone plan}
## Scope
{From milestone plan, updated if changed}
## Acceptance Criteria
{From milestone plan}
- [ ] {Criterion 1}
- [ ] {Criterion 2}
## Applicable Learnings
{Lessons from previous work that apply to this task}
## Plan
### Files Involved
{List of files to create, modify, or delete}
### Approach
{How we're going to implement this}
### Implementation Steps
- [ ] {Step 1}
- [ ] {Step 2}
- [ ] {Step 3}
### Open Questions
{Uncertainties to resolve during implementation}
## Outcome
### Acceptance Verification
- [x] {Criterion 1 - verified}
- [x] {Criterion 2 - verified}
### Learnings
{What we learned from this task - also append to docs/plan/learnings.md}
### Follow-up Items
{Anything discovered that affects other tasks or the milestone plan}
Execution Log Template
Create at docs/plan/milestones/{milestone}/tasks/{task}/execution-log.md.
Entries are in reverse chronological order - newest at the top, so the latest activity is always visible first.
# Execution Log: {identifier} - {name}
## {Timestamp} - Latest entry
{Entry describing what happened}
**Issue:** {If applicable - describe the problem}
**Solution:** {How it was resolved}
**Decision:** {If applicable - what was decided and why}
**Learning:** {If applicable - insight for future work}
## {Timestamp} - Previous entry
{Earlier entry}
Signals
- GitHub stars
- 205
- Forks
- 19
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
plan-task- Source
- github.com/mattolson/agent-sandbox