
execute-project
The ONLY way to interact with existing projects. Load when user references ANY project by name, ID, or number. Includes: continue, resume, status, progress, check, review, work on [existing project]. NEVER read project files directly.
"The ONLY way to interact with existing projects. Load when user references ANY project by name, ID, or number. Includes: continue, resume, status, progress, check, review, work on [existing project]. NEVER read project files directly."
šÆ Onboarding Awareness (CONTEXTUAL SUGGESTIONS)
During project execution, AI should watch for teachable moments:
Onboarding Suggestions During Execution
Check learning_tracker.completed in user-config.yaml for contextual suggestions:
If user encounters repeating patterns:
learn_skills: false ā Suggest when user does something that could be a skill
Pattern detection: If user asks to do something similar to what they've done before,
or creates similar outputs repeatedly ā gently suggest 'learn skills':
š” I notice this task is similar to [previous task]. If you do this regularly,
it might be worth learning about Skills (reusable workflows). Run 'learn skills'
(10 min) when you have time.
If user asks about integrations during execution:
learn_integrations: false ā Suggest when user mentions external tools
š” You mentioned [tool]. If you work with external tools often, 'learn integrations'
(10 min) teaches how Nexus connects to services like Notion, GitHub, etc.
On project completion (100%):
If multiple onboarding skills incomplete, suggest the next logical one:
š Project complete! You're getting the hang of Nexus.
š” Next learning opportunity: 'learn skills' - turn repeating work into
reusable workflows (10 min). Or 'learn nexus' for system mastery (15 min).
DO NOT Suggest If:
- User is mid-task and focused (wait for natural breaks)
- User has explicitly dismissed learning suggestions
- All onboarding already complete
Skill: Execute Project
Purpose: Systematically execute project work with continuous progress tracking and task completion validation.
Load When:
- User says: "execute project [ID/name]"
- User says: "continue [project-name]"
- User says: "work on [project-name]"
- Orchestrator detects: Project continuation (IN_PROGRESS status)
Core Value: Ensures work stays aligned with planned tasks and provides continuous visibility into progress.
Quick Reference
What This Skill Does:
- ā Loads project context (planning files, current progress)
- ā Identifies current phase/section and next uncompleted task
- ā Executes work systematically (section-by-section or task-by-task)
- ā Continuously updates task completion using bulk-complete-tasks.py
- ā Validates progress after each section/checkpoint
- ā Handles pause-and-resume gracefully
- ā Auto-triggers close-session when done
Key Scripts Used:
nexus-loader.py --project [ID]- Load project contextbulk-complete-tasks.py --project [ID] --section [N]- Complete sectionbulk-complete-tasks.py --project [ID] --tasks [range]- Complete specific tasksbulk-complete-tasks.py --project [ID] --all- Complete all (when project done)
Prerequisites
Before using this skill, ensure:
- ā
Project exists in
02-projects/with valid metadata - ā
Planning files exist:
overview.md,plan.md(ordesign.md),steps.md(ortasks.md) - ā
Tasks file has checkbox format:
- [ ] Task description - ā
Project status is
IN_PROGRESSorPLANNING(ready to execute)
If prerequisites not met:
- Missing project ā Use
create-projectskill first - Missing planning ā Complete planning phase before execution
- Invalid task format ā Validate with
validate-systemskill
Workflow: 7-Step Execution Process
Step 1: Initialize Progress Tracking
Action: Create comprehensive TodoWrite with ALL workflow steps
Template:
1. Load project context
2. Identify current phase/section
3. Execute Section 1
4. Bulk-complete Section 1
5. Execute Section 2
6. Bulk-complete Section 2
... (repeat for all sections)
N. Project completion validation
N+1. Trigger close-session
Purpose: Provides user visibility into entire execution workflow
Mark complete when: TodoWrite created with all steps
Step 2: Load Project Context
Action: Load complete project context using nexus-loader.py
Commands:
# Load project with full content (overview, plan, steps, etc.)
python 00-system/core/nexus-loader.py --project [project-id]
The loader returns:
- File paths for all planning files (overview.md, plan.md, steps.md, etc.)
- YAML metadata extracted from each file
- Output file listings
_usage.recommended_reads- list of paths to read
Then use Read tool in parallel to load the file contents:
Read: {path from recommended_reads[0]}
Read: {path from recommended_reads[1]}
Read: {path from recommended_reads[2]}
Display Project Summary:
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
PROJECT: [Project Name]
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
Status: IN_PROGRESS
Progress: [X]/[Y] tasks complete ([Z]%)
Current Section: Section [N] - [Name]
Next Task: [Task description]
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
Mark complete when: All planning files loaded, summary displayed
Step 3: Identify Current Phase
Action: Parse tasks file to determine current state
Detection Logic:
# Parse tasks.md or steps.md
tasks = extract_all_tasks(content)
sections = extract_sections(content)
# Find first uncompleted section
current_section = find_first_uncompleted_section(sections, tasks)
# Find next uncompleted task
next_task = find_next_uncompleted_task(tasks)
# Calculate progress
total_tasks = len(tasks)
completed_tasks = count_completed(tasks)
progress_pct = (completed_tasks / total_tasks) * 100
Display Current State:
š CURRENT STATE
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
Progress: [15/40 tasks] (37.5%)
ā
Section 1: Planning (Tasks 1-8) - COMPLETE
ā
Section 2: Setup (Tasks 9-12) - COMPLETE
š Section 3: Implementation (Tasks 13-28) - IN PROGRESS
āā Next: Task 15 - "Implement scoring logic"
āā Remaining: 14 tasks in this section
⬠Section 4: Testing (Tasks 29-35) - NOT STARTED
⬠Section 5: Deployment (Tasks 36-40) - NOT STARTED
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
Ask User:
Ready to continue Section 3: Implementation?
Options:
1. Continue from Task 15 (recommended)
2. Review completed work first
3. Jump to different section
4. Exit and save progress
Mark complete when: Current state identified and displayed
Step 4: Execute Work with Continuous Tracking
CRITICAL PATTERN: Section-based execution with automatic bulk-complete
For each section:
4A. Show Section Overview
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
SECTION 3: IMPLEMENTATION
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
Goal: [Section goal from tasks.md]
Tasks: 13-28 (16 tasks total)
Estimate: [Time estimate if available]
Uncompleted tasks in this section:
[ ] Task 15: Implement scoring logic
[ ] Task 16: Create validation rules
[ ] Task 17: Build API endpoints
... (show all uncompleted)
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
4B. Execute Tasks in Section
Starting Task 15: Implement scoring logic...
[Execute work]
[Show outputs, code, decisions]
ā
Task 15 complete!
Starting Task 16: Create validation rules...
Adaptive Granularity (see references/adaptive-granularity.md):
- Small sections (ā¤5 tasks): Execute all, then bulk-complete
- Large sections (>15 tasks): Checkpoint every 5-7 tasks
- Unstructured (no sections): Checkpoint every 10 tasks
4C. Section Completion Checkpoint
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
SECTION 3: IMPLEMENTATION - COMPLETE! š
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
Tasks completed in this section: 16/16
All work validated and ready to mark complete.
Ready to bulk-complete Section 3?
ā
Marks tasks 13-28 as [x] in steps.md
ā
Updates progress automatically
ā
Validates by re-reading file
Type 'yes' to proceed, or 'review' to check work first.
4D. Bulk-Complete Section
# User confirms ā Execute bulk-complete
python 00-system/skills/bulk-complete/scripts/bulk-complete.py \
--project [project-id] \
--section 3 \
--no-confirm
Validation Output:
[INFO] Using task file: steps.md
Project: 05-lead-qualification
Tasks: 12 uncompleted, 28 completed (Total: 40)
[MODE] Complete 12 uncompleted tasks in Section/Phase 3
[AUTO-CONFIRM] Proceeding without confirmation (--no-confirm flag)
[SUCCESS] Successfully completed 12 tasks!
Updated: 40/40 tasks now complete (100%)
ā
VALIDATED: Re-read file shows 0 uncompleted, 40 completed
File: 02-projects/05-lead-qualification/01-planning/steps.md
4E. Show Updated Progress
ā
Section 3 complete!
Updated Progress: 28/40 tasks (70%)
Remaining sections:
⬠Section 4: Testing (Tasks 29-35) - 7 tasks
⬠Section 5: Deployment (Tasks 36-40) - 5 tasks
Continue to Section 4, or pause for today?
Mark complete when: Section executed and bulk-completed with validation
Step 5: Incremental Progress Updates
After each section/checkpoint:
Display Progress Bar:
Progress: [āāāāāāāāāā] 80% (32/40 tasks)
Completed:
ā
Section 1: Planning (8 tasks)
ā
Section 2: Setup (4 tasks)
ā
Section 3: Implementation (16 tasks)
ā
Section 4: Testing (4 tasks)
Remaining:
⬠Section 5: Deployment (8 tasks)
Ask User:
Options:
1. Continue to Section 5: Deployment
2. Pause and save progress (will resume here next session)
3. Review completed work
4. Jump to different section
Mark complete when: Progress updated and user decides next step
Step 6: Handle Partial Completion
When user says "pause" or "done for today":
Offer Partial Task Completion:
Current progress: 25/40 tasks (62.5%)
Do you want to mark any completed tasks before pausing?
Options:
1. Bulk-complete specific tasks (e.g., "1-10,15-20")
2. Bulk-complete current section (Section 3)
3. No, save current state as-is
If user wants bulk-complete:
# Example: User completed tasks 20-25 but not full section
python 00-system/skills/bulk-complete/scripts/bulk-complete.py \
--project [project-id] \
--tasks 20-25 \
--no-confirm
Then trigger close-session:
Saving progress...
[Trigger close-session skill]
ā
Session saved!
ā
Progress: 25/40 tasks complete (62.5%)
ā
Next session will resume at: Section 3, Task 26
See you next time! š
Mark complete when: Partial completion handled, close-session triggered
Step 7: Project Completion
When all sections done:
Final Validation:
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
PROJECT COMPLETE! š
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
All sections executed:
ā
Section 1: Planning (8 tasks)
ā
Section 2: Setup (4 tasks)
ā
Section 3: Implementation (16 tasks)
ā
Section 4: Testing (7 tasks)
ā
Section 5: Deployment (5 tasks)
Total: 40/40 tasks (100%)
Ready to finalize project completion?
ā
Mark all tasks complete
ā
Update project status to COMPLETE
ā
Archive project
ā
Trigger close-session
Type 'yes' to proceed.
Execute Final Bulk-Complete:
# Complete any remaining tasks
python 00-system/skills/bulk-complete/scripts/bulk-complete.py \
--project [project-id] \
--all \
--no-confirm
Update Project Status:
# Update overview.md metadata
status: COMPLETE
last_worked: [today's date]
Trigger close-session:
ā
Project marked COMPLETE!
ā
All 40/40 tasks checked off
ā
Ready to archive (use 'archive-project' skill)
[Trigger close-session skill]
Congratulations on completing this project! š
Mark complete when: Project finalized, status updated, close-session triggered
Advanced Features
Adaptive Granularity
Auto-detects project size and adjusts tracking granularity:
# Small projects (ā¤15 tasks)
ā Task-by-task execution with real-time updates
# Medium projects (16-30 tasks, with sections)
ā Section-based execution with bulk-complete per section
# Large projects (>30 tasks, with sections)
ā Section-based with periodic checkpoints (every 5-7 tasks)
# Unstructured projects (no sections)
ā Checkpoint every 10 tasks
See: references/adaptive-granularity.md for complete logic
Mental Models Integration (Proactive Offering)
When to Offer: At key decision points during execution (section completion, risk assessment, design choices)
Pattern: AI runs select_mental_models.py, reviews output, and offers 2-3 relevant models to user
Mental Models Skill Integration:
The execute-project skill automatically references mental-models at decision points for:
- Risk analysis at section checkpoints
- Decision-making when multiple approaches exist
- Problem decomposition when stuck on complex tasks
- Systems thinking for dependency validation
Required Workflow:
- Run script to get available models:
python 00-system/mental-models/scripts/select_mental_models.py --format brief - Select 2-3 relevant models based on context
- Offer to user with brief descriptions
- Load individual model file only after user selects
Offering Pattern:
# At Section Completion Checkpoint
Section 3 complete! Before bulk-completing, I've reviewed the mental models catalog and recommend:
1. **Pre-Mortem** ā Imagine failure modes before implementation
Best for: High-stakes sections, risk mitigation
2. **Systems Thinking** ā Analyze interdependencies and feedback loops
Best for: Complex integrations, dependency validation
3. **Force Field Analysis** ā Identify driving vs restraining forces
Best for: Understanding obstacles and enablers
Which approach sounds most useful? Or continue without structured analysis?
[User picks option]
If user picks a model:
ā Read: 00-system/mental-models/models/diagnostic/pre-mortem.md
ā Apply model questions before bulk-completing section
Benefits:
- ā Proactive - AI runs script to identify relevant options
- ā User Choice - User picks which model (or none) to apply
- ā Contextual - Offered at decision points only
- ā Individual files - Each model has its own file with full details
- ā Efficient - Descriptions are brief (3-7 words) but descriptive
When to Skip Offering:
- ā Routine, straightforward sections (offer only at complex/risky points)
- ā User explicitly requests speed over depth
- ā Simple task execution (no major decisions)
See: mental-models framework for full catalog and offering guidance
Error Handling
Common Issues:
Issue: Tasks file not found
Solution: Validate project structure with validate-system skill
Issue: No uncompleted tasks
Solution: Display "All tasks complete!" and offer to mark project COMPLETE
Issue: Invalid task format (no checkboxes)
Solution: Show error with example format: - [ ] Task description
Issue: Bulk-complete script fails
Solution: Fallback to manual Edit tool, log error for debugging
See: references/error-handling.md for complete troubleshooting guide
Integration with Other Skills
create-project ā execute-project
Session 1: create-project
ā Creates planning files (overview, plan, steps)
ā Status: PLANNING
ā close-session saves state
Session 2: execute-project
ā Loads planning files
ā Executes work systematically
ā Status: IN_PROGRESS
ā Continuously updates tasks
ā close-session saves progress
execute-project ā close-session
execute-project (in progress)
ā User says "done for today"
ā execute-project offers partial bulk-complete
ā Triggers close-session
ā close-session:
- Reads current task state
- Creates session report
- Saves progress
- Prepares for next session
execute-project ā archive-project
execute-project (100% complete)
ā Marks project status: COMPLETE
ā Suggests: "Use 'archive-project' skill to move to archive"
ā User invokes archive-project
ā Project moved to 05-archived/
Success Criteria
This skill succeeds when:
- ā Project context loaded with all planning files
- ā Current phase/section identified correctly
- ā Work executed systematically (not ad-hoc)
- ā Tasks bulk-completed after each section
- ā Progress validated after every bulk-complete
- ā User sees continuous progress updates
- ā Partial completion handled gracefully (pause/resume)
- ā Project completion triggers final validation
- ā close-session auto-triggered at appropriate times
Example Interaction
User: "Continue project 05-lead-qualification"
AI:
Loading project 05-lead-qualification...
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
PROJECT: Lead Qualification Workflow
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
Status: IN_PROGRESS
Progress: 12/40 tasks complete (30%)
Current Section: Section 2 - Implementation
Next Task: Task 13 - "Implement scoring logic"
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
Ready to continue Section 2?
User: "Yes"
AI:
Starting Section 2: Implementation...
Task 13: Implement scoring logic
[Executes work, shows code]
ā
Task 13 complete!
Task 14: Create validation rules
[Executes work]
ā
Task 14 complete!
... (continues through Section 2)
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
SECTION 2: IMPLEMENTATION - COMPLETE! š
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
Tasks completed: 16/16
Ready to bulk-complete Section 2?
User: "Yes"
AI:
Running bulk-complete for Section 2...
[SUCCESS] Successfully completed 16 tasks!
ā
VALIDATED: 28/40 tasks now complete (70%)
Updated Progress: [āāāāāāāāāā] 70%
Continue to Section 3: Testing, or pause?
User: "Pause"
AI:
Saving progress...
[Triggers close-session]
ā
Session saved!
ā
Progress: 28/40 tasks (70%)
ā
Next session: Section 3, Task 29
See you next time! š
Reference Documents
For detailed implementation guidance:
- workflow.md - Complete 7-step workflow with examples
- task-tracking.md - Task parsing and bulk-complete logic
- adaptive-granularity.md - Smart detection for project size
- error-handling.md - Troubleshooting common issues
Version: 1.0
Created: 2025-01-22
Status: Production Ready
Author: Nexus-v3 System
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