EffortList AI (Organize Your Life /w Safety)

Manage EffortList AI folders, tasks, and todos. Use when the user wants to organize their life, track projects, or manage schedules via the EffortList AI pla...

📋 EffortList AI (Universal Skill)

🌟 Value Proposition (For Humans)

EffortList AI is a sophisticated life-management platform that merges advanced Generative AI with a robust, deterministic scheduling engine. Use this skill to give your agent full control over your project organization, time protection, and project lifecycles.

🚀 Setup & Authentication

1. Subscription: Requires a developer subscription ($5/month) at effortlist.io.
2. API Key: Human user must generate a Persistent API Key in Developer Settings.
3. Storage: Provide the key via the EFFORTLIST_API_KEY environment variable or OpenClaw internal config (openclaw config set skills.entries.effortlist-ai.env.EFFORTLIST_API_KEY "your_key").

📐 Mental Model (Data Hierarchy)

EffortList AI operates on a strictly nested hierarchy: Folder (Container) ──> Task (Project) ──> Todo (Actionable Slot)

• Folders: Optional top-level containers for grouping related projects.
• Tasks: Actionable projects that can be top-level or nested in a Folder.
• Todos: Granular actionable steps. Every Todo MUST have a parent Task.

🤖 Intelligence & Mapping (For Agents)

User Intent	Agent Workflow	Endpoint Goal
"Plan a project"	Create Folder -> Tasks -> Todos	POST /folders, POST /tasks, POST /todos
"Fix my mistake"	Fetch History -> Target ID -> Undo	GET /api/v1/undo, POST /api/v1/undo?id=...
"Show my day"	Fetch Todos by Date Range	GET /api/v1/todos?from=...&to=...
"Check settings"	Fetch User Profile & Schedule	GET /api/v1/me
"Surgical Edit"	Patch update a specific record	PATCH /api/v1/{type}?id=...
"Manage Links"	Create or update booking links	POST/PATCH /api/v1/availability/links
"Review Appts"	Accept or decline appointments	PATCH /api/v1/appointments/{id}

🛠️ Execution Logic (The "Omni" Way)

1. Surgical Extraction & Patching: Always prefer fetching a specific record by its ID (GET ?id=...) over broad list fetches. When updating, use PATCH with the record ?id=.
2. Phase-Aware Scheduling: Be mindful of the 5-phase Omni processing loop. Proactively flag events with isProtectedTime: true to trigger the server-side safety net. Use ignoreConflicts: true only when explicit user intent overrides overlap protection.
3. Appointment Awareness: Be extremely cautious when deleting or rescheduling items where isBooked: true. This triggers automatic guest notifications/cancellations. Confirm with the user before performing destructive actions on booked slots.
4. Efficiency & Throttling: Respect the 100 requests per minute rate limit. For bulk operations, batch requests appropriately and check X-RateLimit-Remaining headers.
5. Pagination: When listing folders, tasks, or todos, use limit and offset for large datasets.
6. Scheduling Alignment: Before blocking large segments of time or creating new recurring todos, use GET /api/v1/me to align with the user's weeklySchedule, timezone, and minimumNotice preferences.
7. Cascading Safety: Be aware that deleting a Folder or Task is an Atomic Purge. However, the engine protects items that are simultaneously being updated from accidental deletion.
8. Temporal Fidelity: When reporting event times to the user, strictly respect the user's timezone and local time offset (e.g., CDT vs. CST). Provide dates and times exactly as they appear in the local context or as explicitly requested, without performing unsolicited manual shifts. Use the /me endpoint to confirm the active offset before finalizing any scheduling summaries.
9. Global Availability Awareness: Before modifying booking links or schedules, use GET /api/v1/availability to retrieve the current weeklySchedule, timezone, and minimumNotice settings.
10. Undo/Redo Competency: If a destructive operation is performed in error, use the Undo stack (POST /api/v1/undo) to restore state.

🔒 Security & Privacy (Zero Trust)

• Data Isolation: Strict row-level security; users only see their own data.
• AI Privacy: Your personal data is never used to train models.

📖 Deep References

• Full API Reference: API DOCs
• Omni Architecture: (Located in references/architecture.md)
• Security Audit Docs: SECURITY