Agent Skills
Task Reminder
AIPOCH
Organize scattered tasks into actionable lists and generate daily/weekly/deadline reminder plans when you need a structured schedule and exportable outputs (MD/CSV), with optional system notifications.
2
0
FILES
90100Total Score
View Evaluation ReportCore Capability
83 / 100
Functional Suitability
11 / 12
Reliability
10 / 12
Performance & Context
8 / 8
Agent Usability
13 / 16
Human Usability
7 / 8
Security
9 / 12
Maintainability
9 / 12
Agent-Specific
16 / 20
Medical Task
20 / 20 Passed
99You have a scattered set of tasks and need them consolidated into an actionable, prioritized list
4/4
95You want a daily plan that tells you what to focus on each day within a date range
4/4
93Converts a raw task list into an actionable plan across a specified date range
4/4
93Supports reminder modes: daily, weekly, deadline, or all (default)
4/4
93End-to-end case for Converts a raw task list into an actionable plan across a specified date range
4/4
SKILL.md
Validation Shortcut
Run this minimal command first to verify the supported execution path:
python scripts/task_reminder.py --help
When to Use
- You have a scattered set of tasks and need them consolidated into an actionable, prioritized list.
- You want a daily plan that tells you what to focus on each day within a date range.
- You want a weekly reminder plan (e.g., every Monday) to review upcoming work.
- You need a deadline-driven plan that highlights tasks approaching due dates.
- You need to export reminders to Markdown/CSV for sharing, collaboration, or importing into other tools.
Key Features
- Converts a raw task list into an actionable plan across a specified date range.
- Supports reminder modes:
daily,weekly,deadline, orall(default). - Exports results to:
reminders.md(human-readable actionable list + plan)reminders.csv(tabular plan for spreadsheets/tools)
- Accepts interactive input or JSON input via CLI.
- Optional system notifications (disabled by default; requires explicit activation in the script/parameters if supported).
Dependencies
- Python 3.x (standard library only; no third-party packages)
Example Usage
1) Run with interactive input
python scripts/task_reminder.py
2) Run with JSON input (recommended for repeatable runs)
Create input.json:
{
"start_date": "2026-03-01",
"end_date": "2026-03-10",
"reminder_mode": "all",
"weekly_day": 0,
"tasks": [
{
"title": "Write lab report",
"deadline": "2026-03-05",
"priority": 3,
"estimate_hours": 2,
"tags": ["Course", "Lab"]
},
{
"title": "Prepare slides for meeting",
"deadline": "2026-03-08",
"priority": 2,
"estimate_hours": 1.5,
"tags": ["Work"]
}
]
}
Run:
python scripts/task_reminder.py --json input.json
Expected outputs in the working directory:
reminders.mdreminders.csv
Implementation Details
Input Schema
Minimum required fields
tasks: array of task objectsstart_date: string inYYYY-MM-DDend_date: string inYYYY-MM-DD
Optional fields
reminder_mode: one ofdaily/weekly/deadline/all(default:all)weekly_day: integer0..6where0=Mondayand6=Sunday(default:0)
Task object fields (recommended)
title(string): task namedeadline(string,YYYY-MM-DD): due date used for deadline-based reminderspriority(number/int): higher value indicates higher priority (as provided by the user)estimate_hours(number): effort estimate used for planning contexttags(array of strings): categorization for filtering/grouping in outputs
Reminder Modes
- daily: generates a day-by-day plan within
[start_date, end_date]. - weekly: generates reminders on the specified
weekly_daywithin the date range. - deadline: emphasizes tasks by approaching deadlines within the date range.
- all: produces combined outputs for daily/weekly/deadline views.
Output Files
reminders.md: includes an actionable task list and the generated reminder plan in Markdown format.reminders.csv: includes a structured reminder plan table suitable for spreadsheets and imports.
Security/Operational Constraints
- Runs as a local script with no network access.
- Writes only to the output files it generates (e.g.,
reminders.md,reminders.csv) in the specified/working directory. - System notifications are not enabled by default and require explicit activation if implemented.
When Not to Use
- Do not use this skill when the required source data, identifiers, files, or credentials are missing.
- Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
- Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.
Required Inputs
- A clearly specified task goal aligned with the documented scope.
- All required files, identifiers, parameters, or environment variables before execution.
- Any domain constraints, formatting requirements, and expected output destination if applicable.
Recommended Workflow
- Validate the request against the skill boundary and confirm all required inputs are present.
- Select the documented execution path and prefer the simplest supported command or procedure.
- Produce the expected output using the documented file format, schema, or narrative structure.
- Run a final validation pass for completeness, consistency, and safety before returning the result.
Output Contract
- Return a structured deliverable that is directly usable without reformatting.
- If a file is produced, prefer a deterministic output name such as
task_reminder_result.mdunless the skill documentation defines a better convention. - Include a short validation summary describing what was checked, what assumptions were made, and any remaining limitations.
Validation and Safety Rules
- Validate required inputs before execution and stop early when mandatory fields or files are missing.
- Do not fabricate measurements, references, findings, or conclusions that are not supported by the provided source material.
- Emit a clear warning when credentials, privacy constraints, safety boundaries, or unsupported requests affect the result.
- Keep the output safe, reproducible, and within the documented scope at all times.
Failure Handling
- If validation fails, explain the exact missing field, file, or parameter and show the minimum fix required.
- If an external dependency or script fails, surface the command path, likely cause, and the next recovery step.
- If partial output is returned, label it clearly and identify which checks could not be completed.
Quick Validation
Run this minimal verification path before full execution when possible:
python scripts/task_reminder.py --help
Expected output format:
Result file: task_reminder_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any
Deterministic Output Rules
- Use the same section order for every supported request of this skill.
- Keep output field names stable and do not rename documented keys across examples.
- If a value is unavailable, emit an explicit placeholder instead of omitting the field.
Completion Checklist
- Confirm all required inputs were present and valid.
- Confirm the supported execution path completed without unresolved errors.
- Confirm the final deliverable matches the documented format exactly.
- Confirm assumptions, limitations, and warnings are surfaced explicitly.