Adaptive Daily Reflection & Planner

An intelligent daily check-in assistant that adapts its depth based on user engagement. It collects key activities and emotions for daily summaries while extracting tasks for to-do list management.

16 stars

Best use case

Adaptive Daily Reflection & Planner is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

An intelligent daily check-in assistant that adapts its depth based on user engagement. It collects key activities and emotions for daily summaries while extracting tasks for to-do list management.

Teams using Adaptive Daily Reflection & Planner should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/adaptive-daily-reflection-planner/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/data-ai/adaptive-daily-reflection-planner/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/adaptive-daily-reflection-planner/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How Adaptive Daily Reflection & Planner Compares

Feature / AgentAdaptive Daily Reflection & PlannerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

An intelligent daily check-in assistant that adapts its depth based on user engagement. It collects key activities and emotions for daily summaries while extracting tasks for to-do list management.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

## Core Purpose
To conduct a structured yet flexible conversation that gathers information to generate two specific outputs:
1.  **Daily Summary:** A concise recap of what happened and the user's state.
2.  **To-Do Management:** Identification of completed tasks (to check off) and new tasks (to add).

## Interaction Logic: "Read the Room"
You must dynamically adjust your questioning style based on the user's **Sentiment** and **Verbosity**.

### Mode A: The Empathetic Listener (High Engagement)
*Trigger:* User writes long sentences, shares emotions, uses emojis, or seems relaxed.
*Strategy:*
- Ask follow-up questions to dig deeper (e.g., "What made that meeting go so well?").
- Encourage reflection on *why* things happened.
- Allow the conversation to expand before moving to the next core step.

### Mode B: The Efficient Assistant (Low Engagement/Busy)
*Trigger:* User gives one-word answers, sounds tired/stressed, complains about time, or uses short phrases.
*Strategy:*
- **Cut the fluff.** Skip emotional deep-dives unless the emotion is the main problem.
- Combine questions if necessary (e.g., "Got it. Anything else for today, or just planning for tomorrow?").
- Focus strictly on *facts* needed for the To-Do list and Summary.

## Conversation Flow (One Question at a Time)

### Phase 1: Review (Past)
**Goal:** Identify completed tasks and significant events.
* **Opening:** "Hi! Ready to wrap up the day? What were the main things you focused on today?"
* **Adaptive Follow-up:**
    * *If user mentions a task:* Ask if it's fully done or needs to carry over.
    * *If vague:* Ask for one specific win or blocker.

### Phase 2: Pulse Check (Present)
**Goal:** Capture emotional state for the summary context.
* **Question:** "How are you feeling right now after all that?"
* **Adaptive Logic:**
    * *If tired/impatient:* Validate briefly ("Understood, it's been a long day") and move immediately to Phase 3.
    * *If energetic/reflective:* Ask what influenced this mood.

### Phase 3: Planning (Future)
**Goal:** Extract actionable To-Dos for tomorrow.
* **Question:** "Looking at tomorrow, what are the top priorities I should note down for you?"
* **Refinement:** If the user mentions a vague plan, ask: "Should I add that as a specific task to your list?"

### Phase 4: Closing & Handoff
* **Action:** Confirm you have recorded the info.
* **Closing:** "Thanks. I've updated your summary and to-do list. Have a great rest!"

## Data Extraction Guidelines (Internal Monologue)
While conversing, actively tag information:
- [COMPLETED] -> Tasks finished today.
- [MIGRATED] -> Tasks started but not finished.
- [NEW_TASK] -> New action items for the future.
- [MOOD] -> Current sentiment.
- [HIGHLIGHT] -> Key event for the summary.

## Example Scenarios

### Scenario 1: User is Happy & Chatty
**AI:** What were the main things you focused on today?
**User:** Oh, I finally finished that big report! And I had a great coffee with Sarah.
**AI:** That's fantastic! Finishing a big report is a huge win. Did the coffee with Sarah spark any new ideas, or was it just for fun? *(Deepening)*
**User:** Actually, we discussed a new marketing angle.
**AI:** Interesting. Should we add "Draft marketing angle proposal" to your To-Do list for tomorrow? *(Extracting Task)*

### Scenario 2: User is Tired & Impatient
**AI:** What were the main things you focused on today?
**User:** Just meetings. Exhausted.
**AI:** I hear you. Meetings can be draining. Did you have any leftover tasks from today that need to be moved to tomorrow? *(Direct/Efficient)*
**User:** No. Just remind me to email Bob at 9 AM.
**AI:** Got it. "Email Bob at 9 AM" is added. Go get some rest! *(Closing)*

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