Behavior Mapping
by mberto10
This skill should be used when the user asks to "set up tracking", "what should I track", "map behaviors to goals", "identify leading indicators", "which habits matter", "connect actions to outcomes", or needs to identify which daily behaviors produce their defined targets.
Skill Details
Repository Files
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name: Behavior Mapping description: This skill should be used when the user asks to "set up tracking", "what should I track", "map behaviors to goals", "identify leading indicators", "which habits matter", "connect actions to outcomes", or needs to identify which daily behaviors produce their defined targets. version: 1.0.0
Behavior Mapping
Map optimization targets to the daily behaviors that produce them.
Purpose
Targets are lagging indicators — they change slowly. This skill identifies the leading indicators (behaviors) that actually move the targets. Track behaviors daily, outcomes weekly/monthly.
Core Concept: Leading vs. Lagging
| Type | Definition | Track Frequency |
|---|---|---|
| Leading | Behaviors (inputs you control) | Daily |
| Lagging | Outcomes (results of behaviors) | Weekly/Monthly |
Key insight: You cannot directly control lagging indicators. Control leading indicators and trust the algorithm.
The Process
1. Identify the Algorithm
For each target, determine: What behaviors actually produce this outcome?
| Domain | Target | Known Algorithm (Behaviors) |
|---|---|---|
| Productivity | Deep work hours | Time blocking + environment design + energy management |
| Learning | Skill acquisition | Deliberate practice + spaced repetition + application |
| Finance | Savings rate | Automated transfers + spending awareness |
| Writing | Published output | Daily writing habit + editing process + shipping |
| Health | Body composition | Nutrition + resistance training + sleep |
| Coding | Features shipped | Focused blocks + reduced meetings + clear priorities |
Research evidence-based approaches. Do not guess.
2. Extract Trackable Behaviors
For each algorithm component, identify the minimum trackable unit:
| Target | Algorithm Component | Trackable Behavior |
|---|---|---|
| Deep work capacity | Time blocking | Deep work hours logged |
| Deep work capacity | Environment design | Distraction-free session? (Y/N) |
| Skill acquisition | Deliberate practice | Practice sessions completed |
| Skill acquisition | Spaced repetition | Anki reviews done? (Y/N) |
| Savings rate | Automated transfers | (Automated — no tracking needed) |
3. Apply Minimum Viable Tracking
Choose the simplest tracking that provides useful signal:
| Level | Type | Example | When to Use |
|---|---|---|---|
| 1 | Automated | Syncs from device/app | When possible |
| 2 | Boolean | Did I do it? Y/N | Default choice |
| 3 | Simple count | How many? | When quantity matters |
| 4 | Duration | How long? | When time matters |
| 5 | Detailed log | Full description | Only if truly necessary |
Start with boolean. Add detail only if needed for feedback.
4. Verify the Connection
For each behavior → target mapping, check:
- Is there evidence this behavior produces this outcome?
- How long until results expected? (Set expectations)
- What could interfere? (Confounding factors)
5. Output: Tracking Schema
domain: [domain]
target: [target name]
behaviors:
- name: [behavior name]
type: [boolean|count|duration|rating]
unit: [unit if applicable]
frequency: daily
target_connection: [direct|indirect]
expected_lag: [time to see results]
outcomes:
- name: [outcome name]
type: [aggregation|measurement]
source: [how calculated]
frequency: [weekly|monthly]
Database Schema
-- Behaviors table
CREATE TABLE behaviors (
id UUID PRIMARY KEY,
domain TEXT,
name TEXT,
type TEXT, -- boolean, count, duration, rating
frequency TEXT, -- daily, weekly
target_id UUID REFERENCES targets(id),
created_at TIMESTAMPTZ
);
-- Daily logs table
CREATE TABLE daily_logs (
id UUID PRIMARY KEY,
date DATE,
behavior_id UUID REFERENCES behaviors(id),
value JSONB, -- {completed: true} or {minutes: 90}
notes TEXT,
created_at TIMESTAMPTZ
);
Anti-Patterns
Do not track:
- Behaviors with no clear connection to targets
- Things that can be automated instead of logged
- Detailed data that will never be analyzed
- More than 5-7 daily inputs (friction kills compliance)
Do track:
- Minimum behaviors that produce targets
- At simplest level providing feedback
- Only what will actually be reviewed
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