From daily-metrics
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.
npx claudepluginhub mberto10/mberto-compoundThis skill uses the workspace's default tool permissions.
Map optimization targets to the daily behaviors that produce them.
Searches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.
Searches prompts.chat for AI prompt templates by keyword or category, retrieves by ID with variable handling, and improves prompts via AI. Use for discovering or enhancing prompts.
Guides MCP server integration in Claude Code plugins via .mcp.json or plugin.json configs for stdio, SSE, HTTP types, enabling external services as tools.
Map optimization targets to the daily behaviors that produce them.
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.
| 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.
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.
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) |
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.
For each behavior → target mapping, check:
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]
-- 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
);
Do not track:
Do track: