# Brain Reflect

> AI self-review - analyzes recent sessions, patterns, and proposes improvements. Use when the user says "reflect", "self-review", "analyze patterns", "how are we doing", "review sessions", "meta-analysis", or wants insight into recurring themes and AI performance.

- Skill: `cfircoo/brain-reflect` (Agent Skill)
- Install (CLI): `npx skillmds@latest add cfircoo/brain-reflect`
- Raw SKILL.md: https://api.skillmd.com/api/skills/cfircoo/brain-reflect/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: cfircoo (https://skillmd.com/u/cfircoo)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/cfircoo/brain-reflect

---


<objective>
Analyze recent session logs, corrections, and vault activity to identify recurring patterns, frequent mistakes, the user's working habits, and opportunities for rule improvements. Generate a reflection report with actionable proposals.
</objective>

<protocol>

## Step 1: Determine Time Range

If the user specified a time range (e.g., "7 days", "30 days", "this week"), use that.
If no range specified, default to the last 7 days.

Calculate the date range and identify which session logs and daily notes fall within it.

## Step 2: Gather Data

Read the following within the time range:

1. **Session logs** (`Machine/Session-Logs/`): All logs within the date range.
2. **Corrections** (`Machine/Memory/corrections.md`): All corrections, noting dates.
3. **Decisions** (`Machine/Memory/decisions.md`): Recent decisions.
4. **Daily notes** (`Human/Daily/`): Notes within the date range.
5. **Active rules** (`Machine/Rules/active-rules.md`): Current rules for comparison.
6. **Rule changelog** (`Machine/Rules/rule-changelog.md`): Recent rule changes.

## Step 3: Analyze Patterns

### Session Patterns
- **Frequency:** How many sessions in the period? Average per day?
- **Duration:** How long are sessions typically?
- **Topics:** What subjects come up most often?
- **Productivity:** What is the ratio of accomplishments to open items?

### Correction Patterns
- **Frequency:** How many corrections in the period?
- **Categories:** Group corrections by type (formatting, tone, process, content, etc.)
- **Repeats:** Are the same corrections being given multiple times? This indicates a rule that is not being followed or a missing rule.
- **Promotion rate:** How many corrections have been promoted to rules?

### Task Patterns
- **Completion rate:** Of tasks that appear in daily notes, how many get checked off?
- **Carryover frequency:** How often do tasks carry over between days?
- **Eat-the-frog items:** Tasks that have persisted the longest
- **Peak productivity:** Any patterns in when tasks get completed (time of day, day of week)?

### Decision Patterns
- **Volume:** How many decisions in the period?
- **Reversals:** Any decisions marked as `superseded`?
- **Decision domains:** What areas are decisions being made in?

### User Working Patterns
- **Session timing:** When does the user typically start sessions?
- **Focus areas:** What projects or topics get the most attention?
- **Workflow style:** Does the user prefer long deep sessions or short bursts?
- **Common requests:** What types of tasks does the user most often ask for?

## Step 4: Generate Proposals

Based on the analysis, propose specific improvements:

### Rule Proposals
- New rules derived from repeated corrections
- Rule modifications based on observed drift
- Rules to remove if they are consistently irrelevant

### Process Proposals
- Workflow improvements based on session patterns
- Suggestions for recurring tasks that could be templated
- Eat-the-frog interventions for chronically deferred tasks

### Memory Proposals
- Entities that should be added or updated
- Context cache improvements
- Stale information that should be cleaned up

## Step 5: Generate Reflection Report

Write the report to `Machine/reflection-{YYYY-MM-DD}.md`:

```markdown
---
date: YYYY-MM-DD
tags: [reflection, meta]
type: reflection
period: {start-date} to {end-date}
---

# Reflection: {start-date} to {end-date}

## Overview
- **Sessions:** {count} ({avg}/day)
- **Corrections:** {count}
- **Decisions:** {count}
- **Tasks completed:** {count}/{total} ({percent}%)

## Session Patterns
{analysis of session frequency, duration, topics}

## Recurring Themes
1. **{theme}** -- appeared in {N} sessions
   {description}

## Correction Analysis
- **Total corrections:** {N}
- **Repeated corrections:** {list of corrections given 2+ times}
- **Categories:** {breakdown}
- **Promoted to rules:** {N}/{total}

### Corrections Needing Rules
{corrections that keep recurring but have not been promoted to rules}

## User Working Patterns
{observations about the user's work style}

## Eat the Frog
{tasks that have been deferred repeatedly}

## Proposals

### Rule Changes
1. **Add rule:** "{rule}" -- based on {N} corrections about {topic}
2. **Modify rule:** "{rule}" -- current wording does not match practice
3. **Remove rule:** "{rule}" -- not applicable based on {N} sessions

### Process Improvements
1. {suggestion}

### Memory Cleanup
1. {suggestion}

## Meta
This reflection was generated by the reflect skill on {date}.
Previous reflection: {link to last reflection or "None found"}
```

## Step 6: Present and Discuss

Present the key findings and proposals to the user. Ask:

- "Should I apply any of these rule proposals to active-rules.md?"
- "Any of these observations surprise you or seem off?"
- "Want me to act on any of the process improvement suggestions?"

</protocol>

<rules>
- This is primarily a read-and-analyze operation. Do not modify rules without user approval.
- The reflection report file is always saved to `Machine/` (AI zone).
- Be honest about patterns, even uncomfortable ones (like task avoidance).
- Frame observations constructively -- identify the pattern and suggest a solution.
- If there are not enough session logs for meaningful analysis, say so and suggest a minimum data threshold (at least 5 sessions).
- Compare against previous reflection reports if they exist, to track improvement over time.
- Never fabricate patterns -- if the data does not support a conclusion, do not make one.
</rules>

<success_criteria>
- All session logs within the time range are analyzed
- Recurring correction patterns are identified
- User working patterns are observed
- Concrete rule improvement proposals are generated
- Reflection report is saved to Machine/
- Proposals are presented for user review, not auto-applied
- Comparison with previous reflections if available
</success_criteria>