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affaan-m/growth-log

affaan-m

growth-log

Write growth log entries that extract reusable patterns from completed work — root cause, transferable rule, and a recognizable signal — instead of diary-style event narration, with a 4-8 sentence template and merge-duplicates discipline. Use when capturing what was learned after a complex task, debugging session, failure, or rollback, when reviewing progress over a period, or when a delivery gate asks what was learned.

NewUpdated Sep 27, 2026

Growth Log Skill

The problem: Most people write "fixed a bug in X" as a learning log. That's a diary entry, not a learning artifact. A real growth log extracts the pattern so you recognize it next time.

This skill teaches: How to write learning entries that compound across sessions. Works with any note-taking system — Markdown files, Notion, Obsidian, plain text. Templates are generic; adapt to your setup.

When to Activate

  • After completing a complex task (multi-file, new feature, architecture change)
  • After a failure, mistake, or "that was harder than expected" moment
  • When you want to review what you've learned over a period

When NOT to activate: Trivial changes (typo fixes, single-line tweaks, config value changes with no debugging). The threshold: did this task involve debugging, redoing, rollback, or a non-obvious decision? If yes → write an entry. If no → skip.

The Three Rules

Rule 1: Failures > Achievements

A failure is nutritionally denser than a success. One bug that took 2 hours to find teaches more than 3 features that worked first try.

Bad: "Successfully implemented the login flow." Good (web dev): "Login flow: session token wasn't persisting because the cookie SameSite defaulted to Lax in Chrome 128+. Pattern: always explicitly set SameSite=None; Secure when cross-origin. Signal to recognize: auth breaks after browser upgrade or when crossing origin boundaries." Good (data pipeline): "CSV import failed silently on empty rows because pandas.read_csv(dropna=False) keeps zero-width rows that len() counts as valid. Pattern: always df.dropna(how='all', inplace=True) before row-count validation."

Rule 2: The Bole Principle (伯乐原则)

Before writing a new entry, ask: "Is this fundamentally the same as something I already recorded?"

Same root cause, different symptom → merge, don't duplicate. New root cause → new entry.

How to check: Search existing entries for keywords from your root cause before writing. If you find a match, add your new symptom as an additional example under the existing entry rather than creating a duplicate.

Example: "Forgot to update the output index after creating a file" and "Forgot to update skill ratings after a task" — same root cause (no automatic capture trigger). Merge into one entry about "post-task capture gaps."

Rule 3: Must Be Transferable

Every entry must answer: "Next time I face a similar situation, what do I do differently?"

If you can't write that sentence, you haven't extracted the pattern yet.

How to extract a pattern from a concrete event:

  1. State what happened in one sentence
  2. Ask "why?" iteratively until you reach root cause (usually 3-5 whys)
  3. Generalize: "What class of problem is this?" (not "Chrome 128 bug" but "browser default change breaking existing behavior")
  4. Formulate as: "Next time I see [signal], I will [action]."
  5. Name the signal: what specific observable tells you this pattern is active?

Entry Template

Scope: One entry per distinct root cause. Typical length: 4-8 sentences. If it takes >2 minutes to write, you're narrating events. If <30 seconds, you haven't gone deep enough.

## [Title: the pattern, not the event]

### Context
- What was I trying to do?
- What went wrong / what worked surprisingly well?

### Root Cause / Core Insight
- The underlying mechanism, not just the symptom

### The Pattern (transferable)
- Next time [similar situation], I will [specific action].
- Signal to recognize: [what observable tells me this pattern is active?]

### Related
- [entry-name](../path/to/related-entry.md)

Entry Types

All four types use the template above. The type determines which sections carry the most weight:

Type When to Use Emphasis Example Title
Failure Something broke, needed debugging, or required rework Root Cause "Config inheritance ≠ behavior inheritance across sessions"
Methodology A repeatable process emerged from the work Context / Pattern "PPT → open-book exam study guide: three-layer structure"
Pattern Discovery A reusable insight about tools, systems, or thinking Pattern section "PR description template: describe the gap, not the feature"
Capability Change A measurable skill improvement Context (before vs after) "Git: from clone/push to independent PR with 12 commits"

Quality Checklist

Before finalizing a growth log entry:

  • Does the title name the pattern, not the event?
  • Is there a "Next time I will..." sentence?
  • Is the "Signal to recognize" specific enough to trigger the pattern next time?
  • Did I search existing entries for duplicates before writing? (Bole Principle)
  • Is the root cause distinguished from the symptom?
  • Are related memories cross-linked?
  • Is the entry 4-8 sentences? Shorter = too shallow; longer = narrating events.

Anti-Patterns

  • Avoid: "Fixed bug in payment module" (event, not pattern)
  • Avoid: Copying the git commit message verbatim (commits describe what changed; logs extract why it matters)
  • Avoid: Writing an entry for every commit (only when a pattern emerges)
  • Avoid: Skipping the transferable sentence (without it, it's just a diary — this is non-negotiable)
  • Avoid: Duplicating the same pattern under different titles (violates Bole Principle — search before writing)

Storage

Store entries wherever you keep notes. Common patterns:

  • Markdown files in a growth-log/ directory (one file per day: YYYY-MM-DD.md)
  • A dedicated section in Notion, Obsidian, or your note-taking app
  • Plain text files with a consistent naming convention

Pick one convention and stick to it. Searchability matters more than format.

If You Use Delivery Gate

The delivery-gate Stop hook checks that learning files were modified today via filesystem timestamps. This skill teaches what to write — so the file that delivery-gate checks actually contains useful patterns, not empty timestamps.

Task completes → delivery-gate checks: was the learning file touched today?
  → Stale (no file modified): block — "what did you learn?"
  → Fresh (file touched): pass — this skill ensures the content is useful

Having enforcement without methodology → empty entries. Having methodology without enforcement → forgotten captures. Each is independently useful; together they close the loop.

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Overall Score

89/100

Grade

A

Excellent

Grades are signals, not a certification. Always review a skill yourself before use.

Safety

95

Quality

87

Clarity

91

Completeness

83

Summary

A methodology skill that teaches developers how to write structured growth log entries—extracting reusable patterns, root causes, and recognizable signals from completed work, failures, and debugging sessions. Rather than diary-style event narration, it enforces a 4-8 sentence template with three core principles (prioritize failures, eliminate duplicates via the Bole Principle, ensure transferability) and provides four entry types (Failure, Methodology, Pattern Discovery, Capability Change). The skill is storage-agnostic and integrates with delivery-gate checkpoints.

Detected Capabilities

documentation authoringpattern extractiondeduplication logic (conceptual)file organization guidancenote-taking integration

Trigger Keywords

Phrases that agents use to match this skill to user intent.

document learning from failurescapture debugging patternswrite growth log entryextract reusable insightstrack skill improvementsbuild knowledge base

Use Cases

  • Document and internalize lessons after complex debugging sessions or failures
  • Build a searchable, deduplicated knowledge base of reusable patterns across projects
  • Capture process improvements and methodology insights that emerge from real work
  • Satisfy delivery gate requirements with substantive learning artifacts instead of empty timestamps
  • Track measurable skill growth and capability improvements over time
  • Prevent recurring mistakes by formalizing pattern recognition and transferable actions

Quality Notes

  • Clear structural hierarchy with well-scoped sections and descriptive headings
  • Three core rules are memorable, actionable, and supported by concrete examples
  • Template is prescriptive yet flexible—works across storage systems (Markdown, Notion, Obsidian, plain text)
  • Entry types table provides practical guidance for different learning contexts
  • Quality checklist empowers users to self-assess and iterate before finalizing
  • Excellent use of contrasting examples (Bad vs. Good) for both web dev and data pipeline domains
  • Bole Principle anti-duplication policy is explained clearly with a concrete merging example
  • Integration note with delivery-gate closes a common loop between methodology and enforcement
  • The '4-8 sentence' constraint prevents both shallow entries and event narration
  • Anti-patterns section explicitly addresses common failure modes
  • Guidance on when NOT to activate is well-defined and prevents low-value noise entries
Model: claude-haiku-4-5-20251001Analyzed: Sep 27, 2026

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Version History

  1. v2.0

    Contract changed: description

    ✦ AIExpands activation trigger from completion review to include debugging, failures, rollbacks, progress review, and delivery gates; adds 4-8 sentence template structure and merge-duplicates discipline.

    triggering2026-09-27

    LATEST
  2. v1.1

    Content updated

    ✦ AINo detectable behavioral changes between versions.

    2026-09-09

    View This Version
  3. v1.0

    2026-06-30

    View This VersionInitial version

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