Communitygithub.com

growth-log

Use after a complex task, failure, or when reviewing what was learned. Teaches how to write growth logs that extract reusable patterns — not diary entries.

growth-log 是什麼?

growth-log is a Claude Code agent skill that use after a complex task, failure, or when reviewing what was learned. Teaches how to write growth logs that extract reusable patterns — not diary entries.

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/growth-log

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

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:

TypeWhen to UseEmphasisExample Title
FailureSomething broke, needed debugging, or required reworkRoot Cause"Config inheritance ≠ behavior inheritance across sessions"
MethodologyA repeatable process emerged from the workContext / Pattern"PPT → open-book exam study guide: three-layer structure"
Pattern DiscoveryA reusable insight about tools, systems, or thinkingPattern section"PR description template: describe the gap, not the feature"
Capability ChangeA measurable skill improvementContext (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.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

相關技能