惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

L
LangChain Blog
Security Latest
Security Latest
P
Proofpoint News Feed
GbyAI
GbyAI
PCI Perspectives
PCI Perspectives
博客园 - Franky
N
Netflix TechBlog - Medium
博客园_首页
WordPress大学
WordPress大学
K
Kaspersky official blog
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
Vercel News
Vercel News
T
Threatpost
The Hacker News
The Hacker News
H
Help Net Security
S
Securelist
Recent Announcements
Recent Announcements
腾讯CDC
T
Tailwind CSS Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
Engineering at Meta
Engineering at Meta
C
Cisco Blogs
V
V2EX
C
Check Point Blog
S
Schneier on Security
Cyberwarzone
Cyberwarzone
C
Cybersecurity and Infrastructure Security Agency CISA
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
B
Blog RSS Feed
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Jina AI
Jina AI
M
MIT News - Artificial intelligence
T
Threat Research - Cisco Blogs
博客园 - 叶小钗
A
Arctic Wolf
AWS News Blog
AWS News Blog
Latest news
Latest news
Martin Fowler
Martin Fowler
Recorded Future
Recorded Future
Last Week in AI
Last Week in AI
The GitHub Blog
The GitHub Blog
小众软件
小众软件
B
Blog
aimingoo的专栏
aimingoo的专栏
C
Cyber Attacks, Cyber Crime and Cyber Security
V
Visual Studio Blog
P
Palo Alto Networks Blog
Spread Privacy
Spread Privacy

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant Common SOC 2 Failures (Real World) Stop Vibe-Checking Your AI App: A Practical Guide to Evals How to Use SonarQube and SonarScanner Locally to Level Up Your Code Quality Your Next To-Do App Is Dead — I Replaced Mine with an OpenClaw AI Sign a Nostr event in 60 lines of Python using coincurve — no nostr-sdk, no nbxplorer, no rust toolchain ITGC Audit Explained Like You’re in Big 4 Patch Tuesday abril 2026: Microsoft parcha 163 vulnerabilidades y un zero-day en SharePoint Stop scraping everything: a better way to track competitor price changes Listing on MCPize + the Official MCP Registry while routing payments OUTSIDE the marketplace — how I kept 100% of my x402 revenue Building an AI-Powered Risk Intelligence System Using Serverless Architecture Why We Ripped Function Overloading Out of Our AI Toolchain Testing AI-Generated Code: How to Actually Know If It Works SaaS Churn Is Killing Your Business. Here Is What to Do About It (Without a Support Team) The Speed of AI Is No Longer Linear - And Self-Improving Models Are Why How to Implement RBAC for MCP Tools: A Practical Guide for Engineering Teams From Standard Quote to Persuasive Proposal: AI Automation for Arborists I built a CLI that scaffolds complete multi-tenant SaaS apps Axios CVE-2025–62718: The Silent SSRF Bug That Could Be Hiding in Your Node.js App Right Now The dashboard that ended our friendship Data Pipelines Explained Simply (and How to Build Them with Python) The Hidden Cost of AI Systems Nobody Talks About. undefined vs undeclared, and how typeof behaves Switching from file-based jobs to NATS/Kafka in Rust without changing code io_uring Adventures: Rust Servers That Love Syscalls Why Agentic AI is Killing the Traditional Database The POUR principles of web accessibility for developers and designers Quantum Neural Network 3D — A Deep Dive into Interactive WebGL Visualization How To Install Caveman In Codex On macOS And Windows Automation Pipeline Reliability: Why Your Workflow Breaks When Nobody Is Watching I Built an 'Open World' AI Coding Agent — It Works From ANY Folder From Freelancing to Product: A Tech Service Company's SaaS Transformation China's AI Giants: Adding Tencent Hunyuan & ByteDance Doubao to AI University (74 Providers) On the Vibe Coders and Their Lies clerk: Auto-Summarize Your Claude Code Sessions AI Weekly — 2026/04/10–04/17 | The Model Lockdown Is Here, but the Toolchain Is the Real Battleground AI 週報 — 2026/04/10–2026/04/17 模型封鎖潮來了,但工具鏈才是真戰場 Maybe this is how Open-Source apps are born... 🚀 Fine-Tune LLMs with LoRA and QLoRA: 2026 Guide tRPC v11 + Next.js App Router: End-to-End Type Safety Without the Boilerplate ShadCN UI in 2026: Why I Stopped Installing Component Libraries and Started Owning My Components SaaS Billing in React Server Components: Stripe + Supabase Without a Single `useEffect` Join our DEV Weekend Challenge — $1,000 in Prizes Across TEN winners! Submissions Due April 20 at 6:59 AM UTC. Implementing FSRS Spaced Repetition in Flutter + Supabase — Adding Memory Science to an AI Learning App "I Texted My Localhost From the Train — Claude Code Fixed the Bug Before I Got Home" I Built a Sales Prep AI and It Went Deeper Than Expected Design to Code #2: One JSON, Eleven Outputs Solving the 100M-Row Problem: A Summary Table Pattern for High-Volume Push Notification Logs Flutter Web With Wasm: What Actually Changes For Developers I Built 50 Royalty-Free Soundtracks for My Side Project in a Weekend Using AI Music Generation The Vibe Coding Security Checklist: 7 Things to Check Before You Ship Stop Letting Googlebot Guess Fix Your React App's SEO Right Desconstruindo o Streaming do LinkedIn: Como Criar um Engine de Extração de Vídeo de Alta Performance com HLS e FFmpeg (EDA Part-1) EDA (Exploratory Data Analysis) Explained With Real Life — Why Looking at Your Data Is the Most Important Step in Machine Learning Brand Relationship Management at Scale: Our 4-Touch Outreach System for 200+ Brands Why String.fromEnvironment() Might Return an Empty String in Dart JGuardrails 1.0.0 — Hardening Java LLM Apps Against Jailbreaks, Toxicity, and Prompt Injection Plan and Schedule a Full Week of Threads Content From One Claude Conversation Coding Cat Oran Ep3, Five Tables Changed Everything BFF模式详解:构建前后端协同的中间层 I'm done watching freelancers get buried by 200 proposals. So I'm building the alternative. This is my first post BFS Algorithm in Java Step by Step Tutorial with Examples Tracking LLM Pricing Monthly: An Open Dataset for 22 AI Models How We Measure Content ROI on a Comparison Site: Revenue Attribution Without Perfect Data Introducing Nova AI Ops: The AI-Native Operating System for SRE Teams I built a free desktop video downloader for Windows — Grabbit How Talkie OCR Helps Vision-Impaired & Dyslexic Users Read the World Around Them VRCFaceTracking安装和iPhone面捕配置教程,有bug Even CrowdStrike Can't See Your Agents The Automation Gold Rush: What n8n Workflows and Claude Are Opening Up for Developers Right Now
Generate Claude Code skills from your git history
Odilon HUGON · 2026-05-24 · via DEV Community

I could have created a generic "bug fix" skill. A template that asks for the symptom, expected behavior, what's already been tried. Useful. Generic. Forgettable.

Instead, I looked at my git log. On this project, 8 out of 30 commits touch the same Node.js subsystem. Always the same patterns: API timeout, slug regex, corrupted JSON file. The skill I created doesn't ask for the symptom — it directly reads the 3 files that explain 80% of failures, in order. That's the difference between a generic template and a custom skill.

The git log is the most honest documentation of what you actually do. Here's how to use it to generate skills that auto-trigger.

The audit: what your git log really reveals

Start with this command:


git log --oneline -50 | awk '{$1=""; print $0}' | sed 's/([^)]*)//' | sort | uniq -c | sort -rn | head -20

Enter fullscreen mode Exit fullscreen mode

What you see: the real frequency of each task type over the last 50 commits. On this portfolio:


8  fix(veille):       Node.js watch system — frequent bugs, complex architecture
5  feat(blog):        article creation — always the same file order
3  fix(blog):         post-publication fixes — typos, PHP syntax, slugs
2  refactor(veille):  refactors of the same subsystem
1  docs(publish):     workflow updates

Enter fullscreen mode Exit fullscreen mode

Three signals to look for:

  1. Frequency — what recurs often deserves a skill. A one-off task, no.
  2. Repeated scope — always the same files modified together → the skill knows where to look.
  3. Fixes in the same subsystem — multiple fix(X) → there are known failure points worth encoding.

Complete this with the most frequently touched files:


git log --oneline -30 --name-only | grep -v "^[a-f0-9]" | sort | uniq -c | sort -rn | head -15

Enter fullscreen mode Exit fullscreen mode

Git log isn't enough — conversation history matters too

The git log tells you what was done and how often. It doesn't tell you how it was asked for, or what caused friction in the interaction. That's often where the best skill content hides.

Conversation history reveals:

  • Clarifications Claude had to ask every session → missing information to encode in the skill
  • Corrections like "no not like that, like this" → constraints to make explicit
  • Repeated reformulations → exact keywords for trigger conditions
  • Things re-explained across sessions → what should be in the skill, not in your head

In practice, raw conversation text isn't stored. But three sources capture the essence:

1. Memory files (feedback)

Every saved correction is a direct signal. On this project, memory/feedback_article_workflow.md contains:


Don't go through brainstorming for articles — too much ceremony.
Write directly in order: FR → EN → posts.json → OG → php -l → commit → deploy.

Enter fullscreen mode Exit fullscreen mode

That's exactly the main constraint of the blog-article skill. It's not in the code — it's in the correction history. Without memory, this rule would need re-explaining every session.

2. Git log of CLAUDE.md itself


git log --oneline -- CLAUDE.md .claude/CLAUDE.md

Enter fullscreen mode Exit fullscreen mode

Every commit that modifies a CLAUDE.md is a trace of friction that forced a rule update — usually the result of a session where something went wrong. Those additions are direct candidates for skill content.

3. Workflow commits (docs, chore)

On this project: docs(publish): add article creation workflow to CLAUDE.publish.md. That commit exists because a previous session revealed a gap. Whatever was added that day is exactly what a skill should encode.

Combining both sources

Source

What it reveals

Useful for

Git log commits

Frequent tasks, files touched together, fragile subsystems

Identifying which skills to create

Memory/feedback

Past corrections, learned constraints, friction points

Skill content and constraints

Git log of CLAUDE.md

Rules added after friction

Non-obvious constraints to encode

Docs/chore commits

Documentation gaps revealed in session

Sequences and edge cases

A skill generated only from git log knows what to do. A skill generated from git log and correction history knows what to do, and how not to get it wrong.

Turning a pattern into a skill

A Claude Code skill is a markdown file in ~/.claude/plugins/<name>/skills/<name>/SKILL.md. The minimal structure:


---
name: skill-name
description: ">"
  [Trigger conditions — this is where everything happens]
---

[What Claude should do when the skill is triggered]

Enter fullscreen mode Exit fullscreen mode

The description isn't documentation — it's a detection pattern. Claude reads it every message to decide if the skill applies. It should answer: in exactly which situations is this skill relevant?

Vague description → false positives and false negatives


# ❌ Too vague
description: Use when there's a bug.

# ✅ Precise
description: >
  Use for any bug, error or unexpected behavior in the automated watch/veille system.
  Trigger on: "veille doesn't work", "job not running", "article not generated",
  any error in scripts/veille/ or logs/veille-daemon.log.
  Do NOT trigger for blog PHP bugs or deploy issues.

Enter fullscreen mode Exit fullscreen mode

The last line — "Do NOT trigger for" — is as important as the positive conditions. It prevents collisions between similar skills.

Concrete example: 3 skills generated from this project

The git log identified 3 clearly distinct patterns. Here are the corresponding skills.

Skill 1 — article creation (feat(blog) × 5)

Five commits, always the same file order: PHP FR → PHP EN → posts.json → OG image → php -l → commit → deploy. Miss one step and the deploy breaks.


---
name: blog-article
description: >
  Use when asked to create, write, draft or publish a blog article.
  Trigger on: "new article", "write about X", "publish on LinkedIn/dev.to",
  any mention of blog post creation or article workflow.
---

Mandatory execution order:
1. blog/posts/<slug>.php — complete FR version
2. blog/posts/<slug>.en.php — complete EN version
3. blog/posts.json — FR + EN entry, first position
4. npm run og <slug> — OG image
5. php -l on both files — syntax check
6. git commit + push
7. node scripts/publish-article.js <slug> — LinkedIn + dev.to + deploy

Never skip a step. Never commit without php -l.

Enter fullscreen mode Exit fullscreen mode

Skill 2 — veille debug (fix(veille) × 8)

Eight fix commits on the same subsystem. Known failure points, worth encoding directly.


---
name: veille-debug
description: >
  Use for any bug, error or unexpected behavior in the automated watch/veille system.
  Trigger on: veille errors, jobs not running, articles not generated, daemon issues,
  Claude API timeouts in watch context, slug/registry problems.
  Do NOT trigger for blog PHP bugs or LinkedIn/dev.to publishing issues.
---

Read in this order before any diagnosis:
1. scripts/veille/registry.json — configured jobs and their state
2. logs/veille-daemon.log — last execution (timestamp + errors)
3. scripts/veille/runner.js — general architecture

Known failure points (by frequency):
- Claude API timeout → increase timeout in the job config
- Slug regex too restrictive → test with node scripts/veille/test-slug.js
- Corrupt updates.json → delete the file, system recreates on next run
- Wrong cron working directory → check WorkingDirectory in systemd .service
- renderArticle() not writing → verify article.json exists with correct fields

Enter fullscreen mode Exit fullscreen mode

Skill 3 — post-publication fix (fix(blog) × 3)


---
name: blog-fix
description: >
  Use for small fixes on already-published blog articles: typos, grammar,
  PHP syntax errors, slug corrections, missing tags.
  Trigger after publication, not during creation.
  Do NOT trigger for new article creation.
---

Constraints for post-publication fixes:
- Never run scripts/deploy.sh (full deploy)
- Use bash scripts/deploy-files.sh <file1> <file2> (targeted deploy)
- php -l mandatory before any deploy
- If posts.json modified → include it in deploy-files.sh

Commit convention: fix(blog): <short description>

Enter fullscreen mode Exit fullscreen mode

What makes a skill auto-trigger well

After a few weeks of use, the patterns that work:

Short descriptions with concrete keywords. "Trigger on: veille errors, jobs not running" works better than "Use when there are problems with the automated system." Keywords should match what you naturally type.

One skill per context. If two skills can trigger on the same situation, Claude picks — and not always the right one. Better one skill with broader conditions than an overlap between two similar skills.

Encode non-obvious constraints. "Never run deploy.sh for a fix" is the kind of rule you relearn every time if it's not in the skill. That's exactly what skills should encode: decisions already made that you don't want to reconsider each time.

Test with variants. A skill that triggers on "article" but not on "blog post" or "LinkedIn post" is miscalibrated. List the natural formulations you actually use in the description.

Conclusion

The git log is the best starting point because it's honest. It shows what you actually do, not what you think you do. Frequent tasks, fragile subsystems, invariant sequences — it's all there.

The correction history — memory files, CLAUDE.md evolution, workflow commits — fills in what the git log can't show: how things were asked, what caused friction, what constraints were learned the hard way.

A skill generated from git log alone knows what to do. A skill generated from git log and correction history knows what to do, and how not to get it wrong. The difference is 30 minutes reading two sources instead of one.