# Send AI Coding Toolkit to your agent
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
## Fast path
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
## Suggested prompts
### New install

```text
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
```
### Upgrade existing

```text
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
```
## Machine-readable fields
```json
{
  "schemaVersion": "1.0",
  "item": {
    "slug": "afrexai-ai-coding-toolkit",
    "name": "AI Coding Toolkit",
    "source": "tencent",
    "type": "skill",
    "category": "开发工具",
    "sourceUrl": "https://clawhub.ai/1kalin/afrexai-ai-coding-toolkit",
    "canonicalUrl": "https://clawhub.ai/1kalin/afrexai-ai-coding-toolkit",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadUrl": "/downloads/afrexai-ai-coding-toolkit",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-ai-coding-toolkit",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "packageFormat": "ZIP package",
    "primaryDoc": "SKILL.md",
    "includedAssets": [
      "README.md",
      "SKILL.md"
    ],
    "downloadMode": "redirect",
    "sourceHealth": {
      "source": "tencent",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-04-23T16:43:11.935Z",
      "expiresAt": "2026-04-30T16:43:11.935Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
        "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"",
        "redirectLocation": null,
        "bodySnippet": null
      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/afrexai-ai-coding-toolkit"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    }
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/afrexai-ai-coding-toolkit",
    "downloadUrl": "https://openagent3.xyz/downloads/afrexai-ai-coding-toolkit",
    "agentUrl": "https://openagent3.xyz/skills/afrexai-ai-coding-toolkit/agent",
    "manifestUrl": "https://openagent3.xyz/skills/afrexai-ai-coding-toolkit/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/afrexai-ai-coding-toolkit/agent.md"
  }
}
```
## Documentation

### AI Coding Toolkit — Master Every AI Coding Assistant

The complete methodology for 10X productivity with AI-assisted development. Covers Cursor, Windsurf, Cline, Aider, Claude Code, GitHub Copilot, and more — tool-agnostic principles that work everywhere.

### Phase 1: Quick Assessment — Where Are You?

Rate yourself 1-5 on each:

Dimension1 (Beginner)5 (Expert)Prompt quality"Fix this bug"Structured context + constraints + examplesContext managementPaste entire filesCurated context windows, .cursorrules, AGENTS.mdWorkflow integrationAd-hoc usageSystematic agent-first developmentOutput verificationAccept everythingReview, test, iterate before committingTool selectionOne tool for everythingRight tool for right task

Score interpretation:

5-10: Read everything — you'll 10X your output
11-18: Skip to Phase 4+ for advanced techniques
19-25: Focus on Phase 8-10 for mastery patterns

### Decision Guide: Which AI Coding Tool When?

ToolBest ForContext WindowAutonomy LevelCostGitHub CopilotLine/function completion, inline suggestionsCurrent file + neighborsLow (autocomplete)$10-19/moCursorFull-file editing, multi-file refactors, chatProject-aware (indexing)Medium (tab/chat/composer)$20/moWindsurf (Cascade)Autonomous multi-step tasks, flowsProject-aware + flowsHigh (agentic flows)$15/moClineVS Code extension, model-agnostic, transparentManual context + autoHigh (tool use, browser)API costsAiderTerminal-based, git-native, pair programmingRepo map + selected filesMedium-High (git commits)API costsClaude CodeCLI agent, complex multi-file tasksWorkspace-awareHigh (full agent)API costsOpenClawPersistent agent, cron, multi-surfaceWorkspace + memory + toolsVery High (autonomous)API costs

### Selection Decision Tree

Need autocomplete while typing?
  → GitHub Copilot (layer it with any other tool)

Working in VS Code/IDE?
  ├─ Want integrated editor experience? → Cursor or Windsurf
  ├─ Want model flexibility + transparency? → Cline
  └─ Want minimal config, just works? → Cursor

Working in terminal?
  ├─ Want git-native pair programming? → Aider
  ├─ Want full agent with tools? → Claude Code
  └─ Want persistent autonomous agent? → OpenClaw

Building complex multi-file features?
  → Cursor Composer or Windsurf Cascade or Claude Code

Need autonomous background work?
  → OpenClaw (cron, heartbeats, multi-session)

### Recommended Stack (Layer These)

Solo developer:

GitHub Copilot (always-on autocomplete)
Cursor OR Windsurf (primary IDE)
Claude Code OR Aider (terminal agent for complex tasks)

Team:

GitHub Copilot (org-wide)
Cursor (primary IDE, .cursorrules in repo)
CI/CD AI review (automated PR review)

### Phase 3: Context Engineering — The #1 Skill

Context is everything. The quality of AI output is directly proportional to the quality of context you provide.

### The Context Hierarchy (Most → Least Important)

System instructions (.cursorrules, AGENTS.md, CLAUDE.md, .windsurfrules)
Explicit context (files you @mention or add to chat)
Implicit context (open tabs, recent edits, project index)
Model knowledge (training data — least reliable for your codebase)

### Project Rules File Template

Create at project root. Name depends on tool:

Cursor: .cursorrules
Windsurf: .windsurfrules
Claude Code: CLAUDE.md
Aider: .aider.conf.yml + convention docs
OpenClaw: AGENTS.md

# [PROJECT] — AI Coding Context

## Project Overview
- Name: [project name]
- Stack: [e.g., Next.js 14 + TypeScript + Tailwind + Drizzle + PostgreSQL]
- Architecture: [e.g., App Router, server components by default]
- Monorepo: [yes/no, structure if yes]

## Code Standards (ENFORCE STRICTLY)
- TypeScript strict mode (\`tsc --noEmit --strict\`)
- Max 50 lines per function, 300 lines per file
- One responsibility per file
- Naming: camelCase functions, PascalCase types, SCREAMING_SNAKE constants
- Imports: named imports, no default exports
- Error handling: explicit try/catch, typed errors, no silent catches

## Patterns to Follow
- [Pattern 1 with example]
- [Pattern 2 with example]
- [Pattern 3 with example]

## Anti-Patterns (NEVER DO)
- [Anti-pattern 1]
- [Anti-pattern 2]
- [Anti-pattern 3]

## File Structure

src/
components/     # React components
lib/            # Shared utilities
server/         # Server-only code
db/             # Database schema + queries
types/          # Shared TypeScript types

## Testing
- Framework: [vitest/jest/pytest]
- Pattern: AAA (Arrange, Act, Assert)
- Naming: \`should [expected behavior] when [condition]\`
- Coverage target: [80%+]

## Dependencies
- Approved: [list]
- Banned: [list with reasons]

## Common Commands
- \`npm run dev\` — start dev server
- \`npm run test\` — run tests
- \`npm run lint\` — lint + typecheck
- \`npm run build\` — production build

### Context Window Management

The 80/20 Rule: 80% of your context should be the specific files/functions relevant to the task. 20% is project conventions and standards.

Context Compression Techniques:

Summarize, don't dump — Instead of pasting a 500-line file, describe what it does and paste only the relevant section
Use @mentions — @file.ts instead of copy-paste (tool-specific)
Create reference docs — One-page architecture summaries the AI can reference
Prune conversation — Start new chats for new tasks; stale context = hallucinations
Tree command — Give the AI your project structure: tree -I node_modules -L 3

### The Context Refresh Rule

Every 5-10 messages, check: Is the AI still tracking correctly?
If it starts hallucinating file names, functions, or making wrong assumptions — start a new chat with fresh context.
Context is milk. It spoils.

### The SPEC Framework (Structure, Precision, Examples, Constraints)

Bad prompt:

Fix the login bug

Good prompt (SPEC):

## Structure
Fix the authentication flow in \`src/auth/login.ts\`

## Precision
- The login function throws "user not found" even when the user exists
- Error occurs on line 42 when querying by email (case-sensitive match)
- PostgreSQL query uses exact match but emails are stored lowercase

## Examples
- Input: "User@Example.com" → should match "user@example.com" in DB
- Current behavior: returns null
- Expected: returns user record

## Constraints
- Don't change the database schema
- Use the existing \`normalizeEmail()\` utility from \`src/utils/email.ts\`
- Add a test case for case-insensitive lookup
- Keep the existing error handling pattern (throw AppError)

### Prompt Templates by Task Type

Feature Implementation:

Implement [feature] in [file/location].

Requirements:
1. [Requirement with acceptance criteria]
2. [Requirement with acceptance criteria]
3. [Requirement with acceptance criteria]

Constraints:
- Follow existing patterns in [reference file]
- Use [specific library/approach]
- Include error handling for [edge cases]
- Write tests in [test file location]

Reference: Here's how similar feature [X] was implemented:
[paste relevant code snippet]

Bug Fix:

Bug: [description]
File: [path]
Steps to reproduce: [1, 2, 3]
Expected: [behavior]
Actual: [behavior]
Error: [paste error message/stack trace]

Fix constraints:
- Don't change [protected areas]
- Add regression test
- Explain root cause before fixing

Refactoring:

Refactor [file/module] to [goal].

Current state: [describe current architecture]
Target state: [describe desired architecture]
Motivation: [why — performance, readability, maintainability]

Rules:
- Preserve all existing behavior (no functional changes)
- Keep all existing tests passing
- Break into small, reviewable commits
- Each commit should be independently deployable

Code Review:

Review this code for:
1. Correctness — logic errors, edge cases, race conditions
2. Security — injection, auth bypass, data exposure
3. Performance — N+1 queries, unnecessary allocations, missing indexes
4. Maintainability — naming, complexity, test coverage

Be specific: quote the line, explain the issue, suggest the fix.
Skip style/formatting — linter handles that.

[paste code]

### Pattern 1: Test-Driven AI Development (TDD-AI)

1. Write the test first (yourself or with AI help)
2. Ask AI to implement the code that passes the test
3. Run tests — verify green
4. Ask AI to refactor while keeping tests green
5. Review the final code yourself

Why this works: Tests are specifications. The AI writes better code when it has a concrete target. You catch hallucinations immediately.

### Pattern 2: Scaffold → Fill → Review

1. Ask AI to scaffold the architecture (file structure, interfaces, types)
2. Review and approve the scaffold
3. Ask AI to fill in implementation file by file
4. Review each file individually
5. Integration test the full feature

Why this works: You maintain architectural control. The AI handles the grunt work. Errors are caught at each layer.

### Pattern 3: Conversation Threading

Chat 1: Architecture discussion → decisions documented
Chat 2: Implementation of Component A (reference architecture doc)
Chat 3: Implementation of Component B (reference architecture doc)
Chat 4: Integration + testing

Why this works: Fresh context per component prevents drift. Architecture doc provides continuity.

### Pattern 4: AI Pair Programming (Aider/Claude Code)

1. Start session with repo context
2. Describe the task in natural language
3. AI proposes changes as git diffs
4. Review each diff before accepting
5. AI commits with meaningful messages
6. You handle edge cases and integration

### Pattern 5: Autonomous Agent Workflow (OpenClaw/Claude Code)

1. Define task in structured format (acceptance criteria, constraints)
2. Agent plans → executes → verifies (reads files, runs tests)
3. Agent creates PR/branch with changes
4. You review the complete changeset
5. Iterate on feedback

### Cursor

FeaturePower MoveTab completionLet it complete 3-5 tokens before accepting — catches wrong predictions earlyCmd+K (inline edit)Select ONLY the exact lines to change — less context = more accurateChat@file to add context, @codebase for project-wide questionsComposerMulti-file changes — describe the full feature, let it edit across files.cursorrulesProject-specific AI instructions — commit to repo for team alignmentNotepadsReusable context (API docs, design docs) — attach to any chat

Cursor Pro Tips:

Use @git to reference recent changes
Use @docs to reference official library documentation
Create .cursor/rules/ directory for multiple rule files by domain
"Apply" button to accept chat suggestions directly into code

### Windsurf (Cascade)

FeaturePower MoveCascade flowsMulti-step autonomous tasks — it can read, write, run terminalWrite modeDirect file editing with AIChat modeDiscussion without editing.windsurfrulesProject context fileTurbo modeFaster, less accurate — good for simple tasks

Windsurf Pro Tips:

Cascade excels at multi-file refactors — give it the full scope
Use "undo flow" to revert entire multi-step changes
Pin important files in context
Let it read error output from terminal to self-fix

### Cline

FeaturePower MoveModel selectionSwitch models per task (cheap for simple, expensive for complex)Tool useReads files, runs commands, opens browser — full agentTransparencyShows every action before executing — audit everythingCustom instructionsPer-project system promptsAuto-approveConfigure which actions need approval

Cline Pro Tips:

Set spending limits to prevent runaway API costs
Use cheaper models (Haiku/GPT-4o-mini) for simple tasks
Enable "diff mode" to see exact changes before applying
Create task-specific instruction files

### Aider

FeaturePower Move/add filesExplicitly control which files the AI can see/edit/read filesRead-only context (reference files)/architectTwo-model approach — architect plans, editor implementsRepo mapAuto-generates codebase summary for contextGit integrationEvery change is a commit — easy rollback

Aider Pro Tips:

Use --architect flag for complex features (planner + implementer)
/drop files you don't need to free context window
--map-tokens to control repo map size
Run aider --model claude-sonnet-4-20250514 for best code quality

### Claude Code

FeaturePower MoveFull agentReads files, writes code, runs tests, git operationsCLAUDE.mdProject instructions file — auto-loadedSub-agentsSpawn parallel workers for complex tasksMemoryPersistent across sessions (project-level)

Claude Code Pro Tips:

Write a comprehensive CLAUDE.md — it's your biggest leverage
Use "plan mode" first for complex tasks, then "implement"
Let it run tests and self-correct — don't interrupt the loop
Use /compact when context gets long

### The Trust-But-Verify Checklist

After every AI-generated change:

Read every line — don't blindly accept. AI hallucinates plausible-looking code
 Check imports — AI often imports non-existent modules or wrong versions
 Verify function signatures — parameter names, types, return types
 Test edge cases — AI optimizes for the happy path
 Check for security — hardcoded secrets, missing auth checks, SQL injection
 Run the tests — if tests pass, good. If no tests exist, write them first
 Check for drift — did it change files you didn't ask it to change?
 Verify dependencies — did it add packages? Are they real? Are they secure?

### Common AI Code Failures

FailureDetectionFixHallucinated APICode uses functions that don't existCheck library docs before acceptingOutdated patternsUses deprecated APIs (React class components)Specify versions in contextMissing error handlingHappy path only, no try/catchAsk specifically for error casesSecurity holesInline secrets, missing auth, XSSSecurity review as separate stepOver-engineering5 files for a 20-line solutionAsk for simplest possible solutionWrong abstractionsPremature generalizationSpecify "don't abstract, keep concrete"Test theaterTests that pass but test nothingReview test assertions specificallyCopy-paste bugsDuplicated logic with subtle differencesCheck for patterns, extract helpers

### The 3-Read Review

Skim read — Does the structure make sense? Right files, right approach?
Logic read — Does each function do what it claims? Edge cases handled?
Integration read — Does it work with the rest of the codebase? Breaking changes?

### Token Cost Awareness

ModelInput $/1M tokensOutput $/1M tokensBest ForGPT-4o mini$0.15$0.60Simple completions, formattingClaude Haiku$0.25$1.25Quick edits, simple questionsGPT-4o$2.50$10.00Complex code generationClaude Sonnet$3.00$15.00Complex code, long contextClaude Opus$15.00$75.00Architecture, hardest problemso3$10.00$40.00Complex reasoning, algorithms

### Cost Reduction Strategies

Tier your usage — Simple tasks → cheap model. Complex → expensive model
Reduce context — Every unnecessary file in context costs money
Start new chats — Long conversations accumulate expensive history
Use autocomplete for simple stuff — Copilot is flat-rate, much cheaper per completion
Cache project context — Use rules files instead of re-explaining every chat
Batch related tasks — Handle related changes in one conversation

### Monthly Cost Benchmarks (Full-Time Developer)

Usage LevelEstimated Monthly CostLight (Copilot + occasional chat)$20-40Medium (Cursor Pro + daily chat)$40-80Heavy (API-based agents, complex tasks)$80-200Power user (autonomous agents, all day)$200-500+

### Rolling Out AI Coding Tools to a Team

Week 1-2: Foundation

Choose primary tool (Cursor or Windsurf recommended for teams)
Create .cursorrules / .windsurfrules committed to repo
Run a 1-hour workshop: basics, prompt techniques, verification
Set team guidelines (review requirements, security rules)

Week 3-4: Practice

Daily 15-min "AI wins" standup share
Pair sessions: experienced + new user
Collect common prompts into team prompt library
Monitor and address concerns (quality, dependency)

Month 2: Optimization

Measure: time-to-PR, bugs-per-feature, developer satisfaction
Iterate on .cursorrules based on team feedback
Create task-specific prompt templates in shared docs
Address skill gaps: who's using it well, who needs help?

Month 3: Systemization

AI-assisted PR review as CI step
Automated test generation for new features
Custom slash commands / snippets for team workflows
Quarterly review: ROI, quality metrics, tooling updates

### Team Guidelines Template

# AI Coding Guidelines — [Team Name]

## Approved Tools
- [Tool 1] for [use case]
- [Tool 2] for [use case]

## Rules
1. AI-generated code gets the SAME review rigor as human code
2. Never paste proprietary/customer data into AI tools without approved data handling
3. All AI-generated tests must be reviewed for assertion quality
4. Security-sensitive code (auth, payments, PII) requires human-first approach
5. Commit messages should NOT mention AI — own the code you commit

## Quality Gates
- [ ] Typecheck passes (\`tsc --noEmit --strict\`)
- [ ] All tests pass
- [ ] No new warnings
- [ ] Manual review of all AI-generated code
- [ ] Security-sensitive areas reviewed by security champion

### Multi-Agent Architecture for Development

Task: Build feature X

Agent 1 (Architect): Plans the approach, defines interfaces
Agent 2 (Implementer): Writes the code
Agent 3 (Tester): Writes and runs tests
Agent 4 (Reviewer): Reviews for quality, security, patterns

Orchestrator: Coordinates, resolves conflicts, maintains context

### Self-Healing Development Loop

1. Agent writes code
2. Agent runs tests
3. Tests fail → agent reads error, fixes code
4. Repeat until tests pass
5. Agent runs linter
6. Lint fails → agent fixes
7. All green → create PR

### The Prompt Library Pattern

Maintain a prompts/ directory in your project:

prompts/
  feature-implementation.md
  bug-fix.md
  refactoring.md
  code-review.md
  test-generation.md
  migration.md
  documentation.md

Each file is a reusable prompt template. Reference them: "Follow the template in prompts/feature-implementation.md"

### Model Routing Strategy

task_routing:
  autocomplete: copilot  # Always-on, flat rate
  simple_edit: haiku     # Quick, cheap
  feature_impl: sonnet   # Good balance
  architecture: opus     # When it matters
  debugging: sonnet      # Needs to reason about code
  documentation: haiku   # Simple transformation
  security_review: opus  # Can't afford mistakes
  test_generation: sonnet # Needs understanding of code logic

### Phase 11: Anti-Patterns — What NOT to Do

Anti-PatternWhy It FailsDo This InsteadPrompt and prayNo verification = bugs in productionAlways review, always testPaste the whole codebaseOverwhelms context, increases costCurate relevant files onlyNever start new chatsStale context → hallucinationsNew task = new chatTrust without readingAI generates plausible but wrong codeRead every lineSkip tests because AI wrote itAI code has bugs tooTest AI code MORE, not lessUse one model for everythingWaste money on simple tasksTier models by complexityNo project rules fileAI guesses your conventionsWrite .cursorrules / CLAUDE.mdVague promptsGarbage in, garbage outUse SPEC frameworkOver-relianceSkill atrophy, can't debug AI outputUnderstand what AI generatesIgnoring securityAI doesn't prioritize securityExplicit security review step

### AI-Assisted Development Quality Score (0-100)

DimensionWeightCriteriaContext engineering20%Rules files, curated context, fresh chatsPrompt quality15%SPEC framework, task-appropriate templatesVerification rigor20%Review checklist, test coverage, security reviewTool selection10%Right tool for task, model routingCost efficiency10%Tiered usage, context management, batch tasksOutput quality15%Code correctness, maintainability, no driftWorkflow integration10%Systematic process, team alignment

### Weekly Self-Review Questions

What was my best AI-assisted output this week? What made it good?
Where did AI waste my time? What went wrong with context/prompts?
Am I reviewing thoroughly enough, or rubber-stamping?
What prompt patterns worked well? Add to prompt library.
Am I over-relying on AI for things I should understand deeply?

### Monthly Metrics

Acceleration factor: Tasks completed per day vs pre-AI baseline
Bug rate: Bugs in AI-assisted code vs manual code
Cost per feature: API spend / features shipped
Context efficiency: Average conversation length before drift
Coverage: % of codebase with AI-assisted tests

### Quick Reference: Natural Language Commands

"Set up AI coding for [project]" — Generate rules file + tool recommendations
"Write a prompt for [task type]" — Generate SPEC-formatted prompt template
"Review this AI output" — Run the Trust-But-Verify checklist
"Compare [tool A] vs [tool B] for [use case]" — Tool selection analysis
"Optimize my AI coding costs" — Analyze usage and suggest model routing
"Create a team AI coding guide" — Generate team guidelines document
"Debug why AI keeps [hallucinating X]" — Context diagnosis
"Set up test-driven AI workflow for [feature]" — TDD-AI pattern guide
"Create prompt library for [project type]" — Generate prompt templates
"Score my AI coding maturity" — Run the quality assessment
"Onboard [person] to AI coding" — Generate training plan
"Audit AI coding security practices" — Security review checklist
## Trust
- Source: tencent
- Verification: Indexed source record
- Publisher: 1kalin
- Version: 1.0.0
## Source health
- Status: healthy
- Source download looks usable.
- Yavira can redirect you to the upstream package for this source.
- Health scope: source
- Reason: direct_download_ok
- Checked at: 2026-04-23T16:43:11.935Z
- Expires at: 2026-04-30T16:43:11.935Z
- Recommended action: Download for OpenClaw
## Links
- [Detail page](https://openagent3.xyz/skills/afrexai-ai-coding-toolkit)
- [Send to Agent page](https://openagent3.xyz/skills/afrexai-ai-coding-toolkit/agent)
- [JSON manifest](https://openagent3.xyz/skills/afrexai-ai-coding-toolkit/agent.json)
- [Markdown brief](https://openagent3.xyz/skills/afrexai-ai-coding-toolkit/agent.md)
- [Download page](https://openagent3.xyz/downloads/afrexai-ai-coding-toolkit)