Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/daffy0208/ai-dev-standards/usage-examplesgit clone --depth 1 https://github.com/daffy0208/ai-dev-standardsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.03201 |
| Opus 5 | $0.00000 | $0.01600 |
| Sonnet 5 | $0.00000 | $0.00640 |
| Haiku 4.5 | $0.00000 | $0.00320 |
Grade A, and why
usage-examples scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 703 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Usage Examples
Practical examples showing how to use different agents for common development tasks.
Table of Contents
- New Repository Onboarding
- Bug Investigation
- Feature Implementation
- Refactoring
- Security Audit
- Performance Optimization
- Documentation Generation
- Architecture Analysis
- Migration Planning
- Code Review Preparation
New Repository Onboarding
Scenario
You've just cloned a new React/Node.js project and need to understand it quickly.
Approach
Phase 1: Quick Overview (5 minutes)
Agent: Explore (Quick)
Task: "Give me an overview of this project - what it does,
tech stack, and main entry points"
Expected Output:
- Project purpose
- Tech stack (React, Node, PostgreSQL)
- Entry points (index.tsx, server.ts)
- Directory structure
Phase 2: Deep Dive on Key Areas (30 minutes)
Agent: Explore (Medium)
Task: "Explore the authentication system - how users log in,
session management, and authorization checks"
Expected Output:
- Auth flow diagram
- Components involved
- API endpoints
- Security measures
Phase 3: Get Started (Ongoing)
Agent: General-Purpose
Task: "Set up my local development environment following
the project conventions"
Actions:
- Install dependencies
- Configure environment
- Run tests
- Start dev server
Bug Investigation
Scenario
Users report authentication failing with special characters in passwords.
Approach
Phase 1: Locate Code (5 minutes)
Agent: General-Purpose
Task: "Find all code related to password validation and
authentication, especially where user input is processed"
Tools Used:
- Grep for "password", "validation", "auth"
- Read auth-related files
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 703 lines · 0 tokens per session scan A c66f18bc306f
usage-examples is an agent published in the GitHub repository daffy0208/ai-dev-standards (36 stars, last pushed 8mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,201 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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The aidlc-developer-agent is your senior software developer. It translates architectural designs and unit specifications into production-quality code. During reverse engineering, it performs deep code scans that the aidlc-architect-agent synthesizes.