ai-development-guide

A guide for making technical decisions, finding anti-patterns, debugging failures, and checking whether an implementation is complete.

In plain words
What is it for?
Use it when reviewing backend or general implementation choices, investigating failures, or checking code quality.
Why use it?
It helps keep changes focused on verified problems and required value while avoiding unnecessary infrastructure, abstractions, and complexity.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/shinpr/claude-code-workflows/ai-development-guide
Any agent
npx skills add shinpr/claude-code-workflows --skill ai-development-guide
Clone the repo
git clone --depth 1 https://github.com/shinpr/claude-code-workflows

Made for: Claude Code, Codex.

Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,856 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What 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.

ModelPer sessionOnce invoked
Fable 5 $0.00043 $0.02856
Opus 5 $0.00022 $0.01428
Sonnet 5 $0.00009 $0.00571
Haiku 4.5 $0.00004 $0.00286

Measured 2d ago against content hash 286e1570a8b8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ai-development-guide 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.

dev-skills/skills/ai-development-guide/SKILL.md · 260 lines

How it starts

The opening of the file, as written. The whole thing — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AI Developer Guide - Technical Decision Criteria and Anti-pattern Collection

Value-First Engineering

Inspect until the evidence identifies the lowest-total-complexity solution that delivers the required user, operator, or maintainer value while keeping the system correct and maintainable.

  • Resolve verified problems within confirmed scope or dependencies required for the outcome; report other findings with their owning boundary and evidence without expanding the active change.
  • Introduce capabilities, infrastructure, abstractions, or speculative edge-case handling when a current outcome, verified constraint, or evidence-backed material risk requires them.
  • Treat behavior-preserving maintenance inside the confirmed responsibility as current maintainer value when repository evidence shows it reduces change ambiguity, duplicate ownership, defect risk, or future implementation and verification cost without expanding observable product scope.

Judge total complexity across every activated surface: user decisions, settings, modes, concepts, outputs, persistent state, and implementation paths, together with their UX, runtime, implementation, testing, documentation, and maintenance cost. Compare only dimensions that differ between viable approaches. Prefer reuse or no new mechanism when it delivers the same confirmed value and proof at lower total complexity.

Technical Anti-patterns (Red Flag Patterns)

Pause the affected decision and review the design when detecting the following patterns:

Code Quality Anti-patterns

  1. Duplicating one responsibility across independently maintained locations - Review whether the duplicated logic has one change reason and should have one owner
  2. Multiple responsibilities mixed in a single file - Violates Single Responsibility Principle (SRP)
  3. Defining same content in multiple files - Violates DRY principle
  4. Making changes without checking dependencies - Potential for unexpected impacts
  5. Disabling code with comments - Should use version control
  6. Error suppression - Hiding problems creates technical debt
  7. Bypassing safety mechanisms (type systems, validation, contracts) - Circumventing language's correctness guarantees

Read the full file on GitHub · 260 lines

Changes

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.

  1. 2d ago First seen · 260 lines · 43 tokens per session scan A 286e1570a8b8

Subscribe to this mod's changes

ai-development-guide is a skill published in the GitHub repository shinpr/claude-code-workflows (675 stars, last pushed 5d ago), licensed MIT. It adds 43 tokens to every session and 2,856 once invoked, about $0.0002 per session on Opus 5. 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.

Related

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