ai-development-guide

A software development guide focused on finding the real cause of bugs and keeping changes within the required scope. It also covers checking that fixes and quality improvements are complete.

In plain words
What is it for?
Use it when fixing bugs, reviewing code quality, refactoring, making technical decisions, or checking implementation quality.
Why use it?
It helps avoid superficial fixes, unnecessary refactoring, and changes that are not supported by evidence. It keeps attention on the affected behavior and its governing requirements.

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

Made for: Claude Code, Codex.

Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,022 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 $0.00040 $0.01022
Opus 5 $0.00020 $0.00511
Sonnet 5 $0.00008 $0.00204
Haiku 4.5 $0.00004 $0.00102

Measured 2d ago against content hash 1b530138fd4a, 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.

.agents/skills/ai-development-guide/SKILL.md · 80 lines

How it starts

The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AI Development Guide

Reference

Read references/frontend.md only for React or TypeScript frontend work whose changed behavior or quality failure needs those rules.

Outcome Boundary

Deliver the confirmed outcome and keep the changed system correct. Investigate, repair, refactor, and verify only as far as one of these requires:

  • a current requirement or accepted design decision;
  • a dependency needed for that outcome;
  • an observed failure or contradiction in the changed path;
  • an evidence-backed material risk created or exposed by the change.

Keep implementation scope within those evidence-backed reasons and report unrelated debt separately.

Root-Cause Discipline

When an observed failure exists:

  1. Reproduce or identify the failing observable condition.
  2. Trace the responsible control, data, or state path until the cause is supported by evidence.
  3. Correct the cause at the smallest responsibility boundary that preserves the governing contract.
  4. Verify the original failure and the affected contract.

Stop causal questioning when the evidence supports the responsible path and its verification. Apply a direct correction when the cause and proof are already evident. Keep root-cause reasoning in the active task or response; create a separate artifact only for a named downstream consumer. Preserve error visibility and test strength, and correct the observed cause rather than masking it with an unconditional fallback or symptom patch.

Proportionate Impact Analysis

Before changing code, inspect the target and enough representative callers, consumers, tests, configuration, and siblings to determine:

  • the contract being changed or preserved;
  • the directly affected responsibility and dependency direction;
  • the observable verification that can prove the outcome;
  • any adjacent file required for the same outcome.

When the change alters a public, shared, serialized, or persistent contract and its consumers are enumerable, account for every known consumer. For other changes, representative inspection is sufficient. Stop expanding the search when additional context cannot change the implementation or verification decision. Record findings in the active task or response only when another worker needs them.

Read the full file on GitHub · 80 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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 · 80 lines · 40 tokens per session scan A 1b530138fd4a

Subscribe to this mod's changes

ai-development-guide is a skill published in the GitHub repository shinpr/codex-workflows (38 stars, last pushed 5d ago), licensed MIT. It adds 40 tokens to every session and 1,022 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.

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