What is codd-restore?

A workflow for rebuilding design documents from the facts found in an existing codebase. It is for brownfield projects—systems that already have code but lack reliable design documentation.

Use it to infer requirements and reconstruct system or detailed design documents after extracting facts from an undocumented codebase.

It helps recover the system's current structure and apparent intent without starting from requirements for a new project. It separates code extraction, planning, document restoration, and dependency scanning.

codd-restore — Skill for Claude Code and Codex by yohey-w: A workflow for rebuilding design documents from the facts…

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/yohey-w/codd-dev/codd-restore

Any agent

npx skills add yohey-w/codd-dev --skill codd-restore

Clone the repo

git clone --depth 1 https://github.com/yohey-w/codd-dev

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/yohey-w/codd-dev/codd-restore.svg)](https://agentmods.dev/skills/yohey-w/codd-dev/codd-restore)

Your own site

<a href="https://agentmods.dev/skills/yohey-w/codd-dev/codd-restore"><img src="https://agentmods.dev/badge/skills/yohey-w/codd-dev/codd-restore.svg" alt="Measured on agentmods" height="20"></a>

Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,195 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.

Model	Per session	Once invoked
Fable 5	$0.00054	$0.01195
Opus 5	$0.00027	$0.00598
Sonnet 5	$0.00011	$0.00239
Haiku 4.5	$0.00005	$0.00120

Measured 3d ago against content hash dccaea73c900, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

codd-restore 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 3d 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.

skills/codd-restore/SKILL.md · 126 lines

How it starts

The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CoDD Restore

Reconstruct design documents from extracted code facts for brownfield projects. Unlike /codd-generate (which creates docs from requirements), /codd-restore asks "what IS the current design?" — reconstructing intent from code structure.

When to Use

After codd extract has generated extracted docs in codd/extracted/
After codd plan --init has generated wave_config from extracted docs (or requirements)
When you have an existing codebase with no design documentation
When you want to infer requirements from code (wave 0 / requirements docs)

Do NOT use this for greenfield projects with requirements — use /codd-generate instead.

Brownfield Flow

codd extract          # Step 1: Static analysis → codd/extracted/
codd plan --init      # Step 2: Extracted docs → wave_config (auto-detects brownfield)
codd restore --wave 0 # Step 3a: Infer requirements from code facts
codd restore --wave 2 # Step 3b: Reconstruct system design
codd restore --wave 3 # Step 3c: Reconstruct detailed design
codd scan --path .    # Step 4: Build dependency graph

Requirements Inference (Wave 0)

When restoring a document under docs/requirements/, the restore command switches to requirements inference mode:

Infers functional requirements from modules, classes, API routes, and function signatures
Infers non-functional requirements from code patterns (async = performance, rate limiting = scalability, RLS = security)
Infers constraints from frameworks, libraries, and architectural patterns
Marks non-obvious inferences with [inferred] so humans can verify

Important limitations (the prompt explicitly warns the AI about these):

Cannot know features that were planned but never implemented
Cannot distinguish bugs from intentional behavior
Cannot know business context not reflected in code

These are inferred requirements — describing what was built, not original intent. Human review is essential.

Prerequisite Checks

Before every restore run, verify:

Read the full file on GitHub · 126 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.

3d ago First seen · 126 lines · 54 tokens per session scan A dccaea73c900

Subscribe to this mod's changes

codd-restore is a skill published in the GitHub repository yohey-w/codd-dev (114 stars, last pushed 23d ago), licensed MIT. It adds 54 tokens to every session and 1,195 once invoked, about $0.0003 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.

Planning Writing and docs

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens