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 skills add yohey-w/codd-dev --skill codd-restoregit clone --depth 1 https://github.com/yohey-w/codd-devWrote 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.
[](https://agentmods.dev/skills/yohey-w/codd-dev/codd-restore)<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/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<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="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00054 | $0.01195 |
| Opus 5.5 | $0.00022 | $0.00478 |
| Sonnet 5.5 | $0.00011 | $0.00239 |
| Haiku 4.5 | $0.00005 | $0.00120 |
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 1mo 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 — 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 extracthas generated extracted docs incodd/extracted/ - After
codd plan --inithas 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:
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.
- 1mo ago First seen · 126 lines · 54 tokens per session scan A dccaea73c900
codd-restore is a skill published in the GitHub repository yohey-w/codd-dev (115 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 1,195 once invoked, about $0.0002 per session on Opus 5.5. 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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