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 skills/yunbow/ai-dev-os-plugin-claude-code/ai-dev-os-plannpx skills add yunbow/ai-dev-os-plugin-claude-code --skill ai-dev-os-plangit clone --depth 1 https://github.com/yunbow/ai-dev-os-plugin-claude-codeWrote 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/yunbow/ai-dev-os-plugin-claude-code/ai-dev-os-plan)<a href="https://agentmods.dev/skills/yunbow/ai-dev-os-plugin-claude-code/ai-dev-os-plan"><img src="https://agentmods.dev/badge/skills/yunbow/ai-dev-os-plugin-claude-code/ai-dev-os-plan.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00056 | $0.01015 |
| Opus 5 | $0.00028 | $0.00508 |
| Sonnet 5 | $0.00011 | $0.00203 |
| Haiku 4.5 | $0.00006 | $0.00102 |
Grade A, and why
ai-dev-os-plan 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 5d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Dev OS Implementation Plan
Execution Flow
1. Enter Plan Mode
Enter plan mode immediately. All analysis happens before any code is written.
2. Analyze the Request
Parse the user's implementation request and identify:
- Goal: What needs to be built or changed
- Scope: New feature / bug fix / refactoring / enhancement
- Affected area: Which parts of the codebase are involved
3. Identify Affected Files
Search the codebase to determine:
- Files that will be modified
- Files that will be created
- Files that will be deleted
- Related files that provide context (imports, tests, configs)
4. Parse CLAUDE.md and Load Guidelines
Extract the list of guideline file paths from the project's CLAUDE.md. Read the referenced guideline files to understand the active rules.
5. Build Dynamic Mapping
Map affected files to their relevant guidelines using file pattern matching.
Examples by tech stack:
- Python:
*.py→ code.md, naming.md, validation.md;*/router.py→ security.md, cors.md;*/models.py→ naming.md, validation.md;*/schemas.py→ validation.md;alembic/**→ naming.md - Next.js:
*.tsx→ ui.md, form.md, code.md, naming.md;app/**/page.tsx→ routing.md;app/**/action.ts→ server-actions.md - Go:
*.go→ code.md, naming.md, error-handling.md
If checklist templates exist in the plugin's checklist-templates/ directory for the detected tech stack, load them as a reference.
6. Extract Relevant Checklist Items
From the mapped guidelines, extract items that are relevant to this specific change:
- Filter by keywords: "MUST", "MUST NOT", "PROHIBITED", "REQUIRED"
- If a guideline has
checklist: [...]in frontmatter, use those items - Discard items unrelated to the current scope (e.g., skip database rules if no DB changes)
7. Present the Plan
Output the plan in the following format:
## Implementation Plan
### Goal
> [One-line summary of what will be implemented]
### Scope
- Type: [new feature / bug fix / refactoring / enhancement]
- Risk: [low / medium / high]
### Changes
| Action | File | Description |
|--------|------|-------------|
| modify | path/to/file.py | Add validation logic |
| create | path/to/new.py | New service module |
| ... | ... | ... |
### Implementation Steps
1. [Step 1 description]
2. [Step 2 description]
3. ...
### Guideline Checklist
> Auto-generated from AI Dev OS guidelines applicable to this change.
#### [guideline-file-1.md]
- [ ] Rule 1
- [ ] Rule 2
#### [guideline-file-2.md]
- [ ] Rule 3
- [ ] Rule 4
### Principles to Keep in Mind
> From L2 principles relevant to this change.
- [Principle 1]: [Brief explanation of relevance]
- [Principle 2]: [Brief explanation of relevance]
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.
- 5d ago First seen · 137 lines · 56 tokens per session scan A 6b8f7cf3fba6
ai-dev-os-plan is a skill published in the GitHub repository yunbow/ai-dev-os-plugin-claude-code (2 stars, last pushed 5mo ago), licensed MIT. It adds 56 tokens to every session and 1,015 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 2026-08-31.
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