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 dandye/ai-runbooks --skill inventory-contentgit clone --depth 1 https://github.com/dandye/ai-runbooksWrote 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/dandye/ai-runbooks/inventory-content)<a href="https://agentmods.dev/skills/dandye/ai-runbooks/inventory-content"><img src="https://agentmods.dev/badge/skills/dandye/ai-runbooks/inventory-content/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/dandye/ai-runbooks/inventory-content"><img src="https://agentmods.dev/badge/skills/dandye/ai-runbooks/inventory-content.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 | $0.00022 | $0.00559 |
| Opus 5 | $0.00011 | $0.00280 |
| Sonnet 5 | $0.00004 | $0.00112 |
| Haiku 4.5 | $0.00002 | $0.00056 |
Grade A, and why
inventory-content 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 10d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Inventory Skill
Create a systematic catalog of information assets within a specified path. This skill builds a comprehensive inventory including metadata, file characteristics, and format analysis to support content governance and strategic planning.
Inputs
PATH- The directory or file path to inventory (e.g., "/documentation")OUTPUT_FORMAT- (Optional) The output format for the inventory, e.g., "csv", "json", "markdown" (default: "json")METADATA_EXTRACTION- (Optional) Boolean, whether to extract deep metadata (author, date, tags) (default: true)FORMAT_ANALYSIS- (Optional) Boolean, whether to analyze file formats and types (default: true)
Workflow
Step 1: Asset Discovery
Recursively scan the PATH to identify all files and assets.
- Record file paths, names, and sizes.
- Identify file types (Markdown, HTML, PDF, Image, etc.).
Step 2: Metadata Extraction
If METADATA_EXTRACTION is true, extract metadata from each asset:
- System Metadata: Creation date, modification date, owner.
- Embedded Metadata: Frontmatter (YAML), title headers, tags, categories.
- Content Metrics: Word count, reading time estimation.
Step 3: Format & Structure Analysis
If FORMAT_ANALYSIS is true, analyze the structure:
- Template Usage: Identify if standard templates are used.
- Hierarchy Depth: Depth in the directory structure.
- Resource Dependencies: Images or other assets linked.
Step 4: Inventory Report Generation
Compile the data into a structured inventory format (CSV, JSON, or Markdown Table) as specified by OUTPUT_FORMAT.
Required Outputs
A CONTENT_INVENTORY_REPORT in the specified OUTPUT_FORMAT containing:
- Asset List: Full list of discovered assets.
- Metadata Table: Columns for Title, URL/Path, Author, Last Modified, Type, Tags.
- Summary Statistics: Total count by type, average age, volume by category.
Quick Reference
- Purpose: Establish a baseline understanding of content assets for governance.
- Use Case: Migration planning, audit preparation, consolidation projects.
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.
- 10d ago First seen · 61 lines · 22 tokens per session scan A f53dfcf13188
inventory-content is a skill published in the GitHub repository dandye/ai-runbooks (124 stars, last pushed 26d ago), licensed Apache-2.0. It adds 22 tokens to every session and 559 once invoked, about $0.0001 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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