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 joneqian/claude-skills-suite --skill process-faqgit clone --depth 1 https://github.com/joneqian/claude-skills-suiteWrote 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/joneqian/claude-skills-suite/process-faq)<a href="https://agentmods.dev/skills/joneqian/claude-skills-suite/process-faq"><img src="https://agentmods.dev/badge/skills/joneqian/claude-skills-suite/process-faq/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/joneqian/claude-skills-suite/process-faq"><img src="https://agentmods.dev/badge/skills/joneqian/claude-skills-suite/process-faq.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00052 | $0.04529 |
| Opus 5 | $0.00026 | $0.02264 |
| Sonnet 5 | $0.00010 | $0.00906 |
| Haiku 4.5 | $0.00005 | $0.00453 |
Grade A, and why
process-faq 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 11d 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 — 596 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Process FAQ - FAQ Knowledge Base Processor
Transform raw FAQ documents into RAG-optimized structured format with intelligent content expansion and analysis.
What This Skill Does
This skill helps you:
- Convert FAQ documents to readable Markdown format (script)
- Analyze FAQ content and identify expansion opportunities (Claude)
- Expand content: split complex questions, rewrite answers (Claude)
- Standardize format and generate keywords automatically (script)
Supported Input Formats
- Excel (.xlsx)
- Word (.docx)
- PDF (.pdf)
- Text (.txt)
Workflow Overview (3-Step Process)
Step 1: Convert → Markdown (script)
Step 2: Expand → Enhanced FAQ (Claude - THIS IS THE KEY STEP!)
Step 3: Standardize → Final RAG format (script)
New Workflow (Claude + Script Collaboration)
Step 1: Convert to Markdown (Script)
First, convert the input file to Markdown so Claude can read and analyze it:
python process-faq/scripts/convert_to_markdown.py <input_file>
This creates a *_for_analysis.md file with structured FAQ content.
Why Markdown?
- Claude can directly read and understand the content
- Better for analyzing content quality vs just checking format
- Allows for nuanced, intelligent analysis
Step 2: Claude Analyzes and Expands Content (CRITICAL!)
This is the most important step where YOU (Claude) create value!
Read the Markdown file and perform deep content analysis and expansion:
Phase A: Content Quality Analysis (质量检查)
IMPORTANT: Do this BEFORE expanding content!
Identify and document issues:
-
Logical Issues (逻辑问题)
- Contradictions: Do different answers give conflicting information?
- Example: Q1 says "支持退货" but Q2 says "不支持退货"
- Inconsistencies: Do similar questions have different answers?
- Example: "配送时间 3 天" vs "配送时间 5-7 天"
- Outdated Information: References to old products, prices, or policies?
- Contradictions: Do different answers give conflicting information?
-
Duplicate/Redundant Content (重复内容)
What ships with it
4 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.
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.
- 11d ago First seen · 596 lines · 52 tokens per session scan A 5a21aaa1c795
process-faq is a skill published in the GitHub repository joneqian/claude-skills-suite (32 stars, last pushed 7mo ago), licensed MIT. It adds 52 tokens to every session and 4,529 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.
Other skills, from other repositories
markitdown
Convert heterogeneous documents and selected URIs to Markdown with Microsoft MarkItDown for text analysis, search, and LLM/RAG ingestion. Covers safe local conversion, streams, Office/PDF/data formats, batch workflows, plugins, vision OCR, Azure extraction, and the official MCP server.
llamaindex
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM…
azure-ai
Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.
kb-retriever
A retrieval and question-answering assistant for a local folder of documents, including Markdown, text, PDFs, and spreadsheets.
bailian-kb
A command-line manager for Alibaba Cloud Bailian knowledge bases, which are collections of documents prepared for search and question answering. It handles the stored documents, search services, text chunks, and data-centre files rather than everyday searches.
azure-ai-contentunderstanding-py
Multimodal AI service that extracts semantic content from documents, video, audio, and image files for RAG and automated workflows.