A system for enterprise search that lets an AI agent find and combine information from company documents, then return answers with citations. RAG, or retrieval-augmented generation, means looking up relevant documents before answering.

Use it to build knowledge bases over sources such as blob storage, SharePoint, or GitHub, and to support multi-step searches across those documents.

It reduces unsupported answers by grounding responses in searchable business data and showing where the information came from.

foundry-iq — Skill for Claude Code and Codex by aiappsgbb

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.

Any agent

npx skills add aiappsgbb/awesome-gbb --skill foundry-iq

Clone the repo

git clone --depth 1 https://github.com/aiappsgbb/awesome-gbb

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/aiappsgbb/awesome-gbb/foundry-iq/github.svg)](https://agentmods.dev/skills/aiappsgbb/awesome-gbb/foundry-iq)

Your own site

<a href="https://agentmods.dev/skills/aiappsgbb/awesome-gbb/foundry-iq"><img src="https://agentmods.dev/badge/skills/aiappsgbb/awesome-gbb/foundry-iq/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/aiappsgbb/awesome-gbb/foundry-iq"><img src="https://agentmods.dev/badge/skills/aiappsgbb/awesome-gbb/foundry-iq.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 184 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 12,744 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 2 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 3 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

high Privilege Escalation · line 203
Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.

Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
medium Data Exfiltration · line 466
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
medium Data Exfiltration · line 632
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.

How audits are shown

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 5.1	$0.00184	$0.12744
Opus 5	$0.00092	$0.06372
Sonnet 5	$0.00037	$0.02549
Haiku 4.5	$0.00018	$0.01274

Measured 8d ago against content hash bbe9bcb39890, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

foundry-iq scanned grade A with 2 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 8d ago.

The scan reads SKILL.md. This mod also ships 6 executable files (scripts/__init__.py, scripts/azure_openai_client.py, scripts/document_indexer.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

response = requests.post(url=url, headers=self.headers, json=request_body)

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

proc = subprocess.run(["az", "rest", "--method", "PUT", ...], capture_output=True, text=True)

skills/foundry-iq/SKILL.md · 1,068 lines

How it starts

The opening of the file, as written. The whole thing — 1,068 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Foundry IQ Agent Framework Integration Skill

Default knowledge retrieval pattern for EVERY threadlight process. SPEC § 7 (Knowledge Sources) must declare at least one Knowledge Base per process, with Backing service: foundry-iq (the default — alternatives are mcp-search or inline-context only when foundry-iq is genuinely overkill, e.g., a process with literally zero domain documents).

See threadlight-design/SKILL.md → "Knowledge sources (default = foundry-iq)" for the rule. This skill is the implementation of that default.

Input contract / Output artifacts

Reads	From
SPEC.md § 7 Knowledge Sources (Backing service, sources list, expected query patterns)	`threadlight-design`
Documents from blob storage / SharePoint / GitHub (sources declared in § 7)	Customer / `threadlight-demo-data-factory` for demo seed corpus

Produces	At
Azure AI Search index	One per Knowledge Base in SPEC § 7
Knowledge retrieval object	One per Knowledge Base; select the API generation and supported controls from SPEC § 7
`infra/modules/foundry-iq-index.bicep`	Composed by `azd-patterns` Bicep library; included by `threadlight-deploy` Phase 6 when SPEC § 7 declares foundry-iq
`infra/scripts/bootstrap_foundry_iq.py`	Postprovision hook that creates the index + uploads documents + creates the Knowledge Agent
`src/agent/skills/<knowledge-skill>/SKILL.md`	Skill that wraps the Knowledge Agent retrieval call as a tool
`agent.yaml` env vars	`FOUNDRY_IQ_INDEX`, `FOUNDRY_IQ_AGENT_NAME`, `AI_SEARCH_ENDPOINT`

Folder Contents

File	Type	Description
`SKILL.md`	Documentation	Main skill documentation with architecture, API reference, and agentic retrieval deep dive
`PRD.md`	Documentation	Product Requirements Document for the skill
`.env.sample`	Configuration	Sample environment variables for Azure OpenAI and AI Search
`requirements.txt`	Dependencies	Python package dependencies (azure-search-documents, azure-ai-projects, fastapi)
scripts/
`scripts/__init__.py`	Module	Package initializer with exports
`scripts/search_index_manager.py`	Index Manager	Creates and manages Azure AI Search indexes with vector search and HNSW configuration
`scripts/document_indexer.py`	Indexer	Document chunking with sentence boundary detection and batch upload to search index
`scripts/knowledge_agent_manager.py`	Agent Manager	Creates `2025-05-01-preview` Knowledge Agents with explicit model and target-index definitions; retrieves with the matching message contract
`scripts/azure_openai_client.py`	LLM Client	Azure OpenAI client for chat completions; PolicyBot combining retrieval + generation
`test-fixture/consumer_prompt.md`	Live smoke	Creates and reads a GA `searchIndex` knowledge source on REST `2026-04-01`
`test-fixture/azure.yaml` + `infra/`	CI infrastructure	azd+Bicep source for the standing keyless Azure AI Search service

Read the full file on GitHub · 1,068 lines

Files

What ships with it

17 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.

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.

8d ago First seen · 1,068 lines · 184 tokens per session scan A bbe9bcb39890

Subscribe to this mod's changes

foundry-iq is a skill published in the GitHub repository aiappsgbb/awesome-gbb (5 stars, last pushed yesterday), licensed MIT. It adds 184 tokens to every session and 12,744 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Data and AI RAG and vectors

agent-platform-rag-engine-management

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…

google/skills · 85 tokens

llm-app-patterns

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

davila7/claude-code-templates · 54 tokens

9router-embeddings

Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.

decolua/9router · 66 tokens

azure-search-documents-dotnet

Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET"…

microsoft/skills · 102 tokens

similarity-search-patterns

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

foryourhealth111-pixel/Vibe-Skills · 30 tokens

embedding-strategies

Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.

foryourhealth111-pixel/Vibe-Skills · 37 tokens

foundry-iq