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/microsoft/agent-framework/foundry-config-setupnpx skills add microsoft/agent-framework --skill foundry-config-setupgit clone --depth 1 https://github.com/microsoft/agent-frameworkWhat 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 | $0.00065 | $0.00430 |
| Opus 5 | $0.00032 | $0.00215 |
| Sonnet 5 | $0.00013 | $0.00086 |
| Haiku 4.5 | $0.00006 | $0.00043 |
Grade A, and why
foundry-config-setup 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 yesterday.
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.
What it actually says
Usage
Some samples (notably those under 01-get-started) hardcode the Foundry
project endpoint and model directly in the FoundryChatClient constructor
using placeholder values, for example:
client = FoundryChatClient(
project_endpoint="https://your-project.services.ai.azure.com",
model="gpt-4o",
credential=AzureCliCredential(),
)
These placeholder values are not real and the sample cannot run as written. The validation environment provides the real values through environment variables, so the sample must read them from the environment instead.
When you detect a hardcoded/placeholder project_endpoint (or model) that
is causing the sample to fail with missing or invalid setup:
- Replace the hardcoded
project_endpointvalue with a read from theFOUNDRY_PROJECT_ENDPOINTenvironment variable. - Replace the hardcoded
modelvalue with a read from theFOUNDRY_MODELenvironment variable. - Ensure
import osis present at the top of the file.
The corrected constructor should look like:
import os
client = FoundryChatClient(
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
model=os.environ["FOUNDRY_MODEL"],
credential=AzureCliCredential(),
)
These samples are intentionally written with hardcoded placeholders, so this
is expected setup—not a defect in the sample. After applying the change,
re-run the sample and report the result as a success if it now runs.
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.
- yesterday First seen · 53 lines · 65 tokens per session scan A 72279cefeddb
foundry-config-setup is a skill published in the GitHub repository microsoft/agent-framework (13,222 stars, last pushed 2d ago), licensed MIT. It adds 65 tokens to every session and 430 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 2026-08-30.
Other skills, from other repositories
swarmclaw
AI agent runtime and multi-agent orchestration platform. Teaches agents how to use SwarmClaw's 6 primitive tools, persistent memory, dreaming, delegation, connectors, credentials, and the skill system. Use when an agent is running on SwarmClaw and needs to understand the platform's capabilities.
agent-collaboration
Use this skill when coordinating multiple AI agents. Covers multi-agent patterns, handoffs, and orchestration strategies.
crewai-multi-agent
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies…
strands-review
Local preview of the strands-agents/devtools /strands review agent. Body is the upstream Task Reviewer SOP verbatim — do not paraphrase. Use when the user types /strands-review, asks for a "strands review" of a PR, or wants to anticipate what the remote /strands review GitHub Action will flag. Findings are close but…
docs-writer
Draft or rewrite Strands Agents documentation pages. Use when writing new doc pages, rewriting pages that failed audit, drafting sections for existing pages, or writing blog posts and release notes about Strands. Also triggers on "write a doc", "draft a page", "rewrite the quickstart", "add a tutorial for X"…
pr-writer
Generates pull request titles and descriptions. Use when the user asks to create, open, write, draft, or generate a PR, pull request, or merge request description.