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 ntorga/agent-starter-kit --skill agent-decisiongit clone --depth 1 https://github.com/ntorga/agent-starter-kitWrote 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/ntorga/agent-starter-kit/agent-decision)<a href="https://agentmods.dev/skills/ntorga/agent-starter-kit/agent-decision"><img src="https://agentmods.dev/badge/skills/ntorga/agent-starter-kit/agent-decision/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/ntorga/agent-starter-kit/agent-decision"><img src="https://agentmods.dev/badge/skills/ntorga/agent-starter-kit/agent-decision.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 | $0.00024 | $0.01566 |
| Opus 5 | $0.00012 | $0.00783 |
| Sonnet 5 | $0.00005 | $0.00313 |
| Haiku 4.5 | $0.00002 | $0.00157 |
Grade A, and why
agent-decision 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 2d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
This skill defines the procedure agents follow when encountering ambiguity during execution. It enforces a 5-step decision pipeline — classify, define problem, generate options, pick one, format output — and a self-review rubric (FRAME) scored before any escalation is presented to the user. The rubric gates delivery by total score and enforces a hard-fail on any single zero.
Procedure
-
Classify the ambiguity. When you encounter missing or unclear information, determine which of these 5 types applies:
- Approach choice — multiple valid approaches exist and the user did not specify one, but all lead to the same outcome. Example: whether to use a helper function or inline the logic.
- Minor ambiguity — the intent is clear but a detail is vague and a reasonable default exists. Example: "add logging" without specifying log level — default to info.
- Ambiguous requirement — the user's intent could mean two or more meaningfully different things. Example: "make it faster" could mean optimize the algorithm, add caching, or reduce payload size.
- Missing information — a required fact is absent and cannot be inferred. Example: "deploy to the server" with no server specified and no convention to fall back on.
- Risk confirmation — the action is destructive, expensive, or irreversible and the user has not explicitly authorized it. Example: dropping a database table, force-pushing to main.
-
Apply the branch.
- Approach choice and minor ambiguity → proceed with a documented default. Record your choice in your handoff. Do not block.
- Ambiguous requirement → escalate using the 1-3-1 method (step 3) and output template (step 4).
- Missing information or risk confirmation → stop and ask directly. For non-interactive sessions, return a handoff explaining the gap.
- The dividing line: if the ambiguity changes what you build, escalate. If it only changes how you build the same thing, proceed.
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.
- 2d ago First seen · 112 lines · 24 tokens per session scan A 37afe07136c7
agent-decision is a skill published in the GitHub repository ntorga/agent-starter-kit (142 stars, last pushed 2d ago), licensed MIT. It adds 24 tokens to every session and 1,566 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 2026-09-13.
Other skills, from other repositories
agent-framework-py-release
Use when cutting a Python release for the microsoft/agent-framework monorepo. Triggers on "bump py versions", "cut a python release", "prepare release PR for python", "release py packages", "bump python to X.Y.Z", or similar requests to bump Python package versions and prepare a release PR. Handles all four lifecycle…
python-package-management
Guide for managing packages in the Agent Framework Python monorepo, including creating new connector packages, versioning, and the lazy-loading pattern. Use this when adding, modifying, or releasing packages.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
verify-samples-tool
How to use the verify-samples tool to run, verify, and manage sample definitions in the Agent Framework repository. Use this when adding, updating, or running sample verification.
build-and-test
How to build and test .NET projects in the Agent Framework repository. Use this when verifying or testing changes.
python-feature-lifecycle
Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.