Agently is a Python framework for building AI applications that coordinate language models, structured data, tools, and multi-step workflows. Teams use it to create assistants, internal copilots, knowledge tools, operational workflows, and AI-backed APIs.
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 AgentEra/Agently --skill launch-risk-assessmentgit clone --depth 1 https://github.com/AgentEra/AgentlyWrote 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/agentera/agently/launch-risk-assessment)<a href="https://agentmods.dev/skills/agentera/agently/launch-risk-assessment"><img src="https://agentmods.dev/badge/skills/agentera/agently/launch-risk-assessment.svg" alt="Measured on agentmods" 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 | $0.00043 | $0.00129 |
| Opus 5 | $0.00022 | $0.00064 |
| Sonnet 5 | $0.00009 | $0.00026 |
| Haiku 4.5 | $0.00004 | $0.00013 |
Grade A, and why
Launch Risk Assessment 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 8d 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.
What it actually says
Launch Risk Assessment
Given product context (and positioning if available), produce 5-8 launch risks. For each: category, probability (low/med/high), impact (low/med/high), and a mitigation. Call out the top 3 highest priority risks.
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 · 15 lines · 43 tokens per session scan A 2879e6efe53b
Launch Risk Assessment is a skill published in the GitHub repository AgentEra/Agently (1,649 stars, last pushed yesterday), licensed Apache-2.0. It adds 43 tokens to every session and 129 once invoked, about $0.0002 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
python-run
Run and debug Python scripts in the project. Use when the user says "run python", "execute this script", "debug this py file", or wants to run/modify a .py file. Handles dependency checks, linting, execution, and error analysis.
python
Python package management.
pre-mortem
Pre-mortem (Gary Klein): before starting, assume the project has catastrophically failed.
risk-analysis
Provides a structured framework for risk identification and assessment.
jupyter-live-kernel
Iterative Python via live Jupyter kernel (hamelnb).
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.