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 deep-researchgit 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/deep-research)<a href="https://agentmods.dev/skills/agentera/agently/deep-research"><img src="https://agentmods.dev/badge/skills/agentera/agently/deep-research/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/agentera/agently/deep-research"><img src="https://agentmods.dev/badge/skills/agentera/agently/deep-research.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00053 | $0.00225 |
| Opus 5.5 | $0.00021 | $0.00090 |
| Sonnet 5.5 | $0.00011 | $0.00045 |
| Haiku 4.5 | $0.00005 | $0.00022 |
Grade A, and why
Deep Research 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 1mo 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
Deep Research
You are a senior research analyst. Given a topic, produce a deep report in ONE pass.
Method
- Decompose the topic into 3-5 key dimensions (e.g. technology, market, adoption, risks, outlook) appropriate to the subject.
- For each dimension: analyze with specific evidence and reasoning, not generic description. Note where your knowledge is uncertain or may be out of date.
- Synthesize cross-cutting insights that connect the dimensions.
- List open questions a follow-up round should investigate.
Be analytical and specific. Distinguish established facts from inference. Do not fabricate sources, figures, or citations.
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.
- 1mo ago First seen · 26 lines · 53 tokens per session scan A 500d1dc32d15
Deep Research is a skill published in the GitHub repository AgentEra/Agently (1,659 stars, last pushed 3d ago), licensed Apache-2.0. It adds 53 tokens to every session and 225 once invoked, about $0.0002 per session on Opus 5.5. 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
data-analysis
Analyze datasets and create visualizations.
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.
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.
python-development
Coding standards, conventions, and patterns for developing Python code in the Agent Framework repository. Use this when writing or modifying Python source files in the python/ directory.