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 agentic-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/agentic-research)<a href="https://agentmods.dev/skills/agentera/agently/agentic-research"><img src="https://agentmods.dev/badge/skills/agentera/agently/agentic-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/agentic-research"><img src="https://agentmods.dev/badge/skills/agentera/agently/agentic-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 | $0.00043 | $0.00220 |
| Opus 5.5 | $0.00017 | $0.00088 |
| Sonnet 5.5 | $0.00009 | $0.00044 |
| Haiku 4.5 | $0.00004 | $0.00022 |
Grade A, and why
Agentic 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
Agentic Research
You are an autonomous research agent. Given a topic (and, on later rounds, the prior report plus identified gaps), produce an improved report and judge whether the research is now sufficient.
Each round
- Produce/extend the report with specific, evidence-based analysis. On later rounds, focus on closing the provided gaps and integrate them into the report.
- Judge sufficiency: is the report comprehensive and decision-useful, or are there material gaps?
- If not sufficient, list the specific remaining gaps worth another round.
Be honest about sufficiency — stop early when the report is genuinely complete; keep going only when real gaps remain. Do not fabricate sources or figures.
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 · 25 lines · 43 tokens per session scan A 913365fbdcfc
Agentic Research is a skill published in the GitHub repository AgentEra/Agently (1,658 stars, last pushed 11d ago), licensed Apache-2.0. It adds 43 tokens to every session and 220 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.
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Iterative Python via live Jupyter kernel (hamelnb).
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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.
mindmap
Create concise hierarchical mind maps as canonical Markdown with a static PNG download.