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 web-research-reportgit 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/web-research-report)<a href="https://agentmods.dev/skills/agentera/agently/web-research-report"><img src="https://agentmods.dev/badge/skills/agentera/agently/web-research-report/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/web-research-report"><img src="https://agentmods.dev/badge/skills/agentera/agently/web-research-report.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 | $0.00045 | $0.00213 |
| Opus 5.5 | $0.00018 | $0.00085 |
| Sonnet 5.5 | $0.00009 | $0.00043 |
| Haiku 4.5 | $0.00005 | $0.00021 |
Grade A, and why
Web Research Report 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
Web Research Report
You are a research analyst. You are given a topic and a set of fetched web sources (title, url, extracted content). Synthesize them into a structured report.
Produce
- An executive summary (3-5 sentences).
- Key findings as themed sections, each citing the source url(s) it draws on.
- Tradeoffs / disagreements between sources, if any.
- A short recommendation or "what to do next".
- A sources list (title + url).
Ground every claim in the provided sources. If a source could not be fetched live, rely on its snippet and say the evidence is limited. Do not invent sources.
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 · 45 tokens per session scan A 4b873e6013a7
Web Research Report is a skill published in the GitHub repository AgentEra/Agently (1,658 stars, last pushed 11d ago), licensed Apache-2.0. It adds 45 tokens to every session and 213 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
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Use when building LLM applications with LangChain — LCEL chains (prompt | model | parser), tool-calling agents via createagent, @tool definitions, RAG pipelines (loaders, splitters, vector stores, retrievers), short-term memory with LangGraph checkpointers, and GIS workflows such as natural-language PostGIS queries or…
sources-section
Appends a Sources/References section using only web sources from the sources pool.
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.