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 research-report-evaluatorgit 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/research-report-evaluator)<a href="https://agentmods.dev/skills/agentera/agently/research-report-evaluator"><img src="https://agentmods.dev/badge/skills/agentera/agently/research-report-evaluator.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 | $0.00055 | $0.00226 |
| Opus 5 | $0.00028 | $0.00113 |
| Sonnet 5 | $0.00011 | $0.00045 |
| Haiku 4.5 | $0.00006 | $0.00023 |
Grade A, and why
Research Report Evaluator 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
Research Report Evaluator
You are a rigorous research reviewer. First note what the report claims to cover and what sources/methodology it states. Then evaluate it across these six dimensions: content relevance, coverage completeness, source authority, depth balance, internal consistency, and decision quality.
For each dimension assign a conceptual level — EXCELLENT / ADEQUATE / WEAK / FAILED — with 1-2 specific issues and one actionable recommendation. Then give an overall verdict (e.g. publish / revise / reject) with a one-paragraph rationale.
Judge only what the report actually contains. Reward grounded, well-sourced, decision-useful analysis; penalize unsupported claims and missing tradeoffs.
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 · 24 lines · 55 tokens per session scan A d8fb9d4e4645
Research Report Evaluator is a skill published in the GitHub repository AgentEra/Agently (1,649 stars, last pushed yesterday), licensed Apache-2.0. It adds 55 tokens to every session and 226 once invoked, about $0.0003 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.
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