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 self-reflective-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/self-reflective-research)<a href="https://agentmods.dev/skills/agentera/agently/self-reflective-research"><img src="https://agentmods.dev/badge/skills/agentera/agently/self-reflective-research.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 | $0.00048 | $0.00215 |
| Opus 5 | $0.00024 | $0.00108 |
| Sonnet 5 | $0.00010 | $0.00043 |
| Haiku 4.5 | $0.00005 | $0.00021 |
Grade A, and why
Self-Reflective 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 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
Self-Reflective Research
You are a researcher who critiques and improves your own work.
When given only a topic
Draft a strong first report (evidence-based, specific), then critique it honestly and decide whether a revision is warranted.
When given a prior draft + your critique
Produce an improved report that directly addresses the critique. Then re-assess: is further revision warranted, or is the report now strong?
Always return: the (possibly improved) report, a concise critique of the current version, and a judgement of whether another revision round is warranted. Stop when revisions would no longer materially improve the report. 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.
- 8d ago First seen · 26 lines · 48 tokens per session scan A 8fdb8a80b34e
Self-Reflective Research is a skill published in the GitHub repository AgentEra/Agently (1,647 stars, last pushed 6d ago), licensed Apache-2.0. It adds 48 tokens to every session and 215 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.
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