Borrowing it
Nothing to install: this file belongs to agno-agi/agentos-docker. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/agno-agi/agentos-docker/main/.agents/skills/improve/SKILL.mdgit clone --depth 1 https://github.com/agno-agi/agentos-dockerWrote 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/agno-agi/agentos-docker/improve)<a href="https://agentmods.dev/skills/agno-agi/agentos-docker/improve"><img src="https://agentmods.dev/badge/skills/agno-agi/agentos-docker/improve/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/agno-agi/agentos-docker/improve"><img src="https://agentmods.dev/badge/skills/agno-agi/agentos-docker/improve.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00078 | $0.01785 |
| Opus 5.5 | $0.00031 | $0.00714 |
| Sonnet 5.5 | $0.00016 | $0.00357 |
| Haiku 4.5 | $0.00008 | $0.00178 |
Grade A, and why
improve 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 today.
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.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Improve
Make the agent more reliable at its intended job. For an improvement or hardening request, carry the work through finding weaknesses, diagnosing their causes, making focused fixes and verifying the result. Simulations uncover failure modes; evals provide repeatable evidence and can preserve useful regressions. Prioritize failures by their impact on the user's task.
Adapt this workflow to the request:
- Harden the agent: simulate realistic use, diagnose weaknesses, fix them and verify the fixes against the original failures, fresh variants and relevant regressions.
- Investigate a failure: reproduce an observed failure or failing eval and address its cause within the requested scope.
- Run simulations only: exercise behavior and report findings without requiring edits.
- Save evals: turn selected simulations into repeatable coverage using create-evals.
Infer the intended outcome from the request; clarify only when it materially changes the work. A general hardening request includes targeted fixes, so do not stop at an analysis report when those fixes are in scope and feasible. Retaining evals is optional unless requested; recommend meaningful regression cases rather than saving every probe. Judge success by more reliable behavior across scenarios, not just a higher eval score.
Establish the target and execution context
Read AGENTS.md, inspect git status, and locate the target's
declaration or published Studio configuration. Capture its promised behavior, tools, model,
existing coverage and installed Agno version. Preserve explicit model choices.
Simulations supply user requests; the agent, tools and database are real unless explicitly substituted. Runs and judge calls cost money, and sessions/results persist. State a small initial batch and honor the user's budget. Use owned fixtures for mutations; unique fixture users do not isolate global tools or external services. Externally mutating scenarios need authorized, contained effects. Preserve historical sessions, notes, schedules and Company work.
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.
- today Changed ab6b6e086754
- 3d ago First seen · 140 lines · 78 tokens per session scan A 3061f84b9669
improve is a skill published in the GitHub repository agno-agi/agentos-docker (14 stars, last pushed today), licensed Apache-2.0. It adds 78 tokens to every session and 1,785 once invoked, about $0.0003 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 2026-10-27.
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