Borrowing it
Nothing to install: this file belongs to agno-agi/context. 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/context/main/.agents/skills/eval-and-improve/SKILL.mdgit clone --depth 1 https://github.com/agno-agi/contextWrote 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/context/eval-and-improve)<a href="https://agentmods.dev/skills/agno-agi/context/eval-and-improve"><img src="https://agentmods.dev/badge/skills/agno-agi/context/eval-and-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/context/eval-and-improve"><img src="https://agentmods.dev/badge/skills/agno-agi/context/eval-and-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 | $0.00115 | $0.02634 |
| Opus 5.5 | $0.00046 | $0.01054 |
| Sonnet 5.5 | $0.00023 | $0.00527 |
| Haiku 4.5 | $0.00012 | $0.00263 |
Grade A, and why
eval-and-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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval and Improve
Coding-agent workflow — a
/slash-commandyour coding agent (Claude Code, Codex, …) runs while developing this repo. Not a runtime skill the deployed @context agent runs; those live inskills/.
You're running @context's eval suite, diagnosing every failure, fixing what's in scope, and stopping when all cases pass. Surface area is two files: evals/cases.py (declares cases) and evals/__main__.py (runner). A case applies up to four checks, deterministic ones first (they're the spine; the judge corroborates):
- structural — a zero-arg callable returning
(passed, detail); when set, the agent is not run. Used byboundary_is_structuralto assert the guest/owner toolset asymmetry with no model in the loop. Deterministic. - expected_tool_calls — agno's
ReliabilityEvalasserts which tools fired. Deterministic. - capture_only — for guest runs, asserts every tool that fired is on the capture-only allowlist (no read/act tool, checked at the trace level). Deterministic.
- criteria — agno's
AgentAsJudgeEval(LLM rubric, binary pass/fail), optionally narrowed withjudge_guidelines. Keep it decisive so it doesn't flake.
No custom DSL beyond those fields on the Case dataclass.
0. Preconditions
- Postgres reachable on 5432:
nc -z localhost 5432returns 0. If not,docker compose up -d context-dbfrom the source repo. (docker compose psis unreliable from worktrees or alternate clones.) - Venv active:
source .venv/bin/activate. If.venvdoesn't exist (fresh checkout or worktree), run./scripts/venv_setup.shfirst.evals/cases.pyimports the agents directly fromagents/, so no AgentOS server has to be running. .envpopulated withOPENAI_API_KEY.evals/__main__.pycallsevals.dotenv.load_dotenv()at startup, so you do not need to source.envfirst. Worktrees don't inherit.env(it's gitignored) — copy it from the source repo if missing.
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.
- 2mo ago First seen · 155 lines · 115 tokens per session scan A f7e0954c9023
eval-and-improve is a skill published in the GitHub repository agno-agi/context (13 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 115 tokens to every session and 2,634 once invoked, about $0.0005 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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