agentos-docker: Skill for Claude Code

.agents/skills/eval-and-improve/SKILL.md

eval-and-improve is a skill for Claude Code, Codex from agno-agi/agentos-docker. It costs 74 tokens per session (1,491 once invoked), scanned A, original, Apache-2.0.

An automated workflow for running a project's evaluation tests, investigating each failure, and fixing problems within scope until the tests pass. The suite may use an AI judge, expected tool-call checks, or fixed scoring rules.

In plain words
What is it for?
Use it to run smoke, release, live, or individual evaluation cases, diagnose failures, repair the related code, and repeat the checks.
Why use it?
It removes the need to manually rerun failed evaluations and trace every failure yourself. It also gives a clear stopping point when all cases pass.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is agno-agi/agentos-docker's own configuration. It tells Claude Code and Codex how to work on agentos-docker itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything agentos-docker configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/agno-agi/agentos-docker/main/.agents/skills/eval-and-improve/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/agno-agi/agentos-docker

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for eval-and-improve

README.md
[![agentmods](https://agentmods.dev/badge/skills/agno-agi/agentos-docker/eval-and-improve.svg)](https://agentmods.dev/skills/agno-agi/agentos-docker/eval-and-improve)
Your own site
<a href="https://agentmods.dev/skills/agno-agi/agentos-docker/eval-and-improve"><img src="https://agentmods.dev/badge/skills/agno-agi/agentos-docker/eval-and-improve.svg" alt="Measured on agentmods" height="20"></a>
Per session 74 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,491 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00074 $0.01491
Opus 5 $0.00037 $0.00745
Sonnet 5 $0.00015 $0.00298
Haiku 4.5 $0.00007 $0.00149

Measured 8d ago against content hash 43455a1b1ede, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

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 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.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

.agents/skills/eval-and-improve/SKILL.md · 79 lines

How it starts

The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Eval and Improve

Coding-agent workflow: run as /eval-and-improve or by describing the task.

Run the suite, diagnose every failure, fix what's in scope, stop when green. Cases live in evals/cases.py (agno.eval.Case), the setup/teardown sweeps in evals/hooks.py, the entrypoint in evals/__main__.py. Each case uses an LLM judge (criteria), a tool-call assertion (expected_tool_calls), and/or a deterministic scorer.

0. Preconditions

  • Postgres on 5432 (nc -z localhost 5432; else docker compose up -d agentos-db).
  • Venv active (source .venv/bin/activate; ./scripts/venv_setup.sh if missing). No server needed — cases import the components directly.
  • .env has OPENAI_API_KEY (and PARALLEL_API_KEY if you use one — it pins Agno's expected web tool name at import). Worktrees don't inherit .env.

1. Run

python -m evals --tag smoke            # fast template checks
python -m evals --tag release          # broader pre-release checks
python -m evals --tag live             # current web/source checks
python -m evals --name <case>          # one case
python -m evals --tag smoke --list     # what a selector picks, without running
python -m evals --json-output out.json # machine-readable (carries judge_reason)
python -m evals -v                     # stream the runs

Exit 0 = all passed. Be the only writer: teardowns sweep by snapshot diff, so a note or entity a teammate files during a case gets deleted. Coming from a scheduled failure? Find the case in eval history (db.get_eval_runs(), os.agno.com, or Platform Manager) and reproduce it with --name first; one that won't reproduce is usually environment. Stderr noise at the end of a run (Event loop is closed, httpx timeouts) is harmless.

2. Diagnose

Symptom Likely cause Fix
Judge: right answer, missing X Instructions don't push for X agents/<slug>.py — tighten the rule
Judge: fabricated Should have said it didn't know Add a "say so plainly" rule
Reliability: missing tool Routing rule weak, or the case too narrow Strengthen the rule, or broaden expected_tool_calls
Reliability: additional tool with allow_additional_tool_calls=False Agent fanned out Tighten instructions or allow it
Agno web tool name mismatch (parallel_searchweb_search) PARALLEL_API_KEY differs between .env and shell Sync and re-run
Flips PASS/FAIL across runs, no change Judge variance Re-run 2–3×; still flipping → make criteria more falsifiable
Fails in the suite, passes alone Transient flake / 429 Re-run alone, then the suite; persistent 429s → back off
Many fail at once Broad regression (model, MCP down, tool removed) Find the root cause; no prompt edits
run paused awaiting user input Hit a HITL gate (Builder archive/delete) — never graded Keep the case input on the ungated side
cleanup: in the error A teardown couldn't delete what the case created Hard-delete by id (eval_db.delete_component(id, hard_delete=True), ScheduleManager(eval_db).delete(id), eval_db.delete_learning(id), notes.delete(path)); don't touch the agent or case
refusing to sweep … A guard tripped: rows predate the snapshot, or more rows than a case plausibly creates Don't loosen the guard; inspect, delete the case's rows by hand, re-run

Read the full file on GitHub · 79 lines

Changes

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.

  1. 8d ago First seen · 79 lines · 74 tokens per session scan A 43455a1b1ede

Subscribe to this mod's changes

eval-and-improve is a skill published in the GitHub repository agno-agi/agentos-docker (7 stars, last pushed 7d ago), licensed Apache-2.0. It adds 74 tokens to every session and 1,491 once invoked, about $0.0004 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 2026-08-31.

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