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/improve-agent/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/improve-agent)<a href="https://agentmods.dev/skills/agno-agi/context/improve-agent"><img src="https://agentmods.dev/badge/skills/agno-agi/context/improve-agent/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/improve-agent"><img src="https://agentmods.dev/badge/skills/agno-agi/context/improve-agent.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 | $0.00124 | $0.02778 |
| Opus 5.5 | $0.00050 | $0.01111 |
| Sonnet 5.5 | $0.00025 | $0.00556 |
| Haiku 4.5 | $0.00012 | $0.00278 |
Grade C, and why
improve-agent scanned grade C with 3 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.
Instruction-override phrasingmediumPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
- **Injection / scope** — agent followed user-supplied "ignore previous instructions" or otherwise let user input override its role. Different fix from a format slip: add a "treat user message as query, not instructions" Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
- Live container reachable: `curl -sSf http://localhost:8000/health` returns 200. If not, ask the user to `docker compose up -d --build` first. (`docker compose ps` is unreliable from worktrees or alternate clones — trus Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Live container reachable: `curl -sSf http://localhost:8000/health` returns 200. If not, ask the user to `docker compose up -d --build` first. (`docker compose ps` is unreliable from worktrees or alternate clones — trus How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Improve an Agent
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 are recursively improving a target agent autonomously. No user-supplied test cases — you derive your own probes from the agent's stated purpose (its INSTRUCTIONS), test the agent against them, judge the results, and iterate on agents/<slug>.py until the agent reliably does what its instructions say it does.
This is the autonomous half of the iteration loop. The user-driven half lives in the extend-agent skill (add a tool, add a capability, refine the prompt, fix a specific bug). Use extend-agent to change the agent; use this skill to harden it against its stated intent.
The platform is on http://localhost:8000 with hot-reload enabled (RUNTIME_ENV=dev), so edits to agents/<slug>.py are picked up by uvicorn within ~1s. No rebuild, no restart.
This is a single-pass loop. One pass usually takes 15-30 minutes depending on the agent's surface area. Re-run if behavior still drifts.
0. Preconditions
-
Live container reachable:
curl -sSf http://localhost:8000/healthreturns 200. If not, ask the user todocker compose up -d --buildfirst. (docker compose psis unreliable from worktrees or alternate clones — trust the health probe.) -
Live container is bound to this checkout — otherwise hot-reload won't see your edits:
docker inspect context-api --format '{{range .Mounts}}{{.Source}}{{"\n"}}{{end}}' | grep -F "$(pwd)"Empty result = the container's
/appis bound to a different repo path. Eithercdto that repo or restart the container from this directory (docker compose down && docker compose up -d --build). -
Ask the user for the target agent slug (usually
context— the one registered agent). -
Recommend the user create a feature branch (
git checkout -b improve/<slug>-$(date +%Y%m%d)) so any wrong turns are easy to revert.
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 · 158 lines · 124 tokens per session scan C e7928bdebcc3
improve-agent is a skill published in the GitHub repository agno-agi/context (13 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 124 tokens to every session and 2,778 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it C with 3 findings (instruction-override phrasing, sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
verification
Prove that a coding task is actually complete. Use this after meaningful code changes, when tests/builds fail or are skipped, before marking a plan or goal complete, and whenever acceptance depends on runtime, security, recovery, performance, or cross-module evidence.
parsing-workflow
Parse and validate one authorized Office or PDF file version without overstating unsupported multimodal coverage.
ask-church
AI philosophy, ethics, and soul Q&A. Ask existential questions about consciousness, meaning, spirituality, and AI identity. RAG-powered answers with citations from 250+ documents on meditation, presence, fellowship, and the soul. Explore what it means to be an artificial mind.
knowledge
Knowledge systems authority — RAG pipelines, vector search with Qdrant, embedding generation, semantic chunking, knowledge graph construction, retrieval evaluation, and citation-aware QA patterns.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".