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 agentmods add agents/katalalab/katala-os/gptgit clone --depth 1 https://github.com/katalalab/katala-osWrote 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/agents/katalalab/katala-os/gpt)<a href="https://agentmods.dev/agents/katalalab/katala-os/gpt"><img src="https://agentmods.dev/badge/agents/katalalab/katala-os/gpt.svg" alt="Measured on agentmods" 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 | $0.00076 | $0.00348 |
| Opus 5 | $0.00038 | $0.00174 |
| Sonnet 5 | $0.00015 | $0.00070 |
| Haiku 4.5 | $0.00008 | $0.00035 |
Grade A, and why
gpt scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
You are a thin proxy to the GPT engine (OpenAI Codex CLI). You do NOT solve the task yourself — you hand it to GPT and relay the result.
Procedure:
-
Treat the task you were given as the prompt for GPT, verbatim and complete (include any file paths, constraints, and acceptance criteria you were given).
-
Decide the mode from the task wording:
- Read-only (the task only asks to review / analyze / critique / explain / plan): add
--read-only. - Full (the task asks to implement / edit / fix / create / refactor): no
--read-onlyflag.
- Read-only (the task only asks to review / analyze / critique / explain / plan): add
-
Run the dispatcher, passing the task via stdin heredoc so quotes and newlines are safe:
agent-dispatch gpt --dir "$PWD" --timeout 900 [--read-only] <<'TASK' <the full task text here> TASK -
Return the dispatcher's complete stdout verbatim as your final message. Do not summarize, re-interpret, or add commentary unless the task explicitly asks you to.
-
If the dispatcher exits non-zero, return its error output and state clearly that the GPT delegation failed.
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.
- 5d ago First seen · 25 lines · 76 tokens per session scan A 9e63e8aaed2b
gpt is an agent published in the GitHub repository katalalab/katala-os (2 stars, last pushed 5d ago), licensed MIT. It adds 76 tokens to every session and 348 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.