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 commands/mturac/everything-openai-codex/hookifygit clone --depth 1 https://github.com/mturac/everything-openai-codexWrote 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/commands/mturac/everything-openai-codex/hookify)<a href="https://agentmods.dev/commands/mturac/everything-openai-codex/hookify"><img src="https://agentmods.dev/badge/commands/mturac/everything-openai-codex/hookify.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 5.1 | $0.00012 | $0.00268 |
| Opus 5 | $0.00006 | $0.00134 |
| Sonnet 5 | $0.00002 | $0.00054 |
| Haiku 4.5 | $0.00001 | $0.00027 |
Grade A, and why
hookify 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 2d 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
Create hook rules to prevent unwanted OpenAI Codex behaviors by analyzing conversation patterns or explicit user instructions.
Usage
/hookify [description of behavior to prevent]
If no arguments are provided, analyze the current conversation to find behaviors worth preventing.
Workflow
Step 1: Gather Behavior Info
- With arguments: parse the user's description of the unwanted behavior
- Without arguments: use the
conversation-analyzeragent to find:- explicit corrections
- frustrated reactions to repeated mistakes
- reverted changes
- repeated similar issues
Step 2: Present Findings
Show the user:
- behavior description
- proposed event type
- proposed pattern or matcher
- proposed action
Step 3: Generate Rule Files
For each approved rule, create a file at .codex/hookify.{name}.local.md:
---
name: rule-name
enabled: true
event: bash|file|stop|prompt|all
action: block|warn
pattern: "regex pattern"
---
Message shown when rule triggers.
Step 4: Confirm
Report created rules and how to manage them with /hookify-list and /hookify-configure.
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.
- 2d ago First seen · 51 lines · 12 tokens per session scan A 091f28868bf4
hookify is a command published in the GitHub repository mturac/everything-openai-codex (89 stars, last pushed 12d ago), licensed MIT. It adds 12 tokens to every session and 268 once invoked, about $0.0001 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 2026-09-03.
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