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.
git 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/prune)<a href="https://agentmods.dev/commands/mturac/everything-openai-codex/prune"><img src="https://agentmods.dev/badge/commands/mturac/everything-openai-codex/prune/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/commands/mturac/everything-openai-codex/prune"><img src="https://agentmods.dev/badge/commands/mturac/everything-openai-codex/prune.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 | $0.00015 | $0.00174 |
| Opus 5.5 | $0.00006 | $0.00070 |
| Sonnet 5.5 | $0.00003 | $0.00035 |
| Haiku 4.5 | $0.00002 | $0.00017 |
Grade A, and why
prune scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Prune Pending Instincts
Remove expired pending instincts that were auto-generated but never reviewed or promoted.
Implementation
Run the instinct CLI using the plugin root path:
python3 "${CODEX_PLUGIN_ROOT}/skills/continuous-learning-v2/scripts/instinct-cli.py" prune
Or if CODEX_PLUGIN_ROOT is not set (manual installation):
python3 ~/.codex/skills/continuous-learning-v2/scripts/instinct-cli.py prune
Usage
/prune # Delete instincts older than 30 days
/prune --max-age 60 # Custom age threshold (days)
/prune --dry-run # Preview without deleting
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.
- 1mo ago First seen · 32 lines · 15 tokens per session scan A 4fc9c7f78e90
prune is a command published in the GitHub repository mturac/everything-openai-codex (84 stars, last pushed 2mo ago), licensed MIT. It adds 15 tokens to every session and 174 once invoked, about $0.0001 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 2026-09-03.
Other commands, from other repositories
_repeat
Adds --repeat --wait and --tillend --wait inter-session looping to any command. Uses ScheduleWakeup to pause between runs and .monomind/loops/ .json for dashboard tracking.
pair:session
Pair programming session lifecycle — pre-session setup, context setting, mid-session quality checks, breakpoints, end-of-session recording, and multi-session continuity.
monomind-status
Show the monomind statusline (version, git, swarm, security, hooks, token cost).
config
Configure iterm2-tab-status plugin settings (emoji prefixes, colors, display target, subtitle activity, badge, notifications).
pause
Save session state and exit cleanly. Allows resumption later via resume command.
resume
Resume the most recently paused workflow session with automatic session discovery and status update.