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/instinct-status)<a href="https://agentmods.dev/commands/mturac/everything-openai-codex/instinct-status"><img src="https://agentmods.dev/badge/commands/mturac/everything-openai-codex/instinct-status/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/instinct-status"><img src="https://agentmods.dev/badge/commands/mturac/everything-openai-codex/instinct-status.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 | $0.00010 | $0.00166 |
| Opus 5.5 | $0.00004 | $0.00066 |
| Sonnet 5.5 | $0.00002 | $0.00033 |
| Haiku 4.5 | $0.00001 | $0.00017 |
Grade A, and why
instinct-status 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.
This is a copy
84% identical to instinct-status — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Instinct Status Command
Show instinct status from continuous-learning-v2: $ARGUMENTS
Your Task
Run:
python3 "${CODEX_PLUGIN_ROOT}/skills/continuous-learning-v2/scripts/instinct-cli.py" status
If CODEX_PLUGIN_ROOT is unavailable, use:
python3 ~/.codex/skills/continuous-learning-v2/scripts/instinct-cli.py status
Behavior Notes
- Output includes both project-scoped and global instincts.
- Project instincts override global instincts when IDs conflict.
- Output is grouped by domain with confidence bars.
- This command does not support extra filters in v2.1.
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 · 30 lines · 10 tokens per session scan A b0ea513082e2
instinct-status is a command published in the GitHub repository mturac/everything-openai-codex (86 stars, last pushed 1mo ago), licensed MIT. It adds 10 tokens to every session and 166 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 84% identical to instinct-status, differing in 8 lines, and is treated as a copy.
Other commands, from other repositories
teach
Record a scoped, evidence-linked lesson with explicit applicability and uncertainty.
checkpoint
Create an identified recovery point and inspect exact restoration differences.
recall
Retrieve scoped lessons and evaluate their assumptions, conflicts, and applicability.
retro
Write this session's lessons into the project's lessons/ directory using the memory-discipline format.
style-skill-memory
Generate SKILL memory package from style reference for easy loading and consistent design system usage.
memory-compress
Compress the semantic tree by merging similar nodes and removing redundancy while preserving essential information.