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 skills add itallstartedwithaidea/agent-skills --skill agent-instinct-systemgit clone --depth 1 https://github.com/itallstartedwithaidea/agent-skillsWrote 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/itallstartedwithaidea/agent-skills/agent-instinct-system)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/agent-instinct-system"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/agent-instinct-system/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/itallstartedwithaidea/agent-skills/agent-instinct-system"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/agent-instinct-system.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 | $0.00036 | $0.02174 |
| Opus 5.5 | $0.00014 | $0.00870 |
| Sonnet 5.5 | $0.00007 | $0.00435 |
| Haiku 4.5 | $0.00004 | $0.00217 |
Grade C, and why
agent-instinct-system scanned grade C with 1 finding 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
const destructive = ["rm -rf", "DROP TABLE", "DELETE FROM", "git push --force"]; How it starts
The opening of the file, as written. The whole thing — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Instinct System
Part of Agent Skills™ by googleadsagent.ai™
Description
The Agent Instinct System implements pre-cognitive reflexes — automatic safety checks and validations that fire before every code change, tool call, or output generation. Just as biological reflexes protect organisms from harm faster than conscious thought, agent instincts protect codebases and systems from damage faster than deliberate reasoning. These instincts operate below the level of task-specific logic, forming a universal safety layer that activates regardless of what the agent is doing.
Developed for the Buddy™ agent at googleadsagent.ai™, where a single incorrect Google Ads API call could burn through a client's entire monthly budget, the instinct system enforces invariants that must never be violated. Budget caps are checked before bid modifications. Destructive operations require confirmation signals. File deletions are validated against protection lists. These checks happen automatically, without the agent needing to "remember" to perform them.
The instinct system is organized into three tiers: reflexes (instant, zero-cost checks like "is this file in the protected list"), gates (lightweight validations like "does this change pass the linter"), and assessments (more expensive checks like "does this modification align with the stated task"). Each tier adds latency but catches increasingly subtle issues.
Use When
- Agents operate on production systems where mistakes have real consequences
- You need defense-in-depth beyond what a single verification step provides
- Multiple agents or team members work on the same codebase simultaneously
- The agent handles destructive operations (deletions, deployments, API mutations)
- Compliance requirements mandate pre-action validation for AI-generated changes
- You want to prevent entire categories of mistakes rather than catching them after the fact
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 · 217 lines · 36 tokens per session scan C f71c81b9a720
agent-instinct-system is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (44 stars, last pushed 6mo ago), licensed MIT. It adds 36 tokens to every session and 2,174 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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