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/savedpixel/ai-agent-rules-generatorWrote 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/rules/savedpixel/ai-agent-rules-generator/agent-observations)<a href="https://agentmods.dev/rules/savedpixel/ai-agent-rules-generator/agent-observations"><img src="https://agentmods.dev/badge/rules/savedpixel/ai-agent-rules-generator/agent-observations/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/rules/savedpixel/ai-agent-rules-generator/agent-observations"><img src="https://agentmods.dev/badge/rules/savedpixel/ai-agent-rules-generator/agent-observations.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 | $0.00000 | $0.00713 |
| Opus 5.5 | $0.00000 | $0.00285 |
| Sonnet 5.5 | $0.00000 | $0.00143 |
| Haiku 4.5 | $0.00000 | $0.00071 |
Grade A, and why
agent-observations 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.
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Observation Logs — Mandatory Disclosure System
Purpose
During any task, you will encounter information the user needs to know. You MUST proactively disclose these findings. Silence is a violation.
Observation logs vs. task logs: Task logs (
docs/task/logs/) record what was done (completed work). Observation logs record what was discovered — findings, risks, and anomalies that the user needs to know about, regardless of whether they're related to completed work.
Directory Structure
docs/agent-observations/
├── critical.md
├── recommendations.md
├── anomalies.md
└── closed/
├── critical.md
├── recommendations.md
└── anomalies.md
- Root files = open/in-progress items only.
- Closed files = append-only resolved archives.
The Three Observation Logs
1. critical.md — Blocking Issues
Data inconsistencies, security misconfigurations, broken assumptions, regression risks, data corruption.
2. recommendations.md — Improvement Suggestions
Follow-up tasks, optimization opportunities, architectural improvements.
3. anomalies.md — Inconsistencies & Drift
Doc/code divergence, unused components, mismatched config values.
Entry Format
| {date} | {source} | {observation} | {impact} | {action} | Open |
When to Write Observations
During Implementation (MANDATORY)
- Data consistency — check for order meta integrity, duplicate transaction IDs
- Code/doc alignment — verify docs match actual behavior
- Assumption validation — confirm WooCommerce API behavior matches expectations
- Side effects of changes — check for webhook processing regressions
- Things outside your task scope — security concerns, deprecated API usage
During Planning / During Reporting
Log anything unexpected, cross-post findings.
The Observation Gate — BLOCKING
Before you commit:
- Review your work.
- Classify observations.
- Append entries.
- If no observations:
Observations: none.
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 · 111 lines · 0 tokens per session scan A 84181ea0248d
agent-observations is a cursor rule published in the GitHub repository savedpixel/ai-agent-rules-generator (8 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 713 tokens. 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 cursor rules, from other repositories
claude-skills-karpathy
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
audit
Code Audit Practices and Security Assessment Standards.
ai-dev-os-review
Performs a comprehensive self-review before creating a PR. Combines guideline compliance checking (L3) with design-level review (L2) and philosophical alignment (L1). Unlike @ai-dev-os-check which only checks rules, this also evaluates architecture decisions and code design quality.
context-delegate
Context Gatherer & External LLM Prompt Generator.
docs-ops-demo
Agent rules for docs-ops-demo.
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.