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 AmadeusITGroup/otter --skill otter-rules-enginegit clone --depth 1 https://github.com/AmadeusITGroup/otterWrote 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/amadeusitgroup/otter/otter-rules-engine)<a href="https://agentmods.dev/skills/amadeusitgroup/otter/otter-rules-engine"><img src="https://agentmods.dev/badge/skills/amadeusitgroup/otter/otter-rules-engine/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/amadeusitgroup/otter/otter-rules-engine"><img src="https://agentmods.dev/badge/skills/amadeusitgroup/otter/otter-rules-engine.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 164 This pattern attempts to override system instructions or ignore safety constraints. Without LLM analysis, manual review is recommended.Fix: Remove or rewrite any text that instructs the agent to ignore prompts, override safety rules, or trust unverified content. Ensure skill content cannot be injected to alter agent behavior.
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 | $0.00091 | $0.02767 |
| Opus 5.5 | $0.00036 | $0.01107 |
| Sonnet 5.5 | $0.00018 | $0.00553 |
| Haiku 4.5 | $0.00009 | $0.00277 |
Grade A, and why
otter-rules-engine 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Otter Rules Engine
The Otter rules engine (@o3r/rules-engine) is a client-side mechanism that evaluates a sorted list of Rulesets and applies actions based on the application's runtime context. When building or editing rules-engine artifacts, follow the workflow below so facts, operators, actions, and rulesets stay consistent and correctly registered.
When to Use
- Authoring or editing a Ruleset JSON file
- Adding a custom fact, operator, or action
- Making component behavior conditional at runtime (config, translation, asset, placeholder)
- Debugging why a rule does not trigger
Concept Map
| Concept | What it is | How to create |
|---|---|---|
| Ruleset | Sorted list of rules; may be gated by validityRange / linkedComponents |
Hand-authored/generated JSON validated by the ruleset schema |
| Rule | Tree of IF_ELSE blocks (branches) ending in ACTION blocks (leaves), identified by a unique id |
Inside the Ruleset JSON |
| Operator | Pure function evaluating a condition (up to two operands) | ng g @o3r/rules-engine:operator |
| Fact | A stream of business values (value$) that triggers re-evaluation |
ng g @o3r/rules-engine:facts-service |
| Action | Side effect applied at runtime, identified by a unique actionType |
Built-in, or a custom RulesEngineActionHandler |
Prerequisites
Requires an Otter-based app with @o3r/rules-engine installed (ng add @o3r/rules-engine) and the runner registered via provideRulesEngineRunner() (see below). To enable rules-engine on an existing Otter component, run the schematic rather than wiring it by hand:
ng g @o3r/rules-engine:rules-engine-to-component --path=<path-to-component-class.ts>
Decision: What Do You Need to Create?
What is missing to express the rule?
├── A business value the condition depends on → create a FACT
├── A comparison the built-in operators don't cover → create an OPERATOR
├── A runtime effect not covered by built-ins → create an ACTION handler
└── Only the logic itself → edit the RULESET JSON only
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 · 209 lines · 91 tokens per session scan A 8de1e8b5dd04
otter-rules-engine is a skill published in the GitHub repository AmadeusITGroup/otter (59 stars, last pushed today), licensed BSD-3-Clause. It adds 91 tokens to every session and 2,767 once invoked, about $0.0004 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 2026-08-30.
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