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 RelationalAI/rai-agent-skills --skill rai-discoverygit clone --depth 1 https://github.com/RelationalAI/rai-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/relationalai/rai-agent-skills/rai-discovery)<a href="https://agentmods.dev/skills/relationalai/rai-agent-skills/rai-discovery"><img src="https://agentmods.dev/badge/skills/relationalai/rai-agent-skills/rai-discovery/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/relationalai/rai-agent-skills/rai-discovery"><img src="https://agentmods.dev/badge/skills/relationalai/rai-agent-skills/rai-discovery.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 | $0.00082 | $0.06830 |
| Opus 5 | $0.00041 | $0.03415 |
| Sonnet 5 | $0.00016 | $0.01366 |
| Haiku 4.5 | $0.00008 | $0.00683 |
Grade A, and why
rai-discovery 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 12d 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 — 485 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Question Discovery
Summary
What: Multi-reasoner question discovery from ontology models. Acts as the translation, ideation, and routing layer between the ontology and the reasoners — surfaces what the data can answer, classifies by reasoner family, and translates user-facing problem framings into the technical implementation hints the downstream coding skills consume.
When to use:
- Suggesting questions a given ontology can answer
- Analyzing a user's question to determine reasoner type and feasibility
- Classifying whether a question needs prescriptive, graph, predictive, or rules reasoning
- Identifying multi-reasoner chains (e.g., predict demand then optimize allocation)
- Assessing data feasibility before committing to a workflow
When NOT to use:
- Formulating optimization variables, constraints, objectives — see
rai-prescriptive-problem - PyRel syntax and coding patterns — see
rai-pyrel - Ontology modeling or enrichment — see
rai-ontology - Solver execution and diagnostics — see
rai-prescriptive-results - Post-solve interpretation — see
rai-prescriptive-results
Overview:
- Ground in the real model via
inspect.schema(model)— concepts, properties with real types, relationships, data sources - Analyze the ontology to identify what the data can support
- Classify each opportunity by reasoner type (prescriptive, graph, predictive, rules)
- Identify multi-reasoner chains where applicable
- Assess feasibility (READY / MODEL_GAP / DATA_GAP)
- Present ranked suggestions to the user
- Route the selected question to the appropriate reasoner workflow
Quick Reference
| Signal in Ontology | Reasoner | Question Pattern |
|---|---|---|
| Constrained resources, costs, capacities | Prescriptive | "What should we do?" — allocate, schedule, route. Within prescriptive, formulation splits along a style axis: MIP-style (Problem(model, Float) + HiGHS/Gurobi — continuous-friendly) vs CSP-style (Problem(model, Integer) + MiniZinc — all-integer with globals, multi-solution enumeration, audit/witness). See prescriptive.md § Formulation Style Detection. |
| Network topology, graph structure | Graph | "What patterns exist?" — centrality, clusters, paths |
| Labels/values per entity, historical pair data, graph topology | Predictive | "What will happen?" / "Which Y for each X?" — node classification, node regression, link prediction |
| Threshold/status fields, business rules | Rules | "Is this valid?" — compliance, classification |
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 485 lines · 82 tokens per session scan A f920977c23d1
rai-discovery is a skill published in the GitHub repository RelationalAI/rai-agent-skills (4 stars, last pushed 2d ago), licensed Apache-2.0. It adds 82 tokens to every session and 6,830 once invoked, about $0.0004 per session on Opus 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 2026-08-31.
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