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 ericrisco/rsc-harness --skill business-intelligencegit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/business-intelligence)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/business-intelligence"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/business-intelligence/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/ericrisco/rsc-harness/business-intelligence"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/business-intelligence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00090 | $0.02455 |
| Opus 5 | $0.00045 | $0.01228 |
| Sonnet 5 | $0.00018 | $0.00491 |
| Haiku 4.5 | $0.00009 | $0.00246 |
Grade A, and why
business-intelligence 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 9d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Business intelligence
Answer business questions over the org's data through a governed semantic layer — define each metric once in versioned YAML, then route every "what was revenue last quarter by region" through that layer. Numbers come out consistent, auditable, and the same for everyone. This skill builds the layer and queries it in plain language.
The one rule
Never free-hand SQL against raw tables to answer a governed business question. Go through the layer.
Why, with numbers: in dbt's April 2026 benchmark (ACME Insurance, 11 questions × 20 runs, ~15-table schema), an LLM grounded in a semantic layer scored 98.2% (Claude Sonnet 4.6) / 100% (GPT-5.3 Codex) vs 90.0% / 84.1% for raw text-to-SQL on the same schema; on the unmodeled schema it was 72.7% vs 64.5%, and a 2023 GPT-4 baseline managed 32.7%. The layer is not bureaucracy — it is the accuracy. The model writing SQL against undecorated tables is the failure mode you are eliminating.
Your job is two motions: (1) build the metrics layer (entities, dimensions, measures, metrics) and (2) query it — translate a plain-language question into metric + dimensions + grain + filter, never into a hand-written query.
The four primitives
Every semantic layer (MetricFlow, Cube, warehouse-native) is built from the same four nouns. Learn these and the rest is syntax.
- Entities — the join keys.
order_idis the primary entity oforders;customer_idis a foreign entity that joins tocustomers. Entities are how the layer knows how tables relate so it writes the join, not you. - Dimensions — the axes you group and filter by, including time grains (
order_dateby day/week/month/quarter) and categoricals (region,product_category). - Measures — a single aggregation of a column:
sum(amount),count(distinct customer_id). - Metrics — named, reusable expressions built over measures:
gross_revenue,mrr,gross_margin_pct. This is what a human or agent actually asks for by name.
What ships with it
5 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.
- 9d ago First seen · 163 lines · 90 tokens per session scan A fa671f05f24d
business-intelligence is a skill published in the GitHub repository ericrisco/rsc-harness (74 stars, last pushed yesterday), licensed MIT. It adds 90 tokens to every session and 2,455 once invoked, about $0.0005 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 2026-08-30.
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