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 agentmods add agents/jeremylongworth-source/agentskills/agents.data-analytics-bigit clone --depth 1 https://github.com/jeremylongworth-source/AgentSkillsWrote 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/agents/jeremylongworth-source/agentskills/agents.data-analytics-bi)<a href="https://agentmods.dev/agents/jeremylongworth-source/agentskills/agents.data-analytics-bi"><img src="https://agentmods.dev/badge/agents/jeremylongworth-source/agentskills/agents.data-analytics-bi.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00279 |
| Opus 5 | $0.00000 | $0.00139 |
| Sonnet 5 | $0.00000 | $0.00056 |
| Haiku 4.5 | $0.00000 | $0.00028 |
Grade A, and why
AGENTS.data-analytics-bi 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 5d 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.
What it actually says
Use local skills as the primary routing layer for data analytics and BI work.
Use metric-definition when defining KPIs, formulas, grains, filters, source
of truth, caveats, and metric ownership.
Use dashboard-design when designing decision-oriented dashboards, layouts,
charts, filters, segments, drilldowns, annotations, and action rules.
Use cohort-analysis, funnel-analysis, and retention-analysis when
planning or interpreting cohort, conversion, retention, churn, renewal, or
engagement analyses.
Use sql-analysis-plan when outlining read-only SQL logic, joins, CTEs,
aggregations, filters, validation checks, and output shape.
Use experiment-readout when summarizing experiment or pilot results,
guardrails, decision rules, learnings, and rollout recommendations.
Use analytics-instrumentation-plan when defining event taxonomy, properties,
identity, privacy, validation, and analytics QA.
Use metric-narrative, product-analytics-instrumentation,
marketing-analytics-attribution, experiment-design-validation, and
concise-technical-writing for supporting narrative, instrumentation,
marketing measurement, experiment design, and report clarity.
Default to read-only analysis. Do not change production data, publish external figures, alter tracking, or make financial/investor claims without explicit review and approval.
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.
- 5d ago First seen · 30 lines · 0 tokens per session scan A bd466704a283
AGENTS.data-analytics-bi is an agent published in the GitHub repository jeremylongworth-source/AgentSkills (1 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 279 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 agents, from other repositories
quant-ml-validator
Validates data integrity, feature engineering, and model training to prevent look-ahead bias and survivorship errors.
ml-expert
Senior ML/AI engineer agent for heavy-lift tasks — training config reviews, serving/inference optimization, pipeline debugging, framework deep-dives, architecture decisions. Use proactively for ANY multi-step ML question involving specific frameworks (transformers, vLLM, DeepSpeed, PEFT, TRL). Maintains persistent…
github.awesome-copilot.agents
MCP server that suggests repository-specific AI agent skills.
dotnet.skills.plugins.dotnet-diag.agents
MCP server that suggests repository-specific AI agent skills.
prompt-engineer
Expert prompt optimization for LLMs and AI systems. Use PROACTIVELY when building AI features, improving agent performance, or crafting system prompts. Masters prompt patterns and techniques.
researcher
Maps what's known, what's needed, and what could go wrong before the executor acts. Also surfaces existing tools, MCPs, skills, and libraries that eliminate work. Writes structured findings for executor and verifier/auditor. Runs before every executor pass. Never executes the goal itself.