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 nimrodfisher/data-analytics-skills --skill analysis-documentationgit clone --depth 1 https://github.com/nimrodfisher/data-analytics-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/nimrodfisher/data-analytics-skills/analysis-documentation)<a href="https://agentmods.dev/skills/nimrodfisher/data-analytics-skills/analysis-documentation"><img src="https://agentmods.dev/badge/skills/nimrodfisher/data-analytics-skills/analysis-documentation/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/nimrodfisher/data-analytics-skills/analysis-documentation"><img src="https://agentmods.dev/badge/skills/nimrodfisher/data-analytics-skills/analysis-documentation.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 | $0.00033 | $0.00454 |
| Opus 5.5 | $0.00013 | $0.00182 |
| Sonnet 5.5 | $0.00007 | $0.00091 |
| Haiku 4.5 | $0.00003 | $0.00045 |
Grade A, and why
analysis-documentation 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 — 33 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analysis Documentation
When to use
- Finalising an analysis before sharing it with stakeholders
- Handing off an analysis to another team member or to a future self
- Archiving recurring analyses so they can be run again consistently
- Preparing for peer review or a formal audit
- Converting an exploratory notebook into a reference document
Process
- Confirm audience and scope — determine whether the primary reader is technical (data team), business (stakeholders), or both. For mixed audiences, use a tiered structure. See
references/audience_depth_guide.mdfor calibration. - Write the business context section — state the business question, the stakeholders who requested the analysis, the decisions it informs, and the success criteria.
- Document data sources — for each source, record the table or file, date range, row count, key columns, and any known quality issues or exclusions applied.
- Write the methodology section — describe the analytical approach, tools and library versions, key assumptions, and important decisions made (and alternatives considered). Reference the assumptions log if one exists.
- Record results — include key metrics and statistics, embed or link visualisations with descriptive captions, and present findings in order of importance.
- Write the insights, recommendations, and reproducibility section — connect each finding to a business implication and a next action. Document the steps required to reproduce the analysis (data access, environment, execution order). Use
assets/analysis_doc_template.mdas the structure.
Inputs the skill needs
- Final code (SQL, Python, notebook) and outputs (charts, tables)
- Business question and stakeholder context
- Key findings and recommendations already identified
- Data source details (tables, date ranges, sample sizes)
- Library and tool versions used
Output
assets/analysis_doc_template.md— completed analysis document covering context, data, methodology, results, and reproducibility- Linked or embedded visualisations and code references
What ships with it
2 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.
- 1mo ago First seen · 33 lines · 33 tokens per session scan A 2a2743df0b41
analysis-documentation is a skill published in the GitHub repository nimrodfisher/data-analytics-skills (485 stars, last pushed 25d ago), licensed MIT. It adds 33 tokens to every session and 454 once invoked, about $0.0001 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.
Other skills, from other repositories
claude-md-improver
Audit and improve CLAUDE.md files in repositories. Use when user asks to check, audit, update, improve, or fix CLAUDE.md files. Scans for all CLAUDE.md files, evaluates quality against templates, outputs quality report, then makes targeted updates. Also use when the user mentions "CLAUDE.md maintenance" or "project…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
gke-reliability
Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for generating K8s YAML manifests (use gke-manifest-generation) or disaster recovery…
gke-workload-security
Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running security audits (auditcluster.sh), enforcing Network Policies (default-deny and Dataplane V2 logging), isolating high-risk pods inside GKE Sandbox (gVisor), enforcing Pod…
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and…
glance-canonical-layouts
Use this skill to create home screen widgets using Jetpack Glance Canonical Layouts from the Android Platform Samples project, or when asked to implement standard widget layout patterns (including Check List, Image Grid, Snap Scrolling, and Standard Toolbar).