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 skills/chenchihcu/devspace-mcp/statistical-analysisnpx skills add chenchihcu/DevSpace-MCP --skill statistical-analysisgit clone --depth 1 https://github.com/chenchihcu/DevSpace-MCPWhat 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 | $0.00027 | $0.00299 |
| Opus 5 | $0.00014 | $0.00150 |
| Sonnet 5 | $0.00005 | $0.00060 |
| Haiku 4.5 | $0.00003 | $0.00030 |
Grade A, and why
statistical-analysis 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 yesterday.
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
statistical-analysis
Purpose
Support statistical analysis planning while keeping data requirements, formulas, and interpretation limits explicit.
When to Use
- Defect rate, trend, Pareto, distribution, correlation, or capability analysis.
- Reviewing whether a dataset can support a statistical conclusion.
- Planning data cleaning and formula checks before calculation.
Inputs
- Raw data fields and units.
- Data date range and collection method.
- Cleaning rules and analysis question.
Outputs
- Required field list.
- Cleaning and outlier handling rules.
- Formula and method checklist.
- Evidence-limited conclusions and VERIFY items.
Workflow
- Confirm raw data exists before producing conclusions.
- Define fields, units, filters, and missing data handling.
- Separate descriptive statistics from causal claims.
- Record formula/source assumptions.
Verification
- Check required fields are present.
- Check formulas and grouping logic.
- Check conclusions do not exceed data evidence.
Risks / Limits
- No raw data means no confirmed statistical conclusion.
- Correlation is not causation.
- Formula changes must be disclosed.
Forbidden Actions
- Do not invent datasets.
- Do not hide formula or cleaning assumptions.
- Do not read protected paths or secrets.
Example Task
Plan a Pareto and trend analysis for defect data with required fields and verification steps.
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.
- yesterday First seen · 51 lines · 27 tokens per session scan A 4a5ebf2149b6
statistical-analysis is a skill published in the GitHub repository chenchihcu/DevSpace-MCP (0 stars, last pushed 2mo ago), licensed MIT. It adds 27 tokens to every session and 299 once invoked, about $0.0001 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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