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 notque/vexjoy-agent --skill datagit clone --depth 1 https://github.com/notque/vexjoy-agentWrote 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/notque/vexjoy-agent/data)<a href="https://agentmods.dev/skills/notque/vexjoy-agent/data"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/data/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/notque/vexjoy-agent/data"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/data.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 | $0.00008 | $0.02281 |
| Opus 5.5 | $0.00003 | $0.00912 |
| Sonnet 5.5 | $0.00002 | $0.00456 |
| Haiku 4.5 | $0.00001 | $0.00228 |
Grade A, and why
data 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 25d 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 — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Skill
Two modes. Match the request to a section.
| Signal | Mode |
|---|---|
| Analyze data, CSV, metrics, A/B test, trend, KPI, funnel, distribution | A. Data Analysis |
| Enrich references, generate references, decompose skill, improve depth | B. Reference Enrichment |
A. Data Analysis
Every analysis starts with the decision it supports, works backward to evidence required, then touches the data. Analysis without a decision is arithmetic.
Phase 1: FRAME
Establish what decision this analysis supports.
- Identify the decision, decision-maker, options, and default action if no analysis is done.
- If the user cannot articulate a decision, ask: "What will you do differently based on this analysis?" If exploratory, switch to Exploratory Mode (apply rigor gates, make no causal claims).
- Define evidence requirements: what evidence favors each option, minimum threshold for changing the default, deal-breakers.
- Save
analysis-frame.md.
Gate: Decision identified, options enumerated, evidence requirements saved.
Phase 2: DEFINE
Lock metric definitions before loading data. Defining after seeing data enables cherry-picking.
For each metric: name, exact formula (numerator/denominator), population (included/excluded), time window, segments. For comparisons: define groups and verify fairness.
Save metric-definitions.md. Definitions are locked once Phase 3 starts. If data reveals a definition is unworkable, return here, update, and document the change.
Gate: All metrics defined with formulas and populations.
Phase 3: EXTRACT
Load data. Assess quality. No interpretation.
- Detect tools: try
import pandas; fall back tocsv.DictReader+statistics. - Profile: row count, column types, missing values, date range, distribution stats.
- Quality checks (load
references/rigor-gates.mdGate 1):
| Check | Minimum | If failed |
|---|---|---|
| Sample fraction | Report N of M | Warn if <5% coverage |
| Time window | No gaps >10% | Adjust or note limitation |
| Segment size | 30+ per segment | Merge small segments or exclude |
| Missing rate | <20% per critical column | Impute with disclosure or exclude |
What ships with it
8 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.
- references/decomposition-prompt.md 9.5 KB
- references/enrichment-prompt.md 7.2 KB
- references/output-templates.md 13 KB
- references/preferred-patterns.md 10 KB
- references/quality-rubric.md 6.2 KB
- references/reference-file-template.md 4.0 KB
- references/rigor-gates.md 14 KB
- scripts/gap-analyzer.py 16 KB runs code
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.
- 26d ago Changed · -124 lines · +27 tokens per session 0368fd412bdd
- 27d ago First seen · 225 lines · 8 tokens per session scan A 0fc6b7a05fa9
data is a skill published in the GitHub repository notque/vexjoy-agent (442 stars, last pushed today), licensed MIT. It adds 8 tokens to every session and 2,281 once invoked, about $0.0000 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 2026-09-20.
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