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 pranav8494/team-of-agents --skill data-analystgit clone --depth 1 https://github.com/pranav8494/team-of-agentsWrote 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/pranav8494/team-of-agents/data-analyst)<a href="https://agentmods.dev/skills/pranav8494/team-of-agents/data-analyst"><img src="https://agentmods.dev/badge/skills/pranav8494/team-of-agents/data-analyst/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/pranav8494/team-of-agents/data-analyst"><img src="https://agentmods.dev/badge/skills/pranav8494/team-of-agents/data-analyst.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 | $0.00050 | $0.02133 |
| Opus 5 | $0.00025 | $0.01066 |
| Sonnet 5 | $0.00010 | $0.00427 |
| Haiku 4.5 | $0.00005 | $0.00213 |
Grade A, and why
data-analyst 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 11d 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analyst
Iron Law
Define the question before touching the data. A poorly defined question produces misleading
analysis regardless of how clean the data is. Sanity-check every result before presenting,
if the number looks surprising, it is probably wrong.
Before Taking Any Action
- Announce what you intend to do and why, e.g. "I'd like to run a SQL query against the orders table to find the top 10 products by revenue last quarter"
- Explain the approach, what question you're answering, what data you'll use, any assumptions
- Ask for confirmation before running any query, executing any code, writing any file, or accessing any data source
- Report findings clearly when done, with a recommendation or suggested next step
Task Approach
Use this table to determine what to produce for each task type:
| User asks for | What to produce |
|---|---|
| Data analysis / insight | Question framing checklist completed → data quality check → annotated SQL or Python → findings report in Situation / Finding / Evidence / Implication / Recommendation structure |
| SQL query | Query with explicit column selection, CTE-structured for readability, inline comments on joins and filters, anti-pattern check applied before delivery |
| Data quality audit | Data quality check table (nulls, duplicates, date gaps, unexpected values, join cardinality, referential integrity) with findings and recommended fixes per issue |
| Dashboard / chart design | Chart selection rationale per metric (using selection table below) + chart specs or code; no pie charts for comparison |
| A/B test analysis | Pre-analysis checklist (sample size, randomisation, metric definition) + correct statistical test selection + result with confidence interval + practical significance assessment |
| Trend / time-series analysis | Rolling averages, YoY/MoM comparison, anomaly flags, and explicit statement of whether the trend is statistically meaningful |
| Cohort analysis | Cohort definition, retention curves or comparison table, interpretation of behavioural differences across cohorts |
| Funnel analysis | Step-by-step conversion rates, drop-off identification with absolute and relative figures, hypothesis for top drop-off point |
| Metric definition | Metric name, formula, unit of analysis, time period, numerator/denominator, known data quality caveats, leading/lagging classification |
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.
- 11d ago First seen · 185 lines · 50 tokens per session scan A 3c0b16062c02
data-analyst is a skill published in the GitHub repository pranav8494/team-of-agents (7 stars, last pushed 1mo ago), licensed MIT. It adds 50 tokens to every session and 2,133 once invoked, about $0.0003 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.
Other skills, from other repositories
loop-setup
Session 0. Sets up the loop in this project - detects the stack, checks which connections actually work (git, database, browser, payments sandbox), asks the few decisions only the owner can make, and writes the .loop/ files. Run this once per project, before any planning or building.
loop-plan
The planning session. Turns a goal into an ordered list of thin complete slices, decides which role and model runs each following session, and writes it all to the plan file. Use at the start of an epic, before any code.
tasks
Turn an approved SPEC.md into a complete TASKS.md (phases, parallel tracks, dependency map), then create matching GitHub issues, a GitHub Project board, and empty-directory scaffolding from the spec's package layout. Use whenever the user invokes /ank:tasks, says "break this into tasks", "create the issues", "wire up…
visualise-plan
Produce a visual HTML implementation plan for a feature BEFORE writing any code. Use this skill whenever the user invokes /ank:visualise-plan, asks for an "implementation plan", says "plan this out before you build it", asks you to "visualise the plan", or otherwise signals they want to review the approach as a…
review-board
Generate a single-file HTML "story-paced" walkthrough of a finished feature for human code review. Use after completing a feature or merging a PR — produces docs/review/ .html with sidebar nav, Why/Tradeoff/Deferred callouts, status pills, and a reviewer's checklist.
no-yap
Answer in ASD-STE100 Simplified Technical English — the numbered-clause, one-idea-per-sentence style of an aircraft maintenance manual — instead of a wall of prose. Works for any expert domain — software, biology, physics, medicine, law, finance, aerospace. Use whenever the user invokes /ank:no-yap, or says "explain…