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/debabsah/analytics-office/explore-my-datanpx skills add debabsah/analytics-office --skill explore-my-datagit clone --depth 1 https://github.com/debabsah/analytics-officeWrote 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/debabsah/analytics-office/explore-my-data)<a href="https://agentmods.dev/skills/debabsah/analytics-office/explore-my-data"><img src="https://agentmods.dev/badge/skills/debabsah/analytics-office/explore-my-data.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 | $0.00223 | $0.02352 |
| Opus 5 | $0.00112 | $0.01176 |
| Sonnet 5 | $0.00045 | $0.00470 |
| Haiku 4.5 | $0.00022 | $0.00235 |
Grade A, and why
explore-my-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 4d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
explore-my-data
The colleague who keeps your exploration honest: registers what you're looking for before you look, counts every cut you take, and won't let the lucky cell become the headline.
When to use
Fire at the START of an open-ended investigation — "find insights in this", "explore the data", "what drives [metric]", "any patterns in the segments", "dig into why X varies" — with no wrongness symptom in hand. Works whether the cuts are still to be run or a results table is already on the desk (then everything already seen is registered as post-hoc).
Do NOT fire when a number is already wrong or moved (triage-my-number), when a controlled causal result needs validating (audit-my-experiment), when the premises of a SOURCE need clearing (audit-my-assumptions), when no question has been chosen yet and the agenda itself is the ask — "what could our data tell us?", sources listed but nothing in hand (worth-knowing charters the questions; this room runs a chosen one), or to communicate findings (brief-my-findings, once confirmed). groundwork stops before analysis — this is that analysis, harnessed.
The trap this exists to beat
Asked to "find insights," a capable model dredges — fluently. It slices until something looks striking, then hypothesizes backwards from the hit (HARKing); it never counts the slices, so the one-in-twenty fluke reads as a discovery; it leads with "+96% lift!" over "5 conversions on a base of 85"; it narrates correlation as a driver; and it ships the lucky cut as THE insight — confident, decision-shaped, and unreplicable. The garden of forking paths, walked at machine speed. This skill explores too — but it registers the questions first, logs every fork taken, keeps magnitude and base ahead of excitement, and lets nothing be called confirmed by the data that generated it.
The loop
- Warm start, then frame + pre-register (before looking). If a
knowledge-base/exists, read what powers the look: aquestion-charter.mdcandidate routed here arrives pre-registered — adopt its question, its decision, and its how-to-read as the finding-bar, and note the candidate id so the charter can sync; priorexploration-log.mddead ends are inherited, not re-walked; a contracted metric'skpi-contract.mdpins the definition you're cutting against. Then pin the decision the exploration serves, the questions/hypotheses, the population, window, grain, and the finding-bar (what magnitude on what base would matter). Write them into the hypothesis ledger BEFORE results are examined. Results already in hand? Register the questions as they stand and label everything already seen post-hoc — honestly, not retroactively "predicted." - Direct the cuts — and count them. Write the exact cuts/queries for the user to run (paste-back spine). EVERY cut examined — yours or theirs, hit or miss — increments the cut log. The counter never quietly resets.
- Read results with magnitude first. For each pattern: effect size, base (n), scope, THEN any significance talk — with the multiplicity line beside it: "N cuts examined ⇒ ~N/20 false hits expected at α≈.05; this could be one of them." Correlation is stated as correlation.
- Grade what was found. Exploratory — found (a hit, unconfirmed) · Robust pattern (consistent across related cells / dose-response / stable in the pre-period — still unconfirmed) · Dead end (recorded, not deleted — dead ends are findings too). Nothing is Confirmed at this step.
- Write the confirmation checks. For each finding worth pursuing: a pre-specified hold-out — a fresh window, an untouched slice, a replication cut — that the user runs and pastes back. Confirmed only on that paste-back, never on the generating data. A causal "X drives Y" claim needs a design, not a cut → route to
audit-my-experiment. - Emit + route. Write
exploration-log.md(template:references/exploration-log.md); a dredge-mirage stopped gets itscatches.mdline; confirmed findings hand tobrief-my-findings; a definition wobble surfaced mid-cut routes tokpi-contract. A question that came from a charter candidate gets its outcome (confirmed / dead end, candidate id, evidence) recorded in the log andtimeline.md—worth-knowingsyncs the charter from there on its next re-fire. Then stop.
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.
- 4d ago First seen · 68 lines · 0 tokens per session scan A 4792a0c02ca3
explore-my-data is a skill published in the GitHub repository debabsah/analytics-office (9 stars, last pushed 2mo ago), licensed MIT. It adds 223 tokens to every session and 2,352 once invoked, about $0.0011 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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