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 Kilo-Org/kilo-marketplace --skill data-investigationgit clone --depth 1 https://github.com/Kilo-Org/kilo-marketplaceWrote 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/kilo-org/kilo-marketplace/data-investigation)<a href="https://agentmods.dev/skills/kilo-org/kilo-marketplace/data-investigation"><img src="https://agentmods.dev/badge/skills/kilo-org/kilo-marketplace/data-investigation/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/kilo-org/kilo-marketplace/data-investigation"><img src="https://agentmods.dev/badge/skills/kilo-org/kilo-marketplace/data-investigation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00055 | $0.01315 |
| Opus 5 | $0.00028 | $0.00658 |
| Sonnet 5 | $0.00011 | $0.00263 |
| Haiku 4.5 | $0.00006 | $0.00131 |
Grade A, and why
data-investigation 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 8d 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 — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Investigation
Use this skill to produce investigations that are fast, correct, reproducible, and communicate a clear conclusion rather than a pile of charts.
Purpose
Every investigation should be answerable in one sentence before the first SQL query is written.
Phase 1: Frame Before Querying
1. Write the one-sentence answer first
Before writing any SQL, write the sentence the conclusion is expected to be.
Example: The cohort size gap is a definition problem rather than a product behavior problem.
If the sentence cannot be written, the question is not yet understood.
2. Classify the investigation type
| Type | Trigger | Approach |
|---|---|---|
| Gap analysis | Why do A and B not match? | Establish the gap, localize it, explain it |
| Root cause | Why did this metric change? | Confirm real, isolate segment, align timing, validate mechanism |
| Hypothesis test | Is X causing Y? | Define what must be true, test sub-claims, confirm or reject |
| Feasibility check | Is this number trustworthy? | Check grain, joins, nulls, definition overlap |
3. State 2-3 competing hypotheses before querying
Never investigate with a single hypothesis. That creates confirmation bias.
Order hypotheses by plausibility and note which one is currently expected to be correct and why.
Phase 2: Build Queries In Escalating Specificity
1. Establish first, explain second
Step 1 always confirms the anomaly is real and measures its magnitude. Do not jump to cause until the effect is confirmed.
select
<time_bucket>,
<source_a_count> as metric_a,
<source_b_count> as metric_b,
<source_a_count> - <source_b_count> as gap,
round(100.0 * (<source_a_count> - <source_b_count>) / nullif(<source_a_count>, 0), 1) as pct_gap
from ...
order by 1
2. Localize by breaking one dimension at a time
After confirming the gap, break it down along one dimension per step:
- By time: when did it appear?
- By segment: who is affected?
- By signal/source: which path is missing?
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.
- 8d ago First seen · 204 lines · 55 tokens per session scan A 2d3905cb2d32
data-investigation is a skill published in the GitHub repository Kilo-Org/kilo-marketplace (175 stars, last pushed 22d ago), licensed Apache-2.0. It adds 55 tokens to every session and 1,315 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 2026-09-03.
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