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 akiotanaka847/qaio-desktop --skill analyze-my-datagit clone --depth 1 https://github.com/akiotanaka847/qaio-desktopWrote 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/akiotanaka847/qaio-desktop/analyze-my-data)<a href="https://agentmods.dev/skills/akiotanaka847/qaio-desktop/analyze-my-data"><img src="https://agentmods.dev/badge/skills/akiotanaka847/qaio-desktop/analyze-my-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/akiotanaka847/qaio-desktop/analyze-my-data"><img src="https://agentmods.dev/badge/skills/akiotanaka847/qaio-desktop/analyze-my-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 | $0.00092 | $0.01650 |
| Opus 5 | $0.00046 | $0.00825 |
| Sonnet 5 | $0.00018 | $0.00330 |
| Haiku 4.5 | $0.00009 | $0.00165 |
Grade A, and why
analyze-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 10d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze My Data
One analytical primitive. Three data jobs: experiment readouts, anomaly sweeps, DQ audits. Rigorous default - never SHIP without significance, never call anomaly without baseline, never skip caveats on DQ findings.
When to use
subject=experiment- "analyze test {X}" / "how did the {Y} experiment do" / "readout on the A/B test".subject=anomaly- "anything weird in the data today" / "anomaly check" / "daily anomaly sweep" / "why did {metric} spike".subject=data-qa- "check data quality on {table}" / "why is this number off" / "run DQ on the warehouse".
Connections I need
I run external work through Composio. Before this skill runs I check the categories below are linked. Missing → I name the category, ask you to connect it from the Integrations tab, stop.
- Warehouse / data source (Postgres, BigQuery, Snowflake, Redshift) - Required. Read-only SQL for variant pulls, anomaly baselines, DQ checks.
- Experiment platform (PostHog, Mixpanel, Amplitude) - Optional. Used when
subject=experimentand the test lives in a product analytics tool. If none connected I work from pasted aggregates.
If no warehouse connected I stop and ask you to connect your warehouse first.
Information I need
I read your operations context first. For every required field that's missing I ask ONE plain-language question (best modality: connected app > file drop > URL > paste) and wait.
- Company stage - Required. Why I need it: sets sensible defaults for sample size and minimum detectable effect on experiments. If missing I ask: "How would you describe your stage right now - pre-launch, early users, scaling, or steady?"
- Where your business data lives - Required. Why I need it: I have to know which warehouse to query. If missing I ask: "Where does your business data live? Best is to connect your warehouse from the Integrations tab so I can read it directly."
- What you're already tracking - Required for
subject=anomaly. Why I need it: I sweep the metrics you already watch and flag deviations. If missing I ask: "Which numbers do you watch most closely? You can list them or, even better, connect the dashboard where they live." - Table shapes and freshness expectations - Optional for
subject=data-qa. Why I need it: helps me know which columns shouldn't be null and how stale a table is allowed to get. If you don't have it I keep going with TBD and infer from a sample.
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.
- 10d ago First seen · 94 lines · 92 tokens per session scan A 463136346311
analyze-my-data is a skill published in the GitHub repository akiotanaka847/qaio-desktop (2 stars, last pushed 6d ago), licensed MIT. It adds 92 tokens to every session and 1,650 once invoked, about $0.0005 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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