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 oqtopus-team/qdash --skill qdash-timeseries-diagnosisgit clone --depth 1 https://github.com/oqtopus-team/qdashWrote 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/oqtopus-team/qdash/qdash-timeseries-diagnosis)<a href="https://agentmods.dev/skills/oqtopus-team/qdash/qdash-timeseries-diagnosis"><img src="https://agentmods.dev/badge/skills/oqtopus-team/qdash/qdash-timeseries-diagnosis/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/oqtopus-team/qdash/qdash-timeseries-diagnosis"><img src="https://agentmods.dev/badge/skills/oqtopus-team/qdash/qdash-timeseries-diagnosis.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 | $0.00064 | $0.01271 |
| Opus 5.5 | $0.00026 | $0.00508 |
| Sonnet 5.5 | $0.00013 | $0.00254 |
| Haiku 4.5 | $0.00006 | $0.00127 |
Grade A, and why
qdash-timeseries-diagnosis 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 2d 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QDash Timeseries Diagnosis
Use this skill for evidence-based multi-timeseries investigation. Keep the measurement schema and physical interpretation separate: the comparison tool extracts mechanical evidence, while this skill guides conclusions and next checks.
Principles
- Do not hard-code metric names, targets, sensors, units, CSV columns, periods, or timezone assumptions.
- Preserve both raw and transformed results. A visually clean transformed plot must not replace the raw evidence.
- Correlation, shared periodicity, and phase alignment establish association, not causal direction.
- Consider a shared driver when multiple targets and an environmental sensor move together. The sensor itself may be a proxy for an unmeasured actuator, vibration, electrical noise, magnetic noise, or control cycle.
- Count observed cycles, not raw sample points, when judging periodic evidence.
- Resolve cooldown boundaries before interpreting drift or shared periodicity. Analyze each cooldown separately by default; never smooth, interpolate, correlate, or fit a period across a cooldown boundary unless an explicit cross-cooldown comparison is intended and clearly reported.
Workflow
When a compared QDash series comes from a task-specific output, call qdash_get_task_knowledge for that task before assigning physical meaning to its values, expected behavior, or failure modes.
1. Fix the investigation scope
Record the reusable scope with /qdash-investigation-setup when multiple
comparison passes are expected. The JSON context accepts arbitrary QDash
series, CSV mappings, window/timezone, and transform defaults. Record:
- QDash profile and chip
- cooldown IDs and start/end boundaries from
qdash_list_cooldowns - any in-cooldown wiring events that overlap the window
- metrics and target IDs
- tags
- start/end timestamps
- timezone for every source
- expected cadence and units
- local CSV path, time column, value columns, and any row filters
For timestamps without an explicit zone, require a declared offset. Never infer a timezone from the host machine.
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.
- 2d ago First seen · 158 lines · 64 tokens per session scan A 6400cb0d67cb
qdash-timeseries-diagnosis is a skill published in the GitHub repository oqtopus-team/qdash (54 stars, last pushed yesterday), licensed Apache-2.0. It adds 64 tokens to every session and 1,271 once invoked, about $0.0003 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 2026-10-28.
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