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-two-qubit-calibration-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-two-qubit-calibration-diagnosis)<a href="https://agentmods.dev/skills/oqtopus-team/qdash/qdash-two-qubit-calibration-diagnosis"><img src="https://agentmods.dev/badge/skills/oqtopus-team/qdash/qdash-two-qubit-calibration-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-two-qubit-calibration-diagnosis"><img src="https://agentmods.dev/badge/skills/oqtopus-team/qdash/qdash-two-qubit-calibration-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 | $0.00060 | $0.00891 |
| Opus 5.5 | $0.00024 | $0.00356 |
| Sonnet 5.5 | $0.00012 | $0.00178 |
| Haiku 4.5 | $0.00006 | $0.00089 |
Grade A, and why
qdash-two-qubit-calibration-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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QDash Two-Qubit Calibration Diagnosis
Use this skill when investigating coupling-level calibration quality, especially when tasks are completed but quality may be poor.
Principles
- Treat
completedas execution status, not proof of calibration quality. - Diagnose from evidence before suggesting operational actions.
- Resolve cooldown membership before comparing calibrations or coherence limits. Keep different cooldowns separate, and distinguish a cooldown boundary from an in-cooldown wiring change.
- Prefer read-only tools first:
qdash_list_cooldowns,qdash_target_report,qdash_list_task_results,qdash_get_task_result,qdash_analyze_figure_json, andqdash_get_task_figures. - Do not rerun RB first when validation quality is suspicious. Walk back through prerequisites.
- Do not commit or apply candidates without explicit user confirmation, and only after downstream validation.
Standard read-only investigation
For a coupling cA-B:
- Build context with
qdash_list_cooldowns,qdash_target_report, and recent task results for the coupling. Record the cooldown ID for each result and inspect wiring events when measurement conditions may have changed. - Inspect the latest same-cooldown tasks in this chain by default, and call
qdash_get_task_knowledgefor each task before interpreting its outputs or figures:CheckCrossResonanceCreateZX90CheckZX90CheckBellStateCheckBellStateTomographyCheck2QGateCoherenceLimitZX90InterleavedRandomizedBenchmarking
- Fetch task details and record:
- CR:
cr_amplitude,cr_phase,cancel_amplitude,cancel_phase,rotary_amplitude,zx_rotation_rate - ZX90:
zx90_gate_time - Bell tomography:
bell_state_fidelity - Coherence limit:
two_qubit_gate_coherence_limit - IRB:
zx90_gate_fidelity, its error,zx90_depolarizing_rate,n_trials
- CR:
- For JSON figures, use
qdash_analyze_figure_jsonbefore manually reading raw Plotly JSON. - Summarize symptoms, likely failure mode, and the next read-only or confirmation-gated step.
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 · 70 lines · 60 tokens per session scan A d30a1d66a98b
qdash-two-qubit-calibration-diagnosis is a skill published in the GitHub repository oqtopus-team/qdash (54 stars, last pushed yesterday), licensed Apache-2.0. It adds 60 tokens to every session and 891 once invoked, about $0.0002 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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