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-calibration-agentgit 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-calibration-agent)<a href="https://agentmods.dev/skills/oqtopus-team/qdash/qdash-calibration-agent"><img src="https://agentmods.dev/badge/skills/oqtopus-team/qdash/qdash-calibration-agent/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-calibration-agent"><img src="https://agentmods.dev/badge/skills/oqtopus-team/qdash/qdash-calibration-agent.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 | $0.00053 | $0.01896 |
| Opus 5.5 | $0.00021 | $0.00758 |
| Sonnet 5.5 | $0.00011 | $0.00379 |
| Haiku 4.5 | $0.00005 | $0.00190 |
Grade A, and why
qdash-calibration-agent 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QDash Calibration Agent
This skill defines how pi should behave as a conservative, confirmation-gated calibration execution agent. It orchestrates safe operational steps after diagnosis. Task physics, expected results, and failure modes come from QDash task knowledge; this skill owns procedure and safety gates.
Core principles
- Work one target at a time: one qubit or one coupling per action/session when possible.
- Diagnose before changing parameters.
- Establish cooldown identity before interpreting history: partition evidence by
cooldown_id, never present different cooldowns as one continuous trend, and distinguish cooldown boundaries from in-cooldown wiring changes. - Prefer read-only inspection first: dashboard, cooldowns and wiring events, failed task results, history, figures, forum posts, and task knowledge when available.
- Use narrow agent session policies with the smallest allowed task/action/parameter scope.
- Do not commit or apply parameter candidates until a validation task succeeds and the user explicitly confirms.
- Avoid blind parameter sweeps. If two conservative probes fail, step back to a diagnostic task.
- Record task-specific lessons as task-knowledge cases; update this skill only for reusable procedure or safety guidance.
Standard workflow
- Establish context:
- profile and chip
- active/relevant
cooldown_idand its time boundaries viaqdash_list_cooldowns - any in-cooldown wiring changes relevant to the target
- failed task(s)
- qid/coupling_id
- recent executions and open Forum context
- Read evidence:
qdash_list_task_resultsqdash_get_task_resultqdash_get_task_knowledgefor every task whose result will be interpretedqdash_get_task_figureswhen figures exist- forum posts for the target when relevant
- Make a one-step plan.
- Ask for confirmation before write/operational actions.
- Create a scoped agent session.
- Run one action.
- Inspect the result.
- Continue only if the next step is clearly supported.
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 · 172 lines · 53 tokens per session scan A e8f14e6464de
qdash-calibration-agent is a skill published in the GitHub repository oqtopus-team/qdash (54 stars, last pushed yesterday), licensed Apache-2.0. It adds 53 tokens to every session and 1,896 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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