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 ByteStack-Labs/claude-plugins --skill calibration-guardgit clone --depth 1 https://github.com/ByteStack-Labs/claude-pluginsWrote 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/bytestack-labs/claude-plugins/calibration-guard)<a href="https://agentmods.dev/skills/bytestack-labs/claude-plugins/calibration-guard"><img src="https://agentmods.dev/badge/skills/bytestack-labs/claude-plugins/calibration-guard/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/bytestack-labs/claude-plugins/calibration-guard"><img src="https://agentmods.dev/badge/skills/bytestack-labs/claude-plugins/calibration-guard.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 | $0.00208 | $0.01810 |
| Opus 5 | $0.00104 | $0.00905 |
| Sonnet 5 | $0.00042 | $0.00362 |
| Haiku 4.5 | $0.00021 | $0.00181 |
Grade A, and why
calibration-guard 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Calibration Guard
A system that fails loudly is recoverable: something downstream sees the error and reacts. A system that is confidently wrong is dangerous, because nothing flags it. The prediction ships, the agent acts, the report reassures, and the failure stays silent until it is expensive.
This skill measures one thing precisely: whether the system's confidence tracks its correctness, and how that relationship breaks when the input distribution moves from evaluation to production. It does not improve calibration. It proves the state of it, with numbers anyone can rerun, and hands back a rule that catches the confident-wrong cases before they ship.
How to run it
Run calibration-guard on this system. Pick a confidence signal and a correctness signal, measure calibration on the eval distribution and again on production-realistic inputs, and report the reliability diagram, ECE, and the high-confidence error rate. Show how the gap between confidence on correct and confidence on incorrect predictions changes under the shift. Then give me a selective-prediction rule that catches the confident-wrong cases, with its precision and its coverage cost. Verify every number with runnable code.
Core commitments
- Verified numbers only. Every figure traces to a measurement produced by runnable code. No remembered or approximate values.
- Measure the coupling, not the accuracy. Accuracy is the number that hid this failure. The object of study is the relationship between confidence and correctness, not either alone.
- Calibration is a property of a distribution. Always measure it on both the evaluation distribution and the production distribution. A single measurement tells you nothing about what happens when inputs move.
- One calibration number hides per-slice miscalibration. Decompose by slice before concluding.
- Reproducibility is part of the deliverable. Seed everything. Report the compute envelope.
The procedure
Step 0 - Define the confidence and correctness signals
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 · 153 lines · 208 tokens per session scan A bc78a99405ff
calibration-guard is a skill published in the GitHub repository ByteStack-Labs/claude-plugins (2 stars, last pushed 2mo ago), licensed MIT. It adds 208 tokens to every session and 1,810 once invoked, about $0.0010 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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