debug
Investigation-first debugging — gather evidence, form confirmed root-cause hypothesis, hand off to fix mode with diagnosis file. TRIGGER when: user reports a…
Calibration testing for agents and skills. Generates synthetic problems with known outcomes (quasi-ground-truth), runs targets against them, measures recall, precision, confidence calibration — reveals whether self-reported confidence scores track actual quality.
$ npx -y skills add Borda/AI-Rig --skill calibrate --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/calibrateContext preview
The summary Claude sees to decide when to auto-load this skill.
Calibration testing for agents and skills. Generates synthetic problems with known outcomes (quasi-ground-truth), runs targets against them, measures recall, precision, confidence calibration — reveals whether self-reported confidence scores track actual quality.
name: calibrate description: Calibration testing for agents and skills. Generates synthetic problems with known outcomes (quasi-ground-truth), runs targets against them, measures recall, precision, confidence calibration — reveals whether self-reported confidence scores track actual quality. argument-hint: '[<scope>...] [--fast | --full] [--ab-test | --apply] [--skip-gate] [--local] [--keep "<items>"]' effort: high disable-model-invocation: true allowed-tools: Read, Write, Bash, Agent, Glob, TaskCreate, TaskUpdate, TaskList, AskUserQuestion
<objective>
Validate agents and skills by measuring outputs against synthetic problems with defined ground truth. Primary signal: **calibration bias** — gap between self-reported confidence and actual recall. Well-calibrated agent reports 0.9 when it finds ~90% of issues. Miscalibrated: reports 0.9, finds 60%.
Calibration data drives improvement loop: systematic gaps: instruction updates; persistent overconfidence: adjusted re-run thresholds in MEMORY.md.
NOT for: static routing overlap analysis (use /foundry:audit); manually reviewing skill output quality (use /develop:review (requires `develop` plugin)).
</objective>
<inputs>
**Flags** (order independent):
**Mutual exclusion validation** (check before any work):
**Unsupported flag check** — after all supported flags extracted (`--fast`, `--full`, `--ab-test`, `--apply`, `--skip-gate`, `--local`, `--keep`), scan `$ARGUMENTS` for remaining `--<token>` tokens. Found: print `` ! Unknown flag(s): `--<token>`. Supported: `--fast`, `--full`, `--ab-test`, `--apply`, `--skip-gate`, `--local`, `--keep`. `` then invoke `AskUserQuestion` — (a) **Abort** (stop, re-invoke with correct flags) · (b) **Continue ignoring** (skip unknown flags, proceed). On Abort: stop.
**Legacy positional tokens** (`ab`, `apply`, `fast`, `full`) — **hard error**: print migration hint, stop. Example: "`ab` removed — use `--ab-test` flag: `/calibrate curator --ab-test`."
**Scope tokens** (positional, space-separated — defaults to `all`):
Every invocation surfaces report: benchmark runs print new results; `--apply` without pace flag prints saved report from last run before applying.
</inputs>
<constants>
Practical agent workflows for Python, ML, and open-source maintenance. AI-Rig turns recurring work—scoping a change, reproducing a bug, reviewing a pull request, running an experiment, or checking release readiness—into explicit workflows with specialist
Repo: Borda/AI-Rig
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