---
name: Calibration
slug: calibration
category: Data
description: Calibration partitions table-of-contents page labels into numbering regimes and submits candidate physical-to-printed page offsets for deterministic completion. Use it during document intelligence pipelines to calibrate scanned book anchors without guessing pages.
github: "https://github.com/Ontos-AI/knowhere/tree/main/apps/worker/app/services/document_agent/agents/calibration"
language: Python
stars: 2482
forks: 290
install: "npx degit https://github.com/Ontos-AI/knowhere/tree/main/apps/worker/app/services/document_agent/agents/calibration ~/.claude/skills/calibration"
installs_to: ~/.claude/skills/calibration
source_path: apps/worker/app/services/document_agent/agents/calibration/SKILL.md
collection_size: 1
category_size: 668
added: 2026-08-18T06:57:46.172Z
last_synced: 2026-08-18T06:57:46.172Z
canonical_url: "https://dirskills.com/skills/calibration"
---

# Calibration

Calibration partitions table-of-contents page labels into numbering regimes and submits candidate physical-to-printed page offsets for deterministic completion. Use it during document intelligence pipelines to calibrate scanned book anchors without guessing pages.

**Install:**

```bash
npx degit https://github.com/Ontos-AI/knowhere/tree/main/apps/worker/app/services/document_agent/agents/calibration ~/.claude/skills/calibration
```

## README

# Calibration SubAgent Skill

## Goal

For the **current TOC region**, discover page-numbering **regimes** and an
**initial offset** for each regime that has usable entries. Submit candidate
offsets via `calibration.submit`. After submit, a deterministic completion pass
runs the production tail-verify → binary-search → small-step recalibrate loop
(using the same visual page confirmer as production). Only **complete segments**
are usable for coarse structure; unrecognized pages are treated as **no TOC**.

## Do not use

- Do not scan a fixed window after the TOC (the old “TOC end + N pages” probe).
- Do not invent physical pages you did not inspect or obtain from `link`.
- Do not track a total page-count budget. Limits are **token budgets** and
  **max_rounds** in the payload. A per-call page cap is only a batch-size limit.

## Mandatory first step — partition regimes

Inspect every `page_number` label on the current TOC entries and partition them
into **page-numbering regimes** (distinct numbering systems / label shapes:
decimal digits, roman numerals, prefixed folio labels, etc.).

- Do not mix samples across regimes when computing an offset.
- Run the same initial-calibration procedure independently for each regime that
  has usable entries.

## Phase 1 — Initial offset (your job via tools)

For each regime:

1. Select a small set of entries (prefer spread: early / middle / late when
   enough entries exist).
2. Candidate physical page:
   - If the entry has `link.physical_page`, use it as the primary candidate.
   - Otherwise derive a coarse physical candidate from the printed label and
     `page_count`, then confirm with vision.
3. Progressive `inspect.pages` for that title (start small, expand only if needed):
   - **1st call**: inspect **1** candidate page only.
   - **2nd call** (if miss): inspect up to **3** nearby pages.
   - **3rd call** (if still miss): inspect up to **5** nearby pages.
   Never open with a full 5-page batch when a single page has not been tried.
4. Compute `offset = physical - printed` using this regime’s interpretation of
   the printed label.
5. Submit **candidate** offsets. Do not treat Phase 1 alone as a finished
   coarse-structure calibration.

If `inspect.pages` returns budget exhausted, or rounds run out before a reliable
offset: treat that sample / regime as **not found**, submit whatever regimes you
already confirmed (or `status=failed`), and let production fallback handle the
rest. Do not guess pages.

## Phase 2 — Completion (deterministic after submit)

Not your job and not yours to describe. After submit, production completes each
regime independently (prune → tail verify → binary search → small-step
recalibrate), merges the regimes by physical page, and emits the
`SkeletonAnchor`. It recomputes segment coverage, per-regime status and the
no-TOC entry set itself, so do not submit those.

## Tools

- `inspect.pages`: primary tool for Phase 1. Open physical pages, render, answer
  your question. Prefer the progressive 1→3→5 schedule above. Per-call page
  count is capped; overall spend is limited by the calibration visual token
  budget and `max_rounds`.
- `calibration.submit`: finish Phase 1. Pass the result under
  `tool_args.result` (or result fields directly in `tool_args`).

## Output rules

Submit exactly the fields in the `calibration.submit` schema — `status`,
`regimes`, `notes` — and nothing else:

- Per regime: `kind` and the candidate `offset`. Add `entry_indices` only when
  the regime is not simply the entries whose printed-label shape matches `kind`,
  and `samples` (`title` + `physical`) only for anchors you actually confirmed.
- `notes`: one short sentence saying why. When you found no offset, submit
  `status=failed` and say why in that one sentence.
- Keep `kind` values consistent within one run (`decimal`, `roman`, `prefixed`,
  or `other`).
- Anything else — per-regime status, segment coverage, no-TOC entries, tool call
  counts, region index — is recomputed after submit; emitting it only risks the
  submit being cut off by the output limit, which ends the run with no result.
- Stay within the token / round budgets announced in the payload.