PDF Figure Extract

PDF Figure Extract Skill

Use this skill when the user wants a figure, architecture image, pipeline diagram, qualitative result panel, or other visual artifact extracted from a paper that already exists locally under the current project.

Typical use cases:

• "send me the architecture figure from this paper"
• "extract Figure 3 from the local PDF"
• "return the pipeline overview image"
• "find the model diagram from the paper and send it back"
• "pull out the qualitative comparison panel on page 7"

This skill complements pageindex-grounded, not replaces it. When cached PageIndex artifacts already exist, reuse them to narrow candidate pages before extraction. When PageIndex is missing or inactive, continue in standalone PyMuPDF mode instead of blocking the run.

Mission

Given a request for a figure in an already-downloaded local PDF:

1. Select a bounded local paper set.
2. Reuse PageIndex artifacts when available to narrow likely pages, otherwise shortlist pages directly from the PDF.
3. Extract or render 1-3 figure candidates with PyMuPDF.
4. Inspect those candidates with the agent's multimodal capability when available to confirm which image best matches the user's request.
5. Save the exported artifacts and manifest data under the active research run folder, normally <RUN_ROOT>/figures/.
6. Return the best figure with paper and page context, plus explicit limitations when confidence is reduced.

Prerequisites

• Local PDFs already exist under an active research run folder, normally research/<YYYY-MM-DD>-<topic-slug>/pdf/, or under the legacy flat research/pdf/ layout.
• python3 is installed and available in PATH.
• PyMuPDF is installed:

python3 -m pip install PyMuPDF

Optional but recommended:

• <RUN_ROOT>/pageindex/manifest.json and cached PageIndex trees already exist.
• paper summaries exist under <RUN_ROOT>/pdf/*.md.

If PyMuPDF is missing, only install it when the user explicitly asks for installation or setup work.

Hard Requirements

• Operate only on already-downloaded local PDFs. Do not use this skill for paper discovery.
• Prefer selecting the active research run folder from research/INDEX.md when it exists. If there is no index, infer the best run folder from the user's topic and existing research/*/RUN.md files; fall back to legacy research/pdf/ only for older projects.
• Use projects/SKILLS/pdf-figure-extract/scripts/pdf_figure_extract.py as the canonical local extractor.
• Treat PageIndex as optional acceleration:
  • reuse it when available and relevant
  • do not fail just because PageIndex artifacts are missing
• Keep selection bounded:
  • normally 1 paper for a single-paper request
  • normally 2-3 papers for a cross-paper figure request unless the user explicitly asks for broader coverage
  • normally 1-8 candidate pages per selected paper before exporting images
• Persist artifacts under <RUN_ROOT>/figures/, not under temporary ad hoc folders.
• Export a user-deliverable PNG even when the figure is vector-heavy or mixed-content.
• Before returning the figure, inspect the shortlisted candidate images directly with the active agent's multimodal capability when the provider runtime supports local image inspection.
• If multimodal inspection is unavailable, say so explicitly and fall back to caption, page, and layout heuristics instead of overstating certainty.
• If confidence is low, prefer returning the best candidate or top candidates with limitations rather than pretending the match is exact.
• If the chosen figure should be sent back through Telegram, emit a raw @telegram-file {"kind":"photo","file":"<path>","caption":"optional"} line on its own line with no backticks or code fences. Keep the JSON on one line, keep kind as photo (never image/png/jpg), and on Windows write the path with forward slashes.
• The final reply must identify the paper and page, and include nearby caption text or figure number when available.

OpenColab Progress Helper

OpenColab exposes this progress channel by default during provider runs. When OPENCOLAB_PROGRESS_FILE is available, use this helper:

emit_progress() {
  if [ -z "${OPENCOLAB_PROGRESS_FILE:-}" ]; then
    return 0
  fi
  printf '%s\n' "$1" >> "$OPENCOLAB_PROGRESS_FILE"
}

Useful update categories for this skill:

• selected paper set known
• PageIndex artifacts found or missing
• candidate pages shortlisted
• figure candidates exported
• multimodal verification started or skipped
• degraded standalone fallback
• final figure delivered

Workflow

1. Select the paper set

Use the request plus whatever local artifacts already exist:

• research/INDEX.md
• <RUN_ROOT>/RUN.md
• <RUN_ROOT>/meta/*.json
• <RUN_ROOT>/pdf/*.md
• <RUN_ROOT>/pageindex/manifest.json
• prior <RUN_ROOT>/findings.md

Selection guidance:

• exact single-paper request: 1 paper
• "compare the architecture figures in these two papers": 2 papers
• broader but still bounded figure request: 2-3 papers

2. Prepare the figure workspace

RUN_ROOT="research/<YYYY-MM-DD>-<topic-slug>"
mkdir -p "$RUN_ROOT/figures"/{exports,manifests,notes}

3. Run the extractor

Standalone or auto mode:

python3 projects/SKILLS/pdf-figure-extract/scripts/pdf_figure_extract.py \
  --pdf-path "$RUN_ROOT/pdf/<safe_id>.pdf" \
  --query "architecture figure" \
  --output-root "$RUN_ROOT/figures" \
  --top-k 3

PageIndex-assisted mode when the cached tree is already known:

python3 projects/SKILLS/pdf-figure-extract/scripts/pdf_figure_extract.py \
  --pdf-path "$RUN_ROOT/pdf/<safe_id>.pdf" \
  --query "architecture figure" \
  --pageindex-tree "$RUN_ROOT/pageindex/trees/<safe_id>.json" \
  --output-root "$RUN_ROOT/figures" \
  --top-k 3

Direct figure or page hint mode:

python3 projects/SKILLS/pdf-figure-extract/scripts/pdf_figure_extract.py \
  --pdf-path "$RUN_ROOT/pdf/<safe_id>.pdf" \
  --query "Figure 3" \
  --figure-number 3 \
  --page-hint 5 \
  --output-root "$RUN_ROOT/figures" \
  --top-k 2

The script writes a per-run manifest under $RUN_ROOT/figures/manifests/ and updates $RUN_ROOT/figures/manifest.json with the latest run summary.

4. Verify the candidates multimodally

After extraction:

1. Read the manifest and note the top 1-3 candidate image paths.
2. Inspect those local images directly with the active provider's multimodal capability.
3. Check whether the image actually matches the request:
   • architecture or pipeline overview,
   • the requested figure number,
   • the nearby caption or page context,
   • the expected visual content.
4. Choose the best candidate only after that inspection step.

If the provider runtime cannot inspect local images, say so explicitly and rely on the manifest, page context, caption text, and extraction score as a degraded fallback.

5. Write an optional note

For non-trivial figure retrieval, write:

• $RUN_ROOT/figures/notes/<date>-<topic-slug>.md

Recommended structure:

# Figure Extraction Note: <topic>

## Request

...

## Selected Paper

...

## Extraction Mode

- `pageindex-assisted` or `standalone`

## Chosen Figure

- file: `$RUN_ROOT/figures/exports/...`
- page: ...
- caption: ...

## Limitations

...

6. Return the result

The user-facing reply should:

• answer directly
• identify the selected paper and page
• say whether the result came from pageindex-assisted or standalone
• mention when the returned artifact is a clipped page render rather than a direct embedded-image extraction if that matters
• surface low-confidence matching, missing multimodal verification, missing PageIndex artifacts, or other limitations when they affect confidence
• point to the saved note when one was written

If returning the figure through Telegram, emit the raw @telegram-file line after the short reply.

Output Contract

• <RUN_ROOT>/figures/manifest.json for the latest run summary
• <RUN_ROOT>/figures/manifests/<slug>.json for the per-run manifest
• <RUN_ROOT>/figures/exports/<slug>__p<page>__cand<rank>.png for shortlisted figure candidates
• optional raw extracted image files when the source figure was directly embedded
• optional <RUN_ROOT>/figures/notes/<date>-<topic-slug>.md
• a concise final reply with paper, page, and limitations

Canonical Assets

• Skill doc: projects/SKILLS/pdf-figure-extract/SKILL.md
• Python extractor: projects/SKILLS/pdf-figure-extract/scripts/pdf_figure_extract.py