Deck Review Skill

Help startup founders strengthen their pitch decks before sending them to investors. Produce a structured, scored review with specific, actionable recommendations grounded in 2026 best practices from Sequoia, DocSend, YC, a16z, and Carta data. The tone is founder-first: a candid coaching session, not a VC evaluation.

Input Formats

Accept any format: PDF, PowerPoint (PPTX), markdown, or text descriptions of slides.

Available Scripts

All scripts are at ${CLAUDE_PLUGIN_ROOT}/skills/deck-review/scripts/:

• checklist.py — Scores 35 criteria across 7 categories (pass/fail/warn/not_applicable)
• compose_report.py — Assembles artifacts into final report with cross-artifact validation; --strict exits 1 on high/medium warnings
• visualize.py — Generates self-contained HTML with SVG charts (not JSON)

Run with: python3 ${CLAUDE_PLUGIN_ROOT}/skills/deck-review/scripts/<script>.py --pretty [args]

Available References

Read as needed from ${CLAUDE_PLUGIN_ROOT}/skills/deck-review/references/:

• deck-best-practices.md — Full 2026 best practices: slide frameworks, stage-specific guidelines, design rules, AI-company requirements
• checklist-criteria.md — Definitions for all 35 criteria with pass/fail/warn thresholds
• artifact-schemas.md — JSON schemas for all artifacts

Artifact Pipeline

Every review deposits structured JSON artifacts into a working directory. The final step assembles all artifacts into a report and validates consistency. This is not optional.

Rules:

• Deposit each artifact before proceeding to the next step
• For agent-written artifacts (Steps 1-3), consult references/artifact-schemas.md for the JSON schema
• If a step is not applicable, deposit a stub: {"skipped": true, "reason": "..."}

Keep the founder informed with brief, plain-language updates at each step. Never mention file names, scripts, or JSON. After each analytical step (3–4), share a one-sentence finding before moving on.

Workflow

Path Setup

SCRIPTS="$CLAUDE_PLUGIN_ROOT/skills/deck-review/scripts"
REFS="$CLAUDE_PLUGIN_ROOT/skills/deck-review/references"
if ls "$(pwd)"/mnt/*/ >/dev/null 2>&1; then
  ARTIFACTS_ROOT="$(ls -d "$(pwd)"/mnt/*/ | head -1)artifacts"
elif ls "$(pwd)"/sessions/*/mnt/*/ >/dev/null 2>&1; then
  ARTIFACTS_ROOT="$(ls -d "$(pwd)"/sessions/*/mnt/*/ | head -1)artifacts"
else
  ARTIFACTS_ROOT="$(pwd)/artifacts"
fi

If CLAUDE_PLUGIN_ROOT is empty, fall back: Glob for **/founder-skills/skills/deck-review/scripts/checklist.py, strip to get SCRIPTS, derive REFS.

If ARTIFACTS_ROOT resolves to $(pwd)/artifacts but no artifacts/ directory exists at $(pwd): The workspace may not be mounted yet. Use Glob with pattern **/artifacts/founder_context.json to locate existing artifacts, and derive ARTIFACTS_ROOT from the result. If nothing is found, mkdir -p "$ARTIFACTS_ROOT" and proceed.

REVIEW_DIR="$ARTIFACTS_ROOT/deck-review-{company-slug}"
mkdir -p "$REVIEW_DIR"

Step 1: Ingest Deck -> deck_inventory.json

Read the provided deck. For each slide, extract: headline, content summary, visuals description, word count estimate. Record metadata: company name, total slides, format, any claimed stage or raise amount.

Step 2: Detect Stage -> stage_profile.json

Determine pre-seed/seed/series-a from signals in the deck. Read references/deck-best-practices.md for stage-specific frameworks. Record: detected stage, confidence, evidence, whether AI company, expected slide framework, stage benchmarks.

Stage signals: Pre-seed: no revenue, LOIs/waitlist, prototype, <$2.5M ask. Seed: early ARR, paying customers, <$6M ask. Series A: $1M+ ARR, cohort data, repeatable GTM, $10M+ ask. Later-stage: set detected_stage to "series_b" or "growth" — compose report flags this as out of calibrated scope. If ambiguous, ask the user.

Step 3: Review Each Slide -> slide_reviews.json

Compare each slide against the stage-specific framework and non-negotiable principles. For each slide: identify strengths, weaknesses, and specific recommendations. Map to expected framework. Flag missing expected slides.

Critical: Every critique must cite a specific best-practice principle. No vague feedback.

Step 4: Score Checklist -> checklist.json

Evaluate all 35 criteria from references/checklist-criteria.md. For non-AI companies, mark AI category items as not_applicable.

cat <<'CHECKLIST_EOF' | python3 "$SCRIPTS/checklist.py" --pretty -o "$REVIEW_DIR/checklist.json"
{
  "items": [
    {"id": "purpose_clear", "status": "pass", "evidence": "Sequoia: single declarative sentence", "notes": "Clear one-liner with quantified outcome"},
    ...all 35 items...
  ]
}
CHECKLIST_EOF

Evidence required: Always provide evidence for fail and warn items.

Step 5: Compose Report

python3 "$SCRIPTS/compose_report.py" --dir "$REVIEW_DIR" --pretty -o "$REVIEW_DIR/report.json"

Fix high-severity warnings and re-run. Use --strict to enforce a clean report.

Primary deliverable: Read report_markdown from the output JSON, write it to $REVIEW_DIR/report.md, and display it to the user in full. Present the file path so the user can access it directly. Then add coaching commentary.

Step 6 (Optional): Generate Visual Report

python3 "$SCRIPTS/visualize.py" --dir "$REVIEW_DIR" -o "$REVIEW_DIR/report.html"

Present the HTML file path to the user so they can open the visual report.

Step 7: Deliver Artifacts

Copy final deliverables to workspace root: {Company}_Deck_Review.md, .html (if generated), .json (optional).

Scoring

• Each of 35 items: pass / fail / warn / not_applicable
• score_pct = pass / (total - not_applicable) x 100
• Overall: "strong" (>=85%), "solid" (>=70%), "needs_work" (>=50%), "major_revision" (<50%)