Coding Wrapped

Coding Wrapped

Turn local coding-agent history into a private localhost site. Make the default flow short: infer the language, scan standard local sources, generate the site, open it, and explain the controls.

Product contract

1. Local first — Read local session files and write state only to the user's machine. Bind the site to 127.0.0.1.
2. Facts before stories — Derive every claim from aggregate metrics. Never invent a number because it makes a better card.
3. Fun before judgment — Lead with recognizable habits and memorable moments. Give light suggestions, never scores, rankings, or diagnoses.
4. One language — Infer Chinese or English from the invoking prompt and generate the whole experience in that language.
5. Four distinct scenes — Generate insights four at a time. Their pixel-art illustrations must use different compositions, not four variations of a person, a hub, and connecting lines.

This is not a raw transcript viewer, employee-monitoring tool, productivity score, or cloud analytics service.

Runtime

Require Python 3.9+ and a local web browser. The bundled scripts make no outbound network requests and need no separate API key; copy and illustration generation use the active agent when available. Keep the workflow platform-neutral. Do not require a Codex-only or Claude-only tool.

First-run decisions

1. Infer zh or en from the user's current prompt. Do not ask for a language choice unless the prompt is genuinely ambiguous.
2. Reuse the saved display name when config.json exists. Otherwise infer a suitable short name; ask only if no reasonable name is available.
3. Treat an explicit request to scan or generate Coding Wrapped as authorization to read the standard local Claude Code and Codex session directories. If the user only asks what the Skill does, ask before scanning.
4. Do not ask about themes, metrics, layout, or data sources during normal onboarding. Use the defaults and report which sources were found.
5. Use one interface language. Do not expose a language toggle. A later request to change language is a regeneration request.
6. Do not present an implementation plan during the normal first run. Build the site, show it, then let the user react to something concrete.

Run the workflow

Set SKILL_DIR to this Skill folder. Use a custom CODING_WRAPPED_HOME only when the user requests one; otherwise state lives in ~/.coding-wrapped/.

1. Scan local history

Run:

python3 "$SKILL_DIR/scripts/build_metrics.py" \
  --display-name "<name>" \
  --locale "<zh-or-en>"

This creates 7d, 30d, and all aggregate snapshots. It may read:

• ~/.claude/projects/**/*.jsonl
• ~/.codex/sessions/**/*.jsonl

Never copy raw transcript text into the generated site. Read references/privacy-policy.md before changing the scanner or sending any scan-derived payload to a model.

2. Generate the Coding Overview

Run the brief command:

python3 "$SKILL_DIR/scripts/generate_overview.py" brief \
  --output /tmp/coding-wrapped-overview-brief.json

Read the brief. Write one overview in its requested locale with no score or ranking and up to three sourced recommendations. Keep the summary to two sentences: select two or three representative facts instead of replaying every metric, then state the behavior they reveal. Aim for roughly two desktop lines (45–75 Chinese characters or 18–34 English words). Save the result as:

Read references/coding-best-practices.md when writing recommendations. Choose only from the brief's tip_context.candidate_practices. Match the practice to an observed behavior signal and use its localized action as the factual basis. Do not invent a generic recommendation when no candidate fits.

{
  "copy": {
    "<locale>": {
      "eyebrow": "...",
      "title": "...",
      "summary": "...",
      "recommendations": [
        {
          "id": "...",
          "practice_id": "an-allow-listed-practice-id",
          "title": "...",
          "body": "..."
        }
      ]
    }
  }
}

Persist it:

python3 "$SKILL_DIR/scripts/generate_overview.py" persist \
  --input /tmp/coding-wrapped-overview.json

3. Generate exactly four insights

Run:

python3 "$SKILL_DIR/scripts/generate_insights.py" brief \
  --output /tmp/coding-wrapped-insight-brief.json

Read references/insight-writing.md and references/visual-system.md. Read references/coding-best-practices.md when writing each Light Tip. Choose the four most personal and visually expressive findings, not merely the four largest values. Use exactly one allow-listed tip_practice_id per insight; persistence derives its source links from the catalog. A tip must match an observed aggregate behavior signal.

When an image-generation tool is available:

1. Use the cw-pixel-diorama-v1 contract and required prompt prefix from references/visual-system.md.
2. Resolve the matching theme reference under $SKILL_DIR/assets/frontend-template/assets/ and attach it as an actual image reference. If the tool accepts multiple references, also attach agent-orchestra-warm.png to anchor the shared rendering language.
3. Create exactly four 1536 × 1024 PNG illustrations with four different compositions.
4. Run the reference comparison and six-point visual QA gate before persistence. Regenerate a drifting image; do not accept it merely because it is pixel art.
5. Pass each approved absolute PNG path as image_source.

When no image-generation tool is available, omit image_source; the persistence script uses the four approved fallback illustrations. Do not block the site on image generation.

Write a batch matching the requested locale and persist it:

python3 "$SKILL_DIR/scripts/generate_insights.py" persist \
  --input /tmp/coding-wrapped-insights.json

Every batch must contain exactly four insights. The first real batch replaces the demo seed; later batches append and survive page refreshes.

4. Serve and open the site

Run the server as a persistent process:

python3 "$SKILL_DIR/scripts/serve_site.py" --open

Default URL: http://127.0.0.1:4173/.

Use the local browser to confirm:

• the configured name and language appear;
• the Overview loads;
• four generated insights load;
• factual metric cards load;
• generated images resolve;
• the Insight deck changes cards with mouse drag and touch swipe while vertical page scrolling still works;
• the Dashboard scrolls vertically at 1280 × 720, 1440 × 900, and 1024 × 768, without an unexpected horizontal scrollbar;
• export-card routes remain fixed-size and do not shift;
• no raw prompt, code, project name, or local path is visible.

5. Hand off a tiny manual

After generation, give the user the URL and explain only these controls:

• Scan data refreshes factual aggregates and does not regenerate insights.
• Generate insights uses model allowance, usually takes 1–3 minutes, and always adds four.
• Insight deck supports click, arrow keys, mouse drag, and horizontal swipe.
• Customize chooses which factual metric blocks are shown.
• Export creates a portable local archive:

python3 "$SKILL_DIR/scripts/export_wrapped.py"

End with: raw conversations stay out of the website; only aggregates, generated copy, approved source links, and generated illustrations are saved.

Refresh rules

• Refresh facts whenever requested; this is deterministic and model-free.
• Do not regenerate the Coding Overview more than weekly unless the user asks.
• Auto-refresh the Overview only after it is at least seven days old and there are at least three new sessions or twenty new messages.
• Generate new insights only after explicit user action. Warn only that it uses model allowance and usually takes 1–3 minutes.
• Generate four, never one.

Recovery

• If one source directory is missing, continue with the other and report the reduced coverage.
• If both are missing, explain the expected default paths and stop before generating claims.
• If image generation fails, use the fallback illustrations.
• If port 4173 is busy, choose another local port and report the exact URL.
• Preserve existing state on every retry; writes are atomic.