Happy App Audit

Happy App Audit

Static-only macOS app telemetry auditor. Produces a markdown report describing what an installed .app bundle reports, to whom, how often (inferred), and what it leaves on disk.

When to invoke

Invoke when the user says any of: "审计 / 调查 / 看看 / 拆 / 逆向 / 上报 / 埋点 / 隐私 / 抓 SDK" combined with a .app path or app name. Also invoke when given paths under /Applications, ~/Applications, /Library/Input Methods, or /Library/PrivilegedHelperTools.

Do NOT invoke for: source-code repos, web sites, mobile (iOS/Android) packages — this skill is macOS-bundle specific.

Hard rules (non-negotiable)

• Read only. No curl/wget/nc/dig against discovered endpoints. No lldb attach, dtrace, fs_usage, tcpdump, mitmproxy, frida. No Keychain reads. No DRM bypass. No memory dump.
• Allowed commands only. See references/safe_commands.md. If a step seems to need something outside the whitelist, stop and tell the user instead of improvising.
• Privacy by default. In every output file, scrub device_id, uid, session_id, email, IDFV, IDFA, JWT, and any 16+ hex blob to `` (keep length, drop content).
• Scope cap. Refuse a single invocation that targets more than 5 apps. Refuse paths under /System/, /usr/libexec/, /private/var/db/com.apple.*. Those are OS components, not third-party telemetry targets.

Runtime

{baseDir} = directory of this SKILL.md.

All scripts are bun + TypeScript. Resolve runtime as: prefer bun in PATH, otherwise npx -y bun. If neither exists, abort with a one-line install hint.

# Smoke check
bun --version  ||  npx --version  ||  echo "Need bun (recommended) or npx"

Workflow — 6 phases, in order

Each phase has: Goal → Inputs → Commands → Output → Stop conditions. Do not skip ahead. Do not interleave.

Phase 0 — Scope confirm

Goal. Lock the target list to ≤5 valid .app paths.

Inputs. Whatever the user said — could be a path, a name, or "the input methods I have installed."

Commands.

• If user gave a path → verify it exists and ends with .app
• If user gave a name → search a fixed list:

``bash /Applications (depth 2) ~/Applications (depth 2) /Library/Input Methods (depth 1) /Library/PrivilegedHelperTools (depth 1) ``

• Reject anything under /System/, /usr/libexec/, /private/var/db/com.apple.*

Output. A list target_apps[] with absolute paths.

Stop. If the list is empty, ask the user once. If >5, ask which to keep.

Phase 1 — Metadata snapshot

Goal. Per app, capture the immutable surface: bundle id, version, signing, entitlements, network policy, embedded frameworks.

Inputs. target_apps[] from Phase 0.

Commands. Run scripts/snapshot_app.ts:

bun {baseDir}/scripts/snapshot_app.ts  --out /meta.json

The script collects:

• plutil -p /Contents/Info.plist
• codesign -dv --entitlements - (stderr)
• find /Contents/Frameworks -maxdepth 3 -name '*.dylib' -o -name '*.framework'
• otool -L
• file for arch
• Sizes via du -sh

Output. /meta.json with: bundle_id, version, sandboxed, arbitrary_loads, ats_exceptions[], entitlements_summary[], frameworks[] (each: name, path, size_bytes, archs).

Stop. If bundle_id cannot be read → abort, app is malformed.

Phase 2 — Strings preprocessing

Goal. Turn raw strings of every embedded binary into bucketed markdown that fits in context.

Inputs. meta.json::frameworks[].

Commands.

bun {baseDir}/scripts/classify_strings.ts /meta.json --out /strings/

For each binary, the script runs strings -a -n 6 and sorts each line into one of:

• urls — anything matching https?://
• domains — bare hostnames
• paths — /Library/..., ~/Library/..., container-relative paths
• sql — CREATE TABLE, INSERT INTO, SELECT ... FROM
• events — looks like an event name (/^[a-z][a-z0-9_]{8,80}$/ with at least one underscore)
• keys — base64 / hex blobs ≥ 24 chars (kept count + first 12 chars only, never full)
• noise — discarded

Output. /strings/.{urls,domains,paths,sql,events}.md (the keys bucket holds only counts + redacted previews).

Stop. If a binary is >200 MB → skip it and emit a warning line, do not OOM.

Phase 3 — SDK fingerprint matching

Goal. Identify which third-party SDKs are present and how confident.

Inputs. /strings/, plus references/sdk_fingerprints.md.

Commands.

bun {baseDir}/scripts/match_fingerprints.ts /strings/ \
  --fingerprints {baseDir}/references/sdk_fingerprints.md \
  --out /matched.md

The script applies each fingerprint's tell-tale strings regex set to the bucketed strings. A fingerprint counts as confirmed when its min_hits threshold is met (defined per fingerprint).

Output. /matched.md with one row per SDK: name, vendor, hits, evidence file lines, status (confirmed / partial / absent).

Stop. If zero fingerprints confirmed AND the app embeds no third-party .framework → write a one-line "no telemetry detected" report and skip Phase 4-5.

Phase 4 — Endpoint mapping

Goal. Build the table that answers "where does it talk to, with what protocol, for what purpose, how often?"

Inputs. /strings/*.urls.md + /strings/*.domains.md + /matched.md + references/known_endpoints.md.

Commands. This phase is mostly Claude reading the files. The only mechanical step:

bun {baseDir}/scripts/match_fingerprints.ts /strings/ \
  --fingerprints {baseDir}/references/known_endpoints.md \
  --out /endpoints.md

Then Claude writes /endpoint_table.md: | Endpoint | SDK | Protocol | Inferred purpose | Frequency source | |----------|-----|----------|------------------|------------------|

Frequency source MUST cite either: a literal interval found in /strings/, or a config file found in Phase 5, or "unknown — not stated in static evidence." Never guess.

Stop. If endpoints.md is empty but Phase 3 confirmed an SDK → flag in the report (likely runtime-resolved hosts).

Phase 5 — Local data dive

Goal. Inventory the on-disk surface that the app writes to.

Inputs. meta.json::bundle_id, plus references/data_locations.md.

Commands.

bun {baseDir}/scripts/inventory_data.ts  --out /local_data.md

The script finds:

• ~/Library/Application Support//
• ~/Library/Containers//Data/
• ~/Library/Group Containers/group..*/
• ~/Library/Caches//
• ~/Library/Preferences/.plist
• ~/Library/Logs//

For each .sqlite* file: sqlite3 '.schema' and .tables only — never SELECT. For each .mmkv / .json config: list path + size, do not open.

Output. /local_data.md with: tree of relevant paths, sizes, and SQLite schemas.

Stop. If user is not the file owner → skip with a note (do not prompt sudo).

Phase 6 — Report rendering (+ optional 4:5 card)

Goal. Assemble the user-facing report. Optionally render a 4:5 infographic card for sharing.

Inputs. All prior phase outputs.

Commands.

bun {baseDir}/scripts/render_report.ts  \
  --template {baseDir}/templates/report.md.tmpl \
  --out ~/Documents/app-telemetry-audit/_/report.md

When --card is passed to scripts/run.ts, the orchestrator additionally:

1. Calls lib/card.ts::renderCardPrompt(), which extracts top-6 SDKs (by size, with privacy-hot ones flagged red), top-6 endpoints (preferring endpoints.md confirmed matches with Chinese purpose labels and synthesized proto for quic/-ws. hosts), and top-5 local-data buckets (collapsed by parent dir + note, with DoubaoIme/doubaoime casing variants merged).
2. Writes the filled prompt to /card_prompt.md.
3. Auto-discovers baoyu-imagine at ~/.claude/skills/baoyu-imagine/scripts/main.ts (or via BAOYU_IMAGINE_SCRIPT env), invokes it with --ar 4:5 --quality 2k defaults, and writes /card.png.

Pass --no-image to write only the prompt and skip the image call. Pass any --image-* flag (--image-provider, --image-model, --image-imageSize, etc.) to override the defaults — e.g. --image-provider google --image-model gemini-3-pro-image-preview --image-imageSize 4K for native nano-banana-pro 4K output.

If baoyu-imagine is not installed, Phase 6 still writes card_prompt.md and prints an install hint, but skips the PNG. The skill remains fully functional without it.

Output. Final markdown report. Print its absolute path. If --card, also card_prompt.md and card.png.

Output layout (per app)

~/Documents/app-telemetry-audit/_/
├── meta.json
├── matched.md
├── endpoints.md
├── endpoint_table.md
├── local_data.md
├── strings/
│   └── .{urls,domains,paths,sql,events}.md
├── card_prompt.md          # only if --card requested
└── report.md               # the deliverable

Working files (strings/, intermediate *.md) are kept by default — they are the audit trail. Pass --clean to delete them after report.md is written.

Quick start

# Single app, full audit (markdown report only)
bun {baseDir}/scripts/run.ts /Library/Input\ Methods/DoubaoIme.app

# Add a 4:5 share card (prompt + PNG via baoyu-imagine)
bun {baseDir}/scripts/run.ts /Library/Input\ Methods/DoubaoIme.app --card

# Card prompt only — skip image generation
bun {baseDir}/scripts/run.ts /Library/Input\ Methods/DoubaoIme.app --card --no-image

# Card with Google nano-banana-pro at 4K
bun {baseDir}/scripts/run.ts /Library/Input\ Methods/DoubaoIme.app --card \
  --image-provider google \
  --image-model gemini-3-pro-image-preview \
  --image-imageSize 4K

# Multiple apps in one go (capped at 5)
bun {baseDir}/scripts/run.ts /Library/Input\ Methods/DoubaoIme.app /Applications/Foo.app

scripts/run.ts is a thin orchestrator that calls Phases 1→6 in sequence. Use it for the common case. Use individual phase scripts only when iterating.

--card flag surface

| Flag | Purpose | Default | |------|---------|---------| | --card | Render card_prompt.md AND card.png | off | | --no-image | With --card: write prompt, skip PNG | off | | --image-provider | baoyu-imagine provider (google, openai, replicate, dashscope, …) | provider auto-selected | | --image-model | Model id within the provider | provider default | | --image-ar | Aspect ratio | 4:5 | | --image-size | Explicit WxH | from --image-quality / provider | | --image-quality | normal or 2k | 2k | | --image-imageSize | Google/OpenRouter 1K/2K/4K | from --image-quality | | --out | Override output root | ~/Documents/app-telemetry-audit/ |

Any --image-* flag implicitly enables --card.

Relevant references

• references/safe_commands.md — command whitelist + rationale
• references/sdk_fingerprints.md — SDK detection rules (12+ SDKs MVP)
• references/known_endpoints.md — domain → product reverse lookup
• references/data_locations.md — typical on-disk layout per vendor
• references/methodology_examples.md — two worked examples (WeType, DoubaoIme)
• templates/report.md.tmpl — final report skeleton
• templates/card_prompt.md.tmpl — 4:5 visual card prompt skeleton

Failure modes Claude should NOT do

• Do not paraphrase strings into "looks like X" without quoting the literal evidence line + file path
• Do not infer frequencies from SDK names — only from literal numbers in strings or config files
• Do not run any binary inside the target app
• Do not open .sqlite content — only schemas
• Do not write a report when Phase 3 found nothing — write the short "no telemetry detected" note instead
• Do not invent endpoints from training memory; if the URL is not in /strings/, it does not go in the table

Verification (when developing this skill)

Smoke test on /Library/Input Methods/DoubaoIme.app and confirm the report covers:

• Frameworks: applogrs, Parfait, bytenn, onnxruntime, sscronet, TTNet, ime_net_sdk, sami
• Endpoints: at least 3 of ime.doubao.com/obric/ime/cloud/convert, log-klink.zijieapi.com, ime-gw.oceancloudapi.com, frontier-audio
• Local data: ~/Library/Application Support/DoubaoIme/Parfait/ready/685343/0/

Regression: run on the WeType IME bundle and confirm wetype.weixin.qq.com + CACHE_LOG_TBL schema appear.

Negative: run on a small app with no third-party telemetry — must produce the short "no telemetry detected" output without inventing SDKs.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: iamzhihuix
• Source: iamzhihuix/happy-claude-skills
• License: MIT

Install and usage instructions live in the source repository linked above.