Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
iamzhihuix avatar

Happy App Audit

  • 126 installs
  • 303 repo stars
  • Updated April 20, 2026
  • iamzhihuix/happy-claude-skills

Run pre-release app audits covering auth, data handling, dependencies, config leaks, and common OWASP-style issues before shipping.

About

Happy-app-audit provides a structured security and quality review for applications before launch: authentication boundaries, sensitive data paths, misconfigurations, third-party risks, and compliance-oriented gaps. Use when hardening MVPs, preparing investor or customer diligence, or gating production releases.

  • Auth and session review prompts
  • Secrets and config exposure checks
  • Dependency vulnerability awareness
  • Privacy and data-flow scrutiny
  • Release-blocking issue prioritization

Happy App Audit by the numbers

  • 126 all-time installs (skills.sh)
  • +2 installs in the week ending Aug 2, 2026 (Skillselion tracking)
  • Ranked #935 of 2,203 Security skills by installs in the Skillselion catalog
  • Data as of Aug 2, 2026 (Skillselion catalog sync)
npx skills add https://github.com/iamzhihuix/happy-claude-skills --skill happy-app-audit

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs126
repo stars303
Last updatedApril 20, 2026
Repositoryiamzhihuix/happy-claude-skills

What it does

Run pre-release app audits covering auth, data handling, dependencies, config leaks, and common OWASP-style issues before shipping.

Files

SKILL.mdMarkdownGitHub ↗

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 <redacted:N> (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:
  /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 <app-path> --out <workdir>/meta.json

The script collects:

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

Output. <workdir>/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 <workdir>/meta.json --out <workdir>/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
  • sqlCREATE 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. <workdir>/strings/<binary-name>.{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. <workdir>/strings/, plus references/sdk_fingerprints.md.

Commands.

bun {baseDir}/scripts/match_fingerprints.ts <workdir>/strings/ \
  --fingerprints {baseDir}/references/sdk_fingerprints.md \
  --out <workdir>/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. <workdir>/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. <workdir>/strings/*.urls.md + <workdir>/strings/*.domains.md + <workdir>/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 <workdir>/strings/ \
  --fingerprints {baseDir}/references/known_endpoints.md \
  --out <workdir>/endpoints.md

Then Claude writes <workdir>/endpoint_table.md:

EndpointSDKProtocolInferred purposeFrequency source

Frequency source MUST cite either: a literal interval found in <workdir>/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 <bundle_id> --out <workdir>/local_data.md

The script finds:

  • ~/Library/Application Support/<bundle>/
  • ~/Library/Containers/<bundle>/Data/
  • ~/Library/Group Containers/group.<bundle-prefix>.*/
  • ~/Library/Caches/<bundle>/
  • ~/Library/Preferences/<bundle>.plist
  • ~/Library/Logs/<bundle>/

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

Output. <workdir>/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 <workdir> \
  --template {baseDir}/templates/report.md.tmpl \
  --out ~/Documents/app-telemetry-audit/<YYYY-MM-DD>_<bundle-id>/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 <workdir>/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 <workdir>/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/<YYYY-MM-DD>_<bundle-id>/
├── meta.json
├── matched.md
├── endpoints.md
├── endpoint_table.md
├── local_data.md
├── strings/
│   └── <binary>.{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

FlagPurposeDefault
--cardRender card_prompt.md AND card.pngoff
--no-imageWith --card: write prompt, skip PNGoff
--image-providerbaoyu-imagine provider (google, openai, replicate, dashscope, …)provider auto-selected
--image-modelModel id within the providerprovider default
--image-arAspect ratio4:5
--image-sizeExplicit WxHfrom --image-quality / provider
--image-qualitynormal or 2k2k
--image-imageSizeGoogle/OpenRouter 1K/2K/4Kfrom --image-quality
--outOverride 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 <workdir>/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.

Related skills

Securityauditappseccompliance

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.