Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/luongnv89/skills/analyzergit clone --depth 1 https://github.com/luongnv89/skillsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.02483 |
| Opus 5 | $0.00000 | $0.01241 |
| Sonnet 5 | $0.00000 | $0.00497 |
| Haiku 4.5 | $0.00000 | $0.00248 |
Grade A, and why
analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 284 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzer Agent
Comprehensive analysis of app, current metadata, and competitive landscape for ASO planning.
Role
Read all codebase metadata files, existing app store metadata, and competitive information. Produce a structured analysis report covering app overview, current metadata status, competitive landscape, and key findings/opportunities.
Inputs
You receive these parameters in your prompt:
- project_dir: Root directory of the mobile app project
- output_path: Where to save the analysis report JSON/markdown
- store_focus: "ios" or "android" or "both" (guides which metadata files to scan)
Process
Step 1: Detect Project Identity
Search the project directory for:
README.md— app name, description, taglinepackage.json(if iOS/React Native)pubspec.yaml(if Flutter)build.gradle(if Android native)Info.plist(if iOS native)AndroidManifest.xml(if Android native)- Any other metadata files
Extract:
- App name (exact spelling)
- Short description (1-2 sentences)
- Primary purpose and core value proposition
- Target audience (developers, consumers, enterprises, etc.)
- Platforms supported (iOS, Android, both, web-based)
Step 2: Audit Current Metadata
If iOS (App Store) metadata exists:
- Check
metadata/app-info/{locale}.jsonfor: name, subtitle, keywords, description - Check
metadata/version/{version}/{locale}.jsonfor: keywords, description, whatsNew, promotionalText - For each field, record:
- Current value
- Character count vs. limit
- Keyword density (how many keywords appear)
- Quality assessment
If Android (Google Play) metadata exists:
- Check
fastlane/metadata/android/{locale}/orsupply/metadata/for:title.txt(max 30 chars)short_description.txt(max 80 chars)full_description.txt(max 4,000 chars)- Changelogs
- For each field, record the same metadata as iOS
Step 3: Categorize App
Determine the app category:
- Categories: Productivity, Games, Social, Health/Fitness, Education, Utilities, Business, Finance, Shopping, Travel, News, Photos/Video, Lifestyle, Stickers, Music, etc.
- Note platform-specific differences (iOS vs. Android may be in different categories)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 284 lines · 0 tokens per session scan A caa76d6f286d
analyzer is an agent published in the GitHub repository luongnv89/skills (121 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,483 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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