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 commands/fatihkan/badi/asogit clone --depth 1 https://github.com/fatihkan/badiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/fatihkan/badi/aso)<a href="https://agentmods.dev/commands/fatihkan/badi/aso"><img src="https://agentmods.dev/badge/commands/fatihkan/badi/aso.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.01009 |
| Opus 5 | $0.00000 | $0.00504 |
| Sonnet 5 | $0.00000 | $0.00202 |
| Haiku 4.5 | $0.00000 | $0.00101 |
Grade A, and why
aso 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 4d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
App Store Optimization command. iOS app listing analysis via the iTunes API, keyword optimization, and competitor comparison.
Required Tools
- Bash (badi aso commands)
Procedure
Step 1: Set the Target
Ask the user: "What do you want to analyze?"
- Your own app — Audit + keywords + reviews
- Competitor comparison — 2 apps side-by-side
- Keyword research — Market discovery
- New app metadata — Listing preparation
The App ID is required: take it from the https://apps.apple.com/app/id[APP_ID] URL.
2026 Algorithm Reality (verify live — read the audit through these)
The store algorithms shifted in 2026; re-verify since they keep moving:
- Apple ranks on LLM semantic relevance + behavior — write metadata for MEANING, not exact-match fragments; a natural, topically-dense title/subtitle now ranks better than a keyword-stuffed 100-char field. Core limits are UNCHANGED (Title 30 / Subtitle 30 / Keywords 100).
- Apple Search Ads took a second, in-organic slot — paid results now sit where high organic positions used to convert; discount organic-install forecasts on high-intent/branded terms and treat defending your own brand keywords with paid as near-mandatory in competitive categories.
- Discovery extends beyond the listing — declaring App Intents + indexing content as Spotlight entities surfaces the app in Spotlight / Siri / Apple Intelligence; treat it as an ASO task, not just engineering.
- Screenshot captions are indexed — put real target keywords in the first 1–3 screenshot captions (a de-facto extra indexed field; verify per app, Apple hasn't documented it).
- Custom Product Pages expanded (up to 70, keyword-assignable) — build per-query/segment CPPs for incremental organic surface + tailored conversion.
- AI review summaries — users read an AI-generated summary built from recurring review themes; manage the themes (drive feature mentions, resolve recurring complaints), not just the star average.
- Google Play (I/O 2026) — optimize for "Ask Play" answer-style AI search (clear functional descriptions that map to user questions), front-load the Short Description with primary functional keywords, treat technical health (ANR / battery / retention) as ranking inputs, and use Gemini keyword-targeted custom store listings + Play Shorts where eligible.
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.
- 4d ago First seen · 120 lines · 0 tokens per session scan A d5cc0a1ba125
aso is a command published in the GitHub repository fatihkan/badi (7 stars, last pushed 18d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,009 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 2026-08-31.
Other commands, from other repositories
audit-agents-skills
Audit quality of agents, skills, and commands in a Claude Code project.
sonarqube
Analyze SonarCloud quality issues for a specific PR.
land-and-deploy
Merge PR, wait for CI, verify deploy, run canary — the complete landing pipeline.
methodology-advisor
Analyzes your codebase and asks 3 targeted questions to recommend the right AI-assisted development methodology stack.
scaffold
Interactive coach that asks 4-5 questions to determine whether you need an agent, command, skill, hook, or rule — then generates a ready-to-use template. Usage: /scaffold (no arguments — starts the coaching session).
investigate
Systematic root-cause debugging — find the cause before writing any fix.