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 instructions/zehedisode/ai-workflow-intelligence/claude-mdgit clone --depth 1 https://github.com/zehedisode/ai-workflow-intelligenceWrote 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/instructions/zehedisode/ai-workflow-intelligence/claude-md)<a href="https://agentmods.dev/instructions/zehedisode/ai-workflow-intelligence/claude-md"><img src="https://agentmods.dev/badge/instructions/zehedisode/ai-workflow-intelligence/claude-md.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 | $0.01437 | $0.01437 |
| Opus 5 | $0.00718 | $0.00718 |
| Sonnet 5 | $0.00287 | $0.00287 |
| Haiku 4.5 | $0.00144 | $0.00144 |
Grade A, and why
ai-workflow-intelligence CLAUDE.md 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 3d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Development Workflow Intelligence (AWI)
Proje Amacı
Bu proje, dünya çapında "vibecoding" (AI destekli yazılım geliştirme) yapan geliştiricilerin hangi AI araçlarını, hangi kombinasyonlarla, hangi senaryolarda kullandığını araştırıp standartlaştıran ve bu bilgiden yola çıkarak geliştiricilere en uygun araç/iş akışı kombinasyonunu öneren bir sistemdir.
Üç ana bileşen:
- Collectors — dış kaynaklardan ham veri toplar (GitHub config dosyaları, Reddit/HN tartışmaları, dev anketleri).
- Pipeline — ham veriyi normalize edip ortak şemaya (bkz.
data/schema.md) dönüştürür vedata/processed/altında saklar. - Analysis + CLI — birlikte-kullanım (co-occurrence) analizleri yapar ve
awi recommendkomutuyla kullanıcıya kombinasyon önerir.
Sistem zamanla kendi kendini güncelleyecek: .github/workflows/auto_update.yml
periyodik olarak collector'ları çalıştırıp pipeline'ı yeniden işler ve
data/processed/ snapshot'ını günceller.
Mimari Kararlar
- Dil: Python 3.11+
- CLI framework:
typer - Veri doğrulama:
pydanticmodelleri (src/awi/models/schema.py) - Veri formatı: Diskte JSON Lines (
.jsonl) — büyüyen veri setleri için satır satır ekleme/okuma kolay, git diff'i okunabilir. - Analiz: Başlangıçta basit frekans + co-occurrence matrisi (
pandas). Veri seti büyüdükçe embedding tabanlı benzerlik/kümeleme eklenecek (bkz.src/awi/analysis/READMETODO notları).
Klasör Yapısı
data/
schema.md # Kanonik veri şeması dokümantasyonu
seed/ # Elle küratörlüğü yapılmış başlangıç veri seti
raw/ # Collector'ların ham çıktısı (gitignored)
processed/ # Normalize edilmiş, analiz-hazır veri (jsonl)
src/awi/
collectors/ # Dış kaynaklardan veri çeken modüller
models/ # Pydantic şema tanımları
pipeline/ # Normalize + birleştirme mantığı
analysis/ # Co-occurrence, skorlama, öneri motoru, sentiment
tool_info.py # Araçların yalın Türkçe açıklamaları (ortak kaynak)
mcp_server.py # Öneri motorunu MCP aracı olarak dışa açar
cli.py # Typer CLI giriş noktası
config.py # Merkezi yol sabitleri ve ortam değişkeni override (AWI_DATA_DIR)
scripts/
update_dataset.sh # collector + pipeline'ı tek komutla çalıştırır
baslat.sh # Teknik bilmeyen için tek komutluk sohbet yardımcısı
vibe_oneri.py # baslat.sh'nin çalıştırdığı soru-cevap önerici
MCP_KURULUM.md # MCP'yi Cursor/Windsurf/Claude Desktop'a bağlama rehberi
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.
- 3d ago First seen · 116 lines · 1,437 tokens per session scan A 29bbcd494c99
ai-workflow-intelligence CLAUDE.md is an instructions file published in the GitHub repository zehedisode/ai-workflow-intelligence (0 stars, last pushed 1mo ago), licensed MIT. It adds 1,437 tokens to every session, about $0.0072 per session on Opus 5. 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.
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