Every task it re-discovers your codebase from scratch.grep, read, re-read, forget, repeat.It burns most of its budget BEFORE it writes a single line.Repowise fixes that. 👇⚡️ Index once. Never rediscover again.One index, kept current on every commit. Your agent reads the answer instead of the codebase.📉 The numbers: → 64,039 tokens → 2,391 tokens to load one commit's context [get_context] → -96% tokens, -89% file reads, -70% tool calls to load context 🗺️ What one index builds - 5 layers: → ◈ Graph: Dependency graph across 17 languages, symbol-level, with PageRank & call resolution → ◈ Git: Hotspots, ownership, hidden coupling (co-change), bus factor, bug history → ◈ Docs: Auto-generated wiki + auto-generates your CLAUDE.md & AGENTS.md from the REAL index → ◈ Decisions: Architectural decisions mined from 8 sources → ★ Code Health: 25 deterministic markers - 1 to 10 per file, zero LLM, under 30s on a 3,000-file repo 🎯 It names the ACTUAL fix: Not "this file is risky". It says Extract Class / Move Method / Break Cycle / Split File, with exact methods + blast radius attached Plus 10 task-shaped MCP tools for Claude Code, Cursor, Codex.✅ Free, open source (AGPL-3.0), runs 100% on your machine. No API key for first index. pip install repowise

Let Agents Design Agents!Memento-Skills is a self-evolving agent framework where agents learn from failures and rewrite their own skills.Most agent frameworks treat skills as static. You write them once, load them into context, and hope they work. When they fail, you debug manually or try again with the same broken skill.Memento-Skills takes a different approach. When a skill fails, the system reflects on why it failed, locates the broken skill, rewrites it, and stores the improved version back into the skill library.Here's how it works:The framework runs a continuous Read → Execute → Reflect → Write loop.Read: Retrieve candidate skills from the local library instead of loading every skill into context.Execute: Run skills in a local sandbox with actual tool calling - file operations, web search, scripts, external systems.Reflect: When execution fails, the system records what went wrong, updates the skill's utility score, and attributes the failure to specific skills.Write: Rewrite broken skills, optimize weak ones, or create new skills when nothing suitable exists.This isn't about accumulating more skills. It's about building a skill library that improves through task experience.The system was tested on HLE (Humanity's Last Exam) and GAIA (General AI Assistants) benchmarks. Performance improved over multiple learning rounds as the skill library grew from basic atomic skills into a richer set of learned capabilities.Built for open-source LLM ecosystems - works with Kimi, MiniMax, GLM, and other OpenAI-compatible endpoints.Comes with 9 built-in skills (filesystem, web-search, PDF, docx, xlsx, pptx, image analysis, skill-creator, dependency install) that serve as the starting point for the evolving library.It's 100% open sourceLink to Memento-Skills in comments!

📚 While Reading the Book Mastering NLP From Foundations to Agents by Lior Gazit and Meysam Ghaffari, Ph.D. (2nd Edition), one lesson became crystal clear:Production AI is rarely limited by the model. It's limited by the system around the model.The book covers the full stack:🧠 NLP Foundations → ⚙️ ML → 🤖 LLMs → 🔍 RAG & MCP → 👥 Agents → 🛡️ Guardrails → 🚀 AI ProductsWhat stood out to me were the lessons that every AI engineer eventually learns the hard way:🔍 Lesson 1: Hallucinations are often retrieval problems.Production Reality:You upgrade the model.Responses don't improve.Later, you discover the vector search was returning poor context.🧠 Lesson 2: Better prompts don't fix broken architectures.Production Reality:Teams spend weeks prompt tuning.The real issue is missing tools, weak workflows, or poor orchestration.📚 Lesson 3: RAG quality depends on chunking, embeddings, and indexing.Production Reality:Everyone blames the LLM.The root cause is often the retrieval pipeline.👥 Lesson 4: Agents fail because of systems, not intelligence.Production Reality:Agents loop forever.Call wrong tools.Lose context.Break workflows.The challenge becomes orchestration, not reasoning.🛡️ Lesson 5: Safety cannot be bolted on later.Production Reality:The first enterprise customer asks:"How do you prevent unsafe outputs?"Now governance becomes an engineering problem.⚡ Lesson 6: Latency is a product feature.Production Reality:The model works perfectly.Users still abandon it because responses take 20 seconds.📈 Lesson 7: Evaluation is more important than demos.Production Reality:Most AI systems look impressive in demos.Few survive real-world traffic.🚀 Lesson 8: AI products are different from AI models.Production Reality:Building the model is often 20% of the effort.Operating, monitoring, securing, evaluating, and improving the system is the other 80%.My biggest takeaway:Most engineers learn AI like this:LLMs → RAG → AgentsProduction engineers eventually realize the real stack looks like this:Data → ML → NLP → LLMs → Retrieval → Agents → Governance → ProductsThe future belongs to engineers who understand the entire system, not just the model.Activate to view larger image,

The Anatomy of a new Claude 'Fable 5' Prompt:1. TaskStart with why, NOT what. Claude 5 connects the dots.'I'm working on [goal] for [who it's for]. They need [what the output enables]. With that in mind: [task].'2. Context FilesUpload your expertise. Stop explaining in prompts."Read these files completely before responding: [filename .md] - [what it contains]."The file is the brain. This part never changes.3. ReferenceShow Claude 5 what good looks like."Reference for what I want to achieve: [paste]."One example beats ten instructions.4. EffortThe new change, a few people are talking about."This is a [routine / hard / hardest-unsolved] problem. Scope it like it's at the top of your range."Teams testing Claude 5 on easy tasks undersell it. Give it your hardest problem.5. Act"AskUserQuestion" is still the king.Add "When you have enough information to act, act. Don't re-litigate my decisions. While weighing a choice, give a recommendation."6. ScopeClaude 5 over-delivers by default. Control it."Do the simplest thing that works well. No extra features, refactors, or abstractions. If I'm describing a problem, the deliverable is your assessment."The old one did too little. This one does too much.7. DelegateOne Claude is no longer the limit."Split independent subtasks across subagents & keep working while they run. Verify with a fresh-context subagent."It's not a chatbot anymore. It's a team lead.8. EvidenceThe line that removes fake progress reports."Before reporting progress, audit every claim against a tool result. If it's unverified, say so. Tests failed? Show the output."Anthropic tested this. It nearly eliminated fabricated status updates.9. MemoryClaude 5 gets smarter every run. If you let it."Record learnings in [notes .md] — one per file. Update, no duplicate. Delete what turns out wrong."Your prompts expire. Your learning file compounds.10. CheckpointIt can run for hours. Decide when it stops."Pause only for: destructive actions, scope changes, or input only I can provide. Never end your turn on a promise."The old fear was Claude stopping too late. The new fear is stopping too early.11. ReportThe last block. The first thing you read."Open with the outcome - the TLDR I'd ask for. Complete sentences. Clear beats short."It worked for hours. You read for ten seconds.Copy the full prompt template + download my personal md. files for Claude here: Step 1. Go to how-to-ai.guide.Step 2. Subscribe for free. Don't pay anything.Step 3. Open my welcome email.Step 4. Hit the automatic reply button inside.Step 5. Download my .md files. Ready to upload.

The Anatomy of a new Claude 'Fable 5' Prompt:1. TaskStart with why, NOT what. Claude 5 connects the dots.'I'm working on [goal] for [who it's for]. They need [what the output enables]. With that in mind: [task].'2. Context FilesUpload your expertise. Stop explaining in prompts."Read these files completely before responding: [filename .md] - [what it contains]."The file is the brain. This part never changes.3. ReferenceShow Claude 5 what good looks like."Reference for what I want to achieve: [paste]."One example beats ten instructions.4. EffortThe new change, a few people are talking about."This is a [routine / hard / hardest-unsolved] problem. Scope it like it's at the top of your range."Teams testing Claude 5 on easy tasks undersell it. Give it your hardest problem.5. Act"AskUserQuestion" is still the king.Add "When you have enough information to act, act. Don't re-litigate my decisions. While weighing a choice, give a recommendation."6. ScopeClaude 5 over-delivers by default. Control it."Do the simplest thing that works well. No extra features, refactors, or abstractions. If I'm describing a problem, the deliverable is your assessment."The old one did too little. This one does too much.7. DelegateOne Claude is no longer the limit."Split independent subtasks across subagents & keep working while they run. Verify with a fresh-context subagent."It's not a chatbot anymore. It's a team lead.8. EvidenceThe line that removes fake progress reports."Before reporting progress, audit every claim against a tool result. If it's unverified, say so. Tests failed? Show the output."Anthropic tested this. It nearly eliminated fabricated status updates.9. MemoryClaude 5 gets smarter every run. If you let it."Record learnings in [notes .md] — one per file. Update, no duplicate. Delete what turns out wrong."Your prompts expire. Your learning file compounds.10. CheckpointIt can run for hours. Decide when it stops."Pause only for: destructive actions, scope changes, or input only I can provide. Never end your turn on a promise."The old fear was Claude stopping too late. The new fear is stopping too early.11. ReportThe last block. The first thing you read."Open with the outcome - the TLDR I'd ask for. Complete sentences. Clear beats short."It worked for hours. You read for ten seconds.Copy the full prompt template + download my personal md. files for Claude here: Step 1. Go to how-to-ai.guide.Step 2. Subscribe for free. Don't pay anything.Step 3. Open my welcome email.Step 4. Hit the automatic reply button inside.Step 5. Download my .md files. Ready to upload.