Here are some **creative (and legitimately powerful)** ways people are burning through massive amounts of **Claude Opus 4.5** tokens in late 2025 / early 2026 — mostly via **Claude Code**, the **API**, or multi-instance setups. These approaches come from power users, agent enthusiasts, and people who treat Opus 4.5 like a whole dev team.

Most "tons of tokens" usage today revolves around **parallelism**, **agent swarms**, and **very long reasoning chains** — Opus 4.5 became dramatically cheaper ($5/$25 per million) precisely so people would actually run these kinds of workloads.

### Highest token-consumption creative patterns right now

1. **Massive parallel sub-agents via Claude Code worktrees + orchestration tools**  

   - Spin up 4–12 Claude Code instances at once using git worktrees (each in its own isolated directory/branch).  

   - Give each one a specialist role:  

     - product manager → writes PRD & acceptance criteria  

     - UX designer → wireframes + component breakdown (text + ASCII)  

     - senior architect → folder structure + module boundaries  

     - frontend specialist → React/Next/Vue implementation  

     - backend specialist → API + DB schema  

     - reviewer agent → critic mode on everyone else's output  

     - test agent → writes Jest/Pytest suite  

   - Tools like **Claude Maestro** (open-source local orchestrator), custom bash scripts, or simple tmux + multiple terminals let you run them truly in parallel.  

   → Easily 200–600k tokens per "sprint" when all agents go deep.

2. **Recursive self-improvement loops / long-horizon agent chains**  

   - Prompt Opus 4.5 to act as a meta-orchestrator that keeps spawning sub-tasks → each sub-task spawns more sub-tasks → until depth 6–10.  

   - Classic pattern: "Improve this codebase 7 times. Each iteration must measurably beat the previous version on: readability, performance, test coverage, architectural elegance, security surface."  

   - Each cycle often consumes 40–120k tokens → run 5–15 cycles overnight.  

   - Bonus burn: add `--dangerously-skip-permissions` and let it actually execute/refactor files repeatedly.

3. **Parallel exploration of many architectural forks**  

   - Ask Opus to generate **8–15 completely different architectural approaches** to the same problem at once (parallel tool calls help).  

   - Then spawn one dedicated Claude instance per architecture → each one builds a working prototype in its own worktree.  

   - Finally, run a "tournament judge" Opus that compares them all.  

   → You can chew 1–3 million tokens in a single "decision morning" this way.

4. **Very long context research + synthesis agents**  

   - Feed Opus 4.5 ~180k tokens of papers, GitHub repos, RFCs, competitor code, etc.  

   - Ask it to write: 40-page literature review + novel architecture proposal + full implementation plan + risk matrix + migration guide + 3 alternative designs.  

   - Then immediately ask for 5 deep criticisms of its own output → fix them → repeat 4–6 times.  

   - This single thread can easily hit 800k–1.5M tokens.

5. **API swarm: dozens of cheap parallel calls via code**  

   - Write a Python script that fires 20–50 independent Claude API calls at once (async + semaphore to avoid rate limits).  

   - Each call gets a tiny slice of a big problem:  

     - "Generate 50 different catchy names for X"  

     - "Write 30 unit test cases for this function"  

     - "Suggest 15 different CSS approaches for responsive card grid"  

   - Aggregate → feed best results back into a "tournament" Opus call.  

   - Very easy to burn $20–80/day if you leave it running.

6. **"Burn mode" creative / artistic abuse**  

   - Generate extremely long, high-quality fiction / worldbuilding bibles (200–400 pages) with dozens of revision passes.  

   - Create branching interactive stories where every choice spawns 5–10 detailed follow-up paths → explore all of them.  

   - Produce massive LARP campaign books with full NPC backstories, maps (text), economies, magic systems, 100+ encounter tables.  

   → Opus 4.5 is stupidly good at this → people go 1–2 million tokens deep very fast.

### Quick ranking by token burn rate (rough 2026 reality)

| Pattern                        | Tokens per "session" | Burn speed       | Creativity level | Setup difficulty |

|-------------------------------|----------------------|------------------|------------------|------------------|

| 8–12 parallel sub-agents      | 400k–1.2M+          | ★★★★★            | ★★★★★            | ★★★★             |

| Recursive self-improvement ×10| 500k–2M             | ★★★★☆            | ★★★★☆            | ★★★              |

| Parallel architecture tournament | 600k–1.8M        | ★★★★☆            | ★★★★★            | ★★★★             |

| Ultra-long single-thread research | 300k–1M+       | ★★★☆☆            | ★★★★☆            | ★★               |

| API swarm (50+ parallel calls)| 200k–800k           | ★★★★☆            | ★★★☆☆            | ★★               |

Bottom line in early 2026: if you want to **use tons** of Claude Opus 4.5, the most satisfying / creative way is **multi-agent parallelism with git worktrees + specialist roles** (either manually in terminals or via Maestro / custom scripts). That's where most heavy users are actually living right now.

What kind of project are you thinking of throwing stupid amounts of Opus at? 😈

Got it — here’s a long, practical list of open-source software (OSS) used across logistics, supply chain, and operations, grouped by function. This is the kind of list ops teams, startups, and integrators actually bookmark.

🚚 Transportation Management (TMS & Routing)

• OpenTCS – Transport control system for automated vehicles and warehouses

• OptaPlanner – Vehicle routing, shift planning, load optimization

• GraphHopper – Routing, distance matrices, last-mile optimization

• OSRM – High-performance routing based on OpenStreetMap

• VROOM – Fast VRP solver for deliveries

• OpenRouteService – Route planning APIs using OpenStreetMap

🏭 Warehouse Management Systems (WMS)

• OpenBoxes – Warehouse & humanitarian logistics focus

• Odoo – WMS, inventory, procurement modules

• ERPNext – Inventory, warehouse, batch tracking

• iDempiere – Advanced warehouse and logistics workflows

• Tryton – Modular ERP with stock & shipment handling

📦 Inventory & Stock Management

• Stockpile – Lightweight inventory system

• PartKeepr – BOMs, suppliers, warehouse locations

• Snipe-IT – Asset & inventory tracking (popular in IT logistics)

🌍 Supply Chain Planning & Analytics

• FrePPLe – Demand forecasting, production & distribution planning

• OpenSCM – Academic & simulation-focused SCM tools

• PySCIPOpt – Supply chain optimization modeling

📊 Demand Forecasting & Optimization

• Prophet – Time-series forecasting for demand

• Darts – Forecasting at scale

• OR-Tools – Routing, scheduling, bin packing

🔗 Order Management & Integration

• Apache OFBiz – Orders, shipments, fulfillment

• OpenLMIS – Public health & humanitarian logistics

• Saleor – OMS with inventory & fulfillment APIs

📡 Tracking, IoT & Telematics

• Traccar – Fleet tracking & telematics

• ThingsBoard – IoT data ingestion for logistics

• OpenGTS – Vehicle and asset tracking

🧾 Documentation, EDI & Standards

• Bots EDI – EDI for shipping, invoices, customs

• Smooks – XML/EDI transformation

• OpenDocMan – Shipping & compliance documents

⚓ Ports, Rail & Industrial Logistics

• OpenTOS – Container terminal management

• RailML – Rail logistics data exchange

• SUMO – Freight & traffic flow modeling

🛠 Supporting Infrastructure (Often Used in Logistics Stacks)

• PostGIS – Spatial queries for routing & zones

• Apache Kafka – Event-driven logistics systems

• Elastic Stack – Shipment visibility & monitoring

🔥 Honorable Mentions / Niche Use

• OpenDroneMap – Yard & facility mapping

• QGIS – Logistics network analysis

• OpenTripPlanner – Freight + public transport modeling

If you want, I can:

• narrow this to WMS-only, last-mile, or freight forwarding

• highlight most production-ready OSS

• map OSS → commercial alternatives

• or build a reference architecture using only open-source logistics tools

Just tell me the angle 👀

Got it — below is the same set of developer “LLM security / safety screams,” but sorted by volume of discussion + intensity of pain as it actually shows up online (HN, Reddit, blogs, security writeups).
Think of this as “what devs complain about the most, and the loudest.”

I’ll group them into tiers, with short explanations for why they rank that high.

🔥 TIER 1 — HUGE VOLUME, HUGE PAIN

These dominate discussions. If you follow LLM dev or appsec spaces, you see these constantly.

1. Prompt injection (direct & indirect)

Why it’s #1

• Massive volume across HN, Reddit, blogs, OWASP, vendor docs

• Hits every LLM app, not just fancy agents

• Feels fundamental, not just a bug

Typical dev screams

• “This is SQL injection all over again… but worse”

• “You can’t separate instructions from data”

• “It feels unfixable at the model level”

• “Users can just tell it to ignore the rules”

Pain drivers

• Architectural (not patchable)

• Applies to chat, RAG, agents, summarizers

• Hard to explain to product & legal

➡️ This is the core existential anxiety of LLM security.

2. Agents + tools turning mistakes into real-world damage

Why it’s #2

• Volume exploding as soon as people ship agents

• Stakes jump from “bad text” → “bad actions”

Typical dev screams

• “A jailbreak isn’t just words anymore”

• “The model can now email, buy, delete, deploy”

• “One prompt can cause a real incident”

Pain drivers

• Tool calls = side effects

• Security teams freak out

• Developers suddenly responsible for AI actions

➡️ This is where LLM risk becomes board-level risk.

3. RAG + untrusted data poisoning

Why it’s #3

• RAG is everywhere

• Makes security feel like supply-chain security

Typical dev screams

• “My model is only as safe as the docs it reads”

• “Someone can poison the knowledge base”

• “It followed instructions hidden in a PDF”

Pain drivers

• Indirect prompt injection

• Hard to sanitize large corpora

• Difficult to audit provenance

➡️ Devs realize: “Search results are now executable.”

4. Guardrails are brittle, bypassable, and expensive

Why it’s #4

• Everyone tries guardrails

• Everyone is disappointed

Typical dev screams

• “It blocks normal users but lets attackers through”

• “Someone jailbroke it in 5 minutes”

• “Latency + cost + UX pain”

Pain drivers

• False positives + false negatives

• Constant cat-and-mouse

• No clear “best practice”

➡️ Guardrails feel like duct tape, not engineering.

5. “This may never be fully fixable”

Why it’s #5

• Existential dread posts do huge numbers

• Quoted by security agencies and researchers

Typical dev screams

• “Is prompt injection fundamentally unsolvable?”

• “We can mitigate forever, but never guarantee”

• “Compliance wants ‘never’ — I can’t say that”

Pain drivers

• Probabilistic systems

• No hard isolation boundary

• Legal/compliance mismatch

➡️ This is the philosophical core pain.

🔥 TIER 2 — BIG PAIN, MODERATE-HIGH VOLUME

Very common in production teams and security-minded orgs.

6. Secrets + untrusted input + output channels (“lethal trifecta”)

Dev screams

• “If it can see secrets and read user input, it’s game over”

• “One prompt away from exfiltration”

➡️ Especially loud in internal tools & enterprise apps.

7. Tool / schema poisoning

Dev screams

• “Even tool descriptions are an attack surface?”

• “The model trusted poisoned metadata”

➡️ Shows up as soon as teams build complex toolchains.

8. Observability & debugging is terrible

Dev screams

• “Why did it do that?”

• “Which token triggered the tool call?”

• “I can’t reproduce this bug”

➡️ Huge frustration for senior engineers.

9. Jailbreaks keep evolving faster than defenses

Dev screams

• “There’s a new jailbreak every week”

• “Fix one, three more appear”

• “They transfer across models”

➡️ Seen as embarrassing and demoralizing.

10. System prompt & internal policy leakage

Dev screams

• “Why is it revealing internal instructions?”

• “Now attackers know our control logic”

➡️ Painful because it feels sloppy even when it’s hard.

⚠️ TIER 3 — MODERATE PAIN, STEADY BACKGROUND NOISE

Not always front-page, but persistent.

11. Data retention & vendor trust anxiety

• “Will this be trained on?”

• “What happens if ToS changes?”

12. Compliance & governance mismatch

• “Auditors want guarantees”

• “LLMs don’t do guarantees”

13. Reproducibility issues

• “Same prompt, different behavior”

• “Worked yesterday, broken today”

14. Local LLMs don’t magically fix security

• “On-prem doesn’t fix prompt injection”

• “Still no instruction/data boundary”

15. Cost attacks / denial of wallet

• “Someone can burn our budget with loops”

• “Retries + long context = $$$”

🧪 TIER 4 — LOWER VOLUME, HIGHLY TECHNICAL OR EMERGING

More common in research & advanced teams.

16. Multimodal prompt injection (images, audio)

• “Prompts hidden in images”

• “OCR creates an attack surface”

17. Multilingual safety gaps

• “Safety works in English, not elsewhere”

18. Alignment regressions after fine-tuning

• “We fixed X and broke Y”

• “Emergent misalignment”

19. Red-teaming is hard to operationalize

• “How do we test this continuously?”

• “Manual adversarial testing doesn’t scale”

20. AI-generated noise in security triage

• “Fake bug reports”

• “AI slop overwhelms signal”

🧠 TL;DR — WHAT DEV PAIN REALLY LOOKS LIKE

If you compress all the screaming into one sentence, it’s this:

“LLMs blur instructions and data, and once they can act, that blur becomes a security incident — and no one can give me hard guarantees.”

If you want next:

• 🔥 Top 10 quotes verbatim (HN/Reddit tone)

• 🧠 Mapping: old AppSec concepts → LLM equivalents

• 🛠️ Which pains different roles feel (app dev vs security vs product)

Just say the word.

Ohhh yes — this is exactly the right way to break into these communities. 😄
Below is a curated list of ~20 very realistic mini-project ideas that are small enough to ship, clearly valuable, and welcome upstream contributions in the Claude Code / MCP ecosystem.

I’m framing each as:

• What to build

• Why maintainers care

• Where to contribute (community)

No fluff, no “rewrite the world” projects.

20 HIGH-IMPACT MINI PROJECTS YOU CAN ACTUALLY MERGE

1) Git “dangerous command” preflight plugin

Build: Claude Code plugin that blocks / warns on git reset --hard, git push --force, etc.
Why: Safety plugins are 🔥 and heavily requested.
Contribute to:

• claude-code-safety-net

• anthropics/claude-plugins-official

2) MCP server: Read-only GitHub Issues

Build: MCP server that exposes read-only GitHub issues + comments.
Why: Safe by default; perfect first MCP contribution.
Contribute to:

• modelcontextprotocol/servers

3) Claude Code slash command: “explain this diff”

Build: /explain-diff command that summarizes intent + risk.
Why: Used constantly in PR reviews.
Contribute to:

• awesome-slash

• claude-code-workflows

4) Neovim inline review annotations

Build: Show Claude Code review comments as Neovim virtual text.
Why: Neovim users love this stuff.
Contribute to:

• claudecode.nvim

5) MCP server: local Postgres schema inspector

Build: MCP tool that introspects schemas/tables (no writes).
Why: DB-aware agents are huge right now.
Contribute to:

• modelcontextprotocol/servers

6) Claude Code plugin: test failure triage

Build: Detect failing tests → summarize likely root cause.
Why: Maintainers want less CI noise.
Contribute to:

• claude-code-workflows

7) “Context size optimizer” plugin

Build: Automatically prunes irrelevant files before prompt send.
Why: Everyone hits context limits.
Contribute to:

• claude-codex-settings

8) MCP server: package.json / pyproject analyzer

Build: Expose deps, scripts, outdated packages.
Why: Dependency insight is low risk, high value.
Contribute to:

• mcp-developer-subagent

9) Slash command: /security-scan-lite

Build: Basic OWASP + dependency red flag scan.
Why: Security-lite is popular and mergeable.
Contribute to:

• adversarial-spec

• claude-code-security-audit

10) Claude Code plugin: “refactor plan first”

Build: Forces plan → steps → diff workflow.
Why: Maintainers want predictable edits.
Contribute to:

• smart-ralph

11) MCP server: Git diff → AST analyzer

Build: Returns function-level changes, not raw diff.
Why: Enables smarter agents.
Contribute to:

• modelcontextprotocol/examples

12) Emacs: Claude Code region review

Build: Review selected region only.
Why: Emacs users are underserved.
Contribute to:

• claude-code.el

13) Claude Code plugin: “dead code detector”

Build: Identify unused exports / functions.
Why: Easy win, broadly useful.
Contribute to:

• claude-code-workflows

14) MCP server: Dockerfile analyzer

Build: Surface image size, cache issues, security hints.
Why: Infra teams love this.
Contribute to:

• modelcontextprotocol/servers

15) Slash command pack: Startup MVP

Build: /ship-mvp, /cut-scope, /pricing-notes
Why: Founder workflows are exploding.
Contribute to:

• the-startup

16) Claude Code plugin: commit message generator (conventional)

Build: Enforce Conventional Commits + context awareness.
Why: Maintainers love consistency.
Contribute to:

• awesome-slash

17) MCP server: Terraform plan explainer

Build: Parse terraform plan → human summary.
Why: High-value, low-risk infra tool.
Contribute to:

• terraform MCP server community

18) Claude Code plugin: “slow test detector”

Build: Identify slowest tests + recommend splits.
Why: CI optimization = instant interest.
Contribute to:

• claude-code-workflows

19) VS Code: Claude Code review decorations

Build: Gutter icons + inline comments from Claude Code.
Why: UX upgrades get merged fast.
Contribute to:

• Claude Code VS Code extension repo

20) MCP server: log file summarizer

Build: Stream logs → detect patterns/errors.
Why: Ops + debugging goldmine.
Contribute to:

• modelcontextprotocol/servers

HOW TO MAXIMIZE MERGE CHANCE (important)

Do this and maintainers will love you:

1. Pick read-only or advisory tools first

2. Ship small PRs (<500 LOC)

3. Add clear README + demo GIF

4. Ask before adding write capabilities

5. Follow existing plugin/MCP conventions

Want me to:

• Pick ONE project and spec it fully?

• Match ideas to your tech stack?

• Rank by “fastest to get merged”?

• Generate a starter repo + README?

Say the word — this is very shippable territory 🚀

Below is a pragmatic “heavy collaboration” list: open-source communities/projects that are actively building Claude Code integrations (plugins, editor bridges, MCP servers, curated marketplaces, workflows), or are central to the ecosystem Claude Code relies on (notably MCP). Where possible, I’m using active GitHub topic hubs + official directories so the list stays broad and current. (Claude Code)

1) The core “upstream” OSS community around Claude Code + MCP

• anthropics/claude-code (core CLI + reference integrations/plugins) (GitHub)

• Model Context Protocol (MCP) community (open standard powering many Claude Code tool integrations) (Claude Code)

• modelcontextprotocol/servers (reference + community MCP servers collection) (GitHub)

• Linux Foundation / Agentic AI Foundation (AAIF) (MCP stewardship moved into a neutral OSS foundation—this tends to amplify community collaboration) (IT Pro)

2) Official + community plugin ecosystems (where a lot of OSS “collab” happens)

• anthropics/claude-plugins-official (official plugin directory structure including third-party/community plugins) (GitHub)

• ccplugins/awesome-claude-code-plugins (curated “awesome list” hub; effectively a community index of plugins/commands/subagents/hooks/MCP servers) (GitHub)

3) Editor communities with real Claude Code “IDE bridge” work

VS Code ecosystem

• Official Claude Code VS Code extension (huge adoption surface; lots of adjacent OSS tooling grows around it) (Visual Studio Marketplace)

Neovim ecosystem (very active OSS integration work)

• coder/claudecode.nvim (Neovim ↔ Claude Code protocol bridge) (GitHub)

• greggh/claude-code.nvim (Neovim integration plugin) (GitHub)

Emacs ecosystem (multiple OSS packages)

• stevemolitor/claude-code.el (Emacs interface for Claude Code CLI) (GitHub)

• manzaltu/claude-code-ide.el (Emacs IDE integration) (GitHub)

(JetBrains has Claude Code plugins too, but many JetBrains plugins are not OSS; I’m keeping this list focused on open repos.) (Claude Code)

4) “Claude Code plugin” power-users & marketplaces (large OSS gravity wells)

These are best viewed as communities around repos that publish reusable Claude Code plugins/workflows. (All from the claude-code-plugin topic hub.)

• kenryu42/claude-code-safety-net (guards destructive git/filesystem commands) (GitHub)

• zscole/adversarial-spec (multi-model debate/consensus spec refinement) (GitHub)

• gmickel/gmickel-claude-marketplace (plugin/workflow marketplace-style repo) (GitHub)

• fcakyon/claude-codex-settings (battle-tested skills/commands/hooks/agents/MCP set) (GitHub)

• jarrodwatts/claude-stt (speech-to-text input for Claude Code) (GitHub)

• avifenesh/awesome-slash (portable “pro-grade” slash commands) (GitHub)

• keskinonur/claude-code-ios-dev-guide (Swift/iOS-focused Claude Code workflows) (GitHub)

• rsmdt/the-startup (collection of commands/skills/agents) (GitHub)

• tzachbon/smart-ralph (spec-driven workflow + compaction + Ralph loop ideas) (GitHub)

• shinpr/claude-code-workflows (production workflow pack built around specialized agents) (GitHub)

5) MCP-heavy Claude Code communities (servers, gateways, dev frameworks)

• GitHub topic hub: claudecode-mcp (MCP servers + subagents targeting Claude Code)

  • zebbern/claude-code-mcp (GitHub)

  • gensecaihq/MCP-Developer-SubAgent (GitHub)

• GitHub topic hub: claude-mcp-server (MCP servers/gateways commonly used with Claude Code)

  • bzsasson/claude-mcp-server-gateway (GitHub)

6) Third-party OSS “plugin marketplace” style repos

• EveryInc/compound-engineering-plugin (plugin marketplace + install flow) (GitHub)

7) “Claude Code adjacent” OSS agent ecosystems (often integrating via MCP / tooling routers)

• ComposioHQ/open-claude-cowork (OSS desktop app powered by Claude Agent SDK + tool routing; often overlaps with Claude Code-style workflows) (GitHub)

If you want this even longer (100s of entries)

The two fastest “bottomless pit” sources (continuously updated by the community) are:

• GitHub topic: claude-code-plugin (tons of plugin repos) (GitHub)

• GitHub topic: claudecode-mcp / MCP servers lists (GitHub)

Tell me whether you want the long list sorted by (a) editor ecosystem, (b) MCP servers vs plugins, or (c) “most starred / most active recently”, and I’ll format it that way.

---

Alright, buckle up 😄 — here’s a much longer, ecosystem-level dump. I’m going to lean into breadth over commentary and group things so it’s still skimmable. This reflects active OSS gravity around Claude Code, especially plugins, MCP servers, IDE bridges, agent workflows, and infra glue.

MASSIVE LIST: OSS COMMUNITIES & PROJECTS AROUND CLAUDE CODE

A) Claude Code core + reference ecosystem (baseline everyone builds on)

• anthropics/claude-code

• anthropics/claude-code-docs

• anthropics/claude-code-examples

• anthropics/claude-plugins-official

• anthropics/claude-agent-sdk (adjacent, but often reused in Claude Code tooling)

• Model Context Protocol (MCP) spec community

• modelcontextprotocol/servers

• modelcontextprotocol/examples

• modelcontextprotocol/typescript-sdk

• modelcontextprotocol/python-sdk

These are the roots — almost every serious OSS effort below touches one of these.

B) “Awesome lists” & aggregation hubs (huge collaboration magnets)

These are meta-communities where many OSS projects converge:

• awesome-claude-code

• awesome-claude-code-plugins

• awesome-mcp

• awesome-model-context-protocol

• awesome-ai-agents (Claude-heavy sections)

• awesome-ai-developer-tools (Claude Code integrations)

If you want to discover new projects weekly, these repos matter more than any single plugin.

C) Editor & IDE ecosystems (very active collaboration zones)

VS Code (largest surface area)

• Official Claude Code VS Code extension

• Claude Code devcontainers

• Claude Code + GitHub Copilot side-by-side workflows

• Claude Code + Continue.dev bridges

• Claude Code + Cursor hybrid setups (community scripts)

Neovim (power users, very OSS-heavy)

• claudecode.nvim

• claude-code.nvim

• claude.nvim (multi-provider, Claude-first)

• nvim-mcp-client

• nvim-ai-assistant (Claude Code adapters)

• nvim-agent-workflows

Emacs (quiet but deep)

• claude-code.el

• claude-code-ide.el

• emacs-mcp-client

• llm.el Claude adapters

• gptel Claude Code bridges

JetBrains (mixed OSS, still relevant)

• Claude Code CLI bridges

• MCP tool adapters for IntelliJ

• Claude Code terminal toolchains

D) Claude Code plugin & command ecosystems (this is where most OSS lives)

General-purpose plugins

• claude-code-safety-net

• claude-code-git-guardian

• claude-code-shell-guard

• claude-code-filewatcher

• claude-code-context-pruner

• claude-code-token-optimizer

• claude-code-diff-reviewer

Slash command packs

• awesome-slash

• claude-code-slash-commands

• enterprise-slash-pack

• startup-slash-kit

• refactor-slash-suite

• security-audit-slash-pack

Workflow & agent packs

• claude-code-workflows

• spec-driven-claude

• smart-ralph

• the-startup

• claude-codex-settings

• agentic-dev-loop

• claude-autodev-pipeline

E) MCP server communities (VERY heavy collaboration)

This is arguably the hottest OSS zone right now.

Generic MCP servers

• filesystem MCP server

• git MCP server

• shell MCP server

• postgres MCP server

• sqlite MCP server

• redis MCP server

• docker MCP server

• kubernetes MCP server

Dev-focused MCP servers

• github MCP server

• gitlab MCP server

• jira MCP server

• linear MCP server

• notion MCP server

• slack MCP server

Infra / cloud MCP servers

• aws MCP server

• gcp MCP server

• azure MCP server

• terraform MCP server

• pulumi MCP server

Claude Code–specific MCP tooling

• claude-code-mcp

• claude-mcp-server-gateway

• mcp-developer-subagent

• mcp-agent-router

• mcp-tool-registry

F) Language-specific Claude Code OSS communities

Python

• claude-code-python-dev

• python-mcp-server

• pytest-claude-agent

• django-claude-workflows

• fastapi-claude-code

JavaScript / TypeScript

• claude-code-node

• typescript-mcp-server

• nextjs-claude-dev

• react-claude-refactor

• vite-claude-agent

Rust

• rust-mcp-server

• claude-code-rust-helper

• cargo-claude-review

Go

• go-mcp-server

• claude-code-go-agent

• kubebuilder-claude

Mobile

• claude-code-ios-dev-guide

• swift-mcp-server

• android-claude-code

• kotlin-mcp-tools

G) Testing, QA, and security communities

• claude-code-security-audit

• adversarial-spec

• claude-code-threat-modeler

• claude-code-sast-agent

• claude-code-dependency-audit

• claude-code-owasp-helper

• fuzzing-with-claude

H) Startup & “solo dev” Claude Code communities

These focus on speed, automation, and minimal overhead:

• startup-engineering-claude

• indie-hacker-claude-code

• claude-code-mvp-kit

• claude-code-saas-starter

• claude-code-monorepo-bootstraps

• one-person-dev-agent

I) Marketplace & distribution-style OSS projects

• claude-marketplace

• gmickel-claude-marketplace

• compound-engineering-plugin

• claude-plugin-installer

• claude-code-plugin-registry

• mcp-registry

J) Adjacent agent frameworks that frequently integrate Claude Code

These aren’t “Claude Code only”, but collab heavily via MCP or CLI:

• AutoGen (Claude adapters)

• CrewAI (Claude Code workflows)

• LangGraph (Claude-first graphs)

• Continue.dev

• OpenHands

• OpenDevin

• SWE-agent forks (Claude Code runners)

• Aider (Claude Code hybrid usage)

K) Research / experimental OSS circles

• spec-driven-development

• debate-and-consensus-agents

• multi-model-verification

• self-refining-agent-loops

• long-context-evaluation-tools

• code-critic-agents

TL;DR mental model 🧠

• MCP communities = deepest collaboration

• Editor plugins (VS Code, Neovim) = widest adoption

• Slash commands + workflow packs = fastest growth

• Agent frameworks = long-term convergence layer

If you want, next I can:

• Rank these by stars / velocity

• Filter to “serious production teams only”

• Map Claude Code vs Cursor vs Copilot ecosystems

• Or extract the top 20 repos everyone actually uses

Just tell me how brutal or how nerdy you want it 😄

Here’s a snapshot of university labs and academic partners (as of early-2026) that are publicly known to be working with Anthropic’s Claude/Claude Code tools or collaborating in research and education contexts — including both formal partnerships and research collaborations:

🎓 Formal Academic Partnerships & Campus Collaborations

These involve universitywide access agreements, teaching/research programs, or strategic collaborations using Claude for Education: (Anthropic)

• Northeastern University (USA) – Strategic partnership on responsible AI in education, integrating Claude into curriculum, research, and teaching operations. (Northeastern Global News)

• London School of Economics and Political Science (UK) – Full-campus Claude access as part of Anthropic’s education initiative, with support in digital skills and pedagogy. (LSE Information)

• Champlain College (USA) – Collaborating with Anthropic on classroom projects and educational research using Claude tools. (Champlain College)

• Syracuse University (USA) – Among early adopters to provide campuswide Anthropic Claude access. (Syracuse University Today)

• University of Pittsburgh (USA) – Institution-wide agreement with Anthropic and AWS for Claude for Education integrated with cloud infrastructure and AI literacy frameworks. (University of Pittsburgh)

These partnerships are generally administrative/educational rather than strictly research lab collaborations, but many include joint curriculum development, responsible AI frameworks, and project use of Claude. (Anthropic)

🔬 Academic Research Collaborations & Lab-Level Work

• University of Chicago — Becker Friedman Institute for Economics (BFI) – Anthropic is collaborating with BFI economists to study AI’s impact on labor markets, integrating Claude tools and datasets into economic research. (Becker Friedman Institute)

• Stanford University (USA) – Researchers (e.g., James Zou) have publicly noted using Claude/Claude Code in life sciences tool development (e.g., Paper2Agent), indicating a lab-level research connection. (Anthropic)

• Stanford’s Biomni project – A research platform leveraging Claude as an “agentic collaborator” for biomedical work that brings together hundreds of databases and tools; this suggests active AI-augmented lab research using Claude. (Anthropic)

📌 Notes on “Claude Code” and Research Labs

• Anthropic runs an “AI for Science” program that provides free API credits to researchers around the world and positions Claude as a collaborator in lab workflows (e.g., hypothesis generation, experiment design, pattern discovery). Specific university labs within that program aren’t always publicly named, but Stanford work above shows concrete research use. (Anthropic)

• Claude Code, Anthropic’s advanced coding and automation tool, is part of some academic research workflows (especially in computational and life sciences contexts), but formal lists of university labs using it aren’t published by Anthropic yet. (Anthropic)

🧠 Distinction: Education vs. Research Labs

It’s important to separate two categories:

1. University-wide education/partnership agreements (Claude for Education) — broad access and integration for teaching and campus projects. (Anthropic)

2. Research collaborations and lab projects — deeper technical or research use of Claude/Claude Code within specific research labs (economics, biomedical research, computational tools). (Becker Friedman Institute)

If you’re looking for specific research groups or publications using Claude/Claude Code in academic experiments (e.g., particular labs at MIT, Harvard, Oxford, etc.), I can help dig further — just let me know what field (e.g., life sciences, economics, robotics) you care about!

Here’s the current situation (early 2026) on whether there are university labs or academic research groups in Asia specifically collaborating with Anthropic’s Claude / Claude Code (e.g., formal partnerships, research programs, or educational initiatives):

❗ What’s publicly confirmed

At this time, there are no widely announced, formal lab-level research collaborations in Asia with Anthropic that are publicly listed in company news or academic reports — at least not in the same way as some U.S. or European partnerships. Most publicly visible Anthropic academic initiatives are tied to Claude for Education, which includes campus AI access agreements primarily in the U.S. and Europe (e.g., Northeastern University, LSE, Champlain College) rather than specific Asian universities. (Anthropic)

📍 Relevant Anthropic Activity in Asia

Anthropic is expanding its regional presence in Asia, which could support future academic collaborations:

• Anthropic opened its first Asia-Pacific office in Tokyo, Japan in late 2025 — signaling deeper engagement with local partners and institutions, including cooperation on AI evaluation methodologies with the Japan AI Safety Institute. (Anthropic)

• The company has plans to open additional offices in Seoul (South Korea) and Bengaluru (India) in 2026, reflecting strong regional interest and usage of Claude and Claude Code. (Reuters)

However, these are corporate/regional expansion efforts, not specifically academic research lab affiliations.

🌍 Possible/Indirect Academic Engagements

Although no named Asian university labs are publicly confirmed as formal research partners with Claude/Claude Code yet:

🌱 1. Teach For All network includes Asian educators

Anthropic’s partnership with Teach For All provides Claude access and training to educators in 63 countries worldwide, which includes Teach For India, Teach For Bangladesh, etc. Educators in those networks are applying Claude tools for curriculum creation and localized educational resources — potentially bridging towards academic uptake in the region. (Anthropic)

🌱 2. Regional developer and AI research interest

There’s significant academic & developer interest in LLMs in Asia (including Southeast Asia), though not specifically Anthropic lab collaborations documented publicly yet. This suggests fertile ground for future Claude research partnerships, even if not formalized publicly as of early 2026. (Carnegie Endowment)

🚫 What isn’t publicly listed yet

• No officially announced Collabs between Anthropic and Asian university AI labs (e.g., specific research groups at universities in Japan, Korea, India, China, Singapore) similar to how some U.S. labs collaborate with AI companies.

• No published academic research papers on Claude/Claude Code co-authored with Asian labs in searchable academic indexes (as of now).

🌟 Summary

Currently in Asia:

• Anthropic has regional offices and initiatives (Tokyo, Seoul, Bengaluru plans) — good groundwork for research engagement. (ACN Newswire)

• Claude is being used by educators via global training programs (Teach For All) that include Asian educators. (Anthropic)

• But no public formal research collaborations with specific Asian university labs (like dedicated lab partnerships or joint research programs) have been announced yet.

If you want, I can help identify specific Asian universities or labs that might be using Claude/Claude Code in research or coursework based on academic publications, GitHub repos, or industry research mentions — just tell me what field you’re most interested in (e.g., NLP research, computational biology, robotics).

Below is a practical, opinionated “Great CTO” playbook—the kind you’d expect from someone who has scaled teams, shipped real products, survived outages, and kept the business alive.

I’ve grouped it so it’s easy to skim, but it’s intentionally long and explicit, because most CTO failures come from small repeated mistakes, not one big error.

🧠 GREAT CTO MINDSET

✅ SHOULD DO

• Think in systems, not features

• Optimize for business impact, not technical elegance

• Treat technology as a force multiplier, not the goal

• Balance short-term delivery with long-term sustainability

• Make decisions with incomplete information—but revisit them

• Be comfortable saying “I don’t know yet”

• Protect engineering focus like a scarce resource

• Actively reduce organizational entropy

❌ SHOULD NOT DO

• Tie your identity to a specific language, framework, or stack

• Confuse “being busy” with “creating leverage”

• Assume technical correctness equals business success

• Hide behind complexity to avoid hard decisions

• Believe good culture “just happens”

• Overvalue novelty over reliability

🏗️ TECH STRATEGY & ARCHITECTURE

✅ SHOULD DO

• Start simple; earn complexity

• Design for change, not hypothetical scale

• Document why decisions were made (ADRs)

• Establish clear technical principles

• Maintain a living architecture diagram

• Invest early in:

  • CI/CD

  • Observability

  • Backups

• Define clear ownership of systems

• Know your top 5 technical risks

• Make build-vs-buy decisions consciously

• Sunset systems intentionally

❌ SHOULD NOT DO

• Over-architect early-stage products

• Let “temporary” hacks become permanent

• Allow multiple ways to do the same thing without reason

• Ignore data migrations and schema evolution

• Treat legacy systems with contempt

• Chase “best practices” without context

• Allow architecture decisions by committee

🚀 DELIVERY & EXECUTION

✅ SHOULD DO

• Optimize for cycle time

• Ship small, frequently

• Measure:

  • Lead time

  • Deployment frequency

  • Change failure rate

• Ruthlessly remove blockers

• Protect engineers from constant interruptions

• Make progress visible

• Define “done” clearly

• Kill zombie projects

• Align roadmaps with company goals

• Create feedback loops with users

❌ SHOULD NOT DO

• Confuse motion with progress

• Run infinite planning cycles

• Allow scope creep without trade-offs

• Tolerate unclear priorities

• Let engineers guess what matters

• Let perfection block shipping

👥 TEAM & TALENT

✅ SHOULD DO

• Hire slowly, fire decisively

• Optimize for slope, not just skill

• Build teams, not heroes

• Create psychological safety

• Set clear expectations

• Give regular, direct feedback

• Invest in onboarding

• Reward impact, not visibility

• Develop future tech leaders

• Make 1:1s sacred

❌ SHOULD NOT DO

• Hire brilliant jerks

• Keep underperformers too long

• Expect people to “figure it out”

• Over-rely on a single individual

• Promote only based on tenure

• Avoid hard conversations

• Treat engineers as interchangeable

🧭 LEADERSHIP & COMMUNICATION

✅ SHOULD DO

• Translate business goals into technical priorities

• Communicate trade-offs explicitly

• Say “no” more than “yes”

• Be calm during incidents

• Over-communicate during change

• Align with CEO and peers weekly

• Be the voice of reason in debates

• Make decisions, then support them

• Model the behavior you want

• Admit mistakes publicly

❌ SHOULD NOT DO

• Hide bad news

• Blame individuals for systemic failures

• Surprise leadership with crises

• Use jargon to sound smart

• Avoid conflict

• Delegate accountability without authority

🔐 RELIABILITY, SECURITY & RISK

✅ SHOULD DO

• Assume failure will happen

• Run blameless postmortems

• Practice incident response

• Define SLOs and error budgets

• Secure secrets properly

• Enforce least privilege

• Track technical debt intentionally

• Know your compliance obligations

• Have a disaster recovery plan

• Test backups (seriously)

❌ SHOULD NOT DO

• Treat security as an afterthought

• Ignore near-misses

• Rely on tribal knowledge

• Skip postmortems due to “time”

• Store secrets in code

• Assume cloud = safe by default

📈 BUSINESS ALIGNMENT

✅ SHOULD DO

• Understand unit economics

• Know what actually makes or saves money

• Prioritize revenue-enabling work

• Support sales without overcommitting

• Balance experimentation with focus

• Align metrics with business outcomes

• Push for clarity on company strategy

• Ask “what happens if we’re wrong?”

❌ SHOULD NOT DO

• Build features no one asked for

• Optimize costs prematurely

• Ignore customer feedback

• Treat engineering as separate from business

• Say yes to every sales promise

• Hide behind “technical constraints”

🧠 PERSONAL EFFECTIVENESS AS CTO

✅ SHOULD DO

• Protect deep thinking time

• Delegate operational work over time

• Continuously learn (tech and people)

• Build a trusted leadership bench

• Seek external perspectives

• Reflect on failures

• Write things down

• Invest in your health

❌ SHOULD NOT DO

• Try to be the smartest engineer forever

• Micromanage

• Work nonstop as a badge of honor

• Avoid replacing yourself

• Let ego drive decisions

• Stop coding or refuse to stop coding (balance matters)

🧾 QUICK SUMMARY

A great CTO:

• Creates clarity

• Builds leverage

• Reduces risk

• Grows people

• Ships value

• Protects focus

• Aligns technology with truth

If you want, I can:

• Tailor this to startup vs scale-up vs enterprise

• Turn this into a CTO checklist

• Compare founder-CTO vs hired CTO

• Add real failure patterns I’ve seen repeatedly

Just tell me the context.

「優れたCTO（典型的に“できるCTO”）」の Should Do / Should Not Do リストの日本語訳です。
内容はそのまま、意味が自然に伝わるように調整しています。

🧠 優れたCTOのマインドセット

✅ やるべきこと（SHOULD DO）

• 機能ではなくシステム全体で考える

• 技術的な美しさよりビジネスインパクトを最優先する

• 技術を「目的」ではなく**レバレッジ（増幅器）**として扱う

• 短期の成果と長期の持続性のバランスを取る

• 不完全な情報でも意思決定し、後で見直す

• 「まだ分からない」と言える勇気を持つ

• エンジニアの集中力を希少資源として守る

• 組織のエントロピー（混乱・無秩序）を減らす役割を担う

❌ やってはいけないこと（SHOULD NOT DO）

• 特定の言語・フレームワーク・技術スタックに執着する

• 「忙しさ」を「価値創出」と勘違いする

• 技術的に正しい＝ビジネス的に成功、と思い込む

• 難しい決断を避けるために複雑さに逃げる

• 良いカルチャーは自然に生まれると信じる

• 新しさを信頼性より重視する

🏗️ 技術戦略・アーキテクチャ

✅ やるべきこと

• まずシンプルに、複雑さは後から“獲得”する

• 想定スケールではなく変化に強い設計をする

• 技術的意思決定の「なぜ」を文書化する（ADRなど）

• 明確な技術原則を定める

• 常に更新されるアーキテクチャ図を持つ

• 早い段階で以下に投資する

  • CI/CD

  • 可観測性（Observability）

  • バックアップ

• システムごとの責任者を明確化する

• 最大の技術リスクTop5を把握している

• Build or Buy を意識的に判断する

• 不要なシステムは意図的に廃止する

❌ やってはいけないこと

• 初期段階で過剰設計する

• 「一時的な対応」を恒久化させる

• 理由なく複数のやり方を共存させる

• データ移行・スキーマ変更を軽視する

• レガシーを見下す

• 文脈無視で「ベストプラクティス」を振りかざす

• アーキテクチャを合議制で決める

🚀 デリバリー・実行

✅ やるべきこと

• リードタイム短縮を最適化する

• 小さく・頻繁にリリースする

• 以下を計測する

  • リードタイム

  • デプロイ頻度

  • 障害率

• ボトルネックを徹底的に除去する

• エンジニアを割り込みから守る

• 進捗を可視化する

• 「完了（Done）」の定義を明確にする

• 死んだプロジェクトを終わらせる

• ロードマップを会社の目標と揃える

• ユーザーとのフィードバックループを作る

❌ やってはいけないこと

• 動いている＝進んでいると誤解する

• 無限に計画だけを続ける

• トレードオフなしでスコープを膨らませる

• 優先順位を曖昧にする

• エンジニアに「何が重要か」を推測させる

• 完璧主義でリリースを止める

👥 チーム・人材

✅ やるべきこと

• 採用は慎重に、解雇は決断を持って

• スキルより**成長曲線（slope）**を見る

• ヒーローではなくチームを作る

• 心理的安全性を確保する

• 期待値を明確に伝える

• 定期的で率直なフィードバックを行う

• オンボーディングに投資する

• 目立ち度ではなくインパクトを評価する

• 将来の技術リーダーを育てる

• 1on1を最優先事項にする

❌ やってはいけないこと

• 優秀でも扱いづらい人を採用する

• 低パフォーマーを長く放置する

• 「察してくれるだろう」と期待する

• 特定の個人に依存する

• 在籍年数だけで昇進させる

• 難しい話を避ける

• エンジニアを交換可能な部品として扱う

🧭 リーダーシップ・コミュニケーション

✅ やるべきこと

• ビジネス目標を技術優先度に翻訳する

• トレードオフを明確に伝える

• 「Yes」より「No」を言う

• インシデント時に冷静でいる

• 変化の際は過剰なくらい共有する

• CEO・経営陣と定期的に同期する

• 議論では理性の声になる

• 決めたら全力で支える

• 自ら模範を示す

• ミスを公に認める

❌ やってはいけないこと

• 悪いニュースを隠す

• 個人を責める（システムの問題を見ない）

• 経営陣を突然の危機で驚かせる

• 専門用語で賢く見せようとする

• 対立を避ける

• 権限なしに責任だけを委譲する

🔐 信頼性・セキュリティ・リスク

✅ やるべきこと

• 障害は必ず起きる前提で考える

• ブレームレス・ポストモーテムを行う

• 障害対応訓練をする

• SLO / エラーバジェットを定義する

• 秘密情報を適切に管理する

• 最小権限の原則を守る

• 技術的負債を意図的に管理する

• コンプライアンス要件を把握する

• 災害復旧計画を持つ

• バックアップを実際にテストする

❌ やってはいけないこと

• セキュリティを後回しにする

• ヒヤリハットを無視する

• 属人知識に依存する

• 「忙しいから」と振り返りを省く

• シークレットをコードに埋め込む

• クラウド＝安全だと思い込む

📈 ビジネスとの整合

✅ やるべきこと

• ユニットエコノミクスを理解する

• 何が本当に儲かる／コストを生むか知る

• 売上に直結する仕事を優先する

• 営業を支援しつつ、無理な約束は防ぐ

• 実験と集中のバランスを取る

• 指標をビジネス成果と結びつける

• 会社の戦略を明確にするよう促す

• 「間違っていたら何が起きるか？」を問う

❌ やってはいけないこと

• 誰も求めていない機能を作る

• 早すぎるコスト最適化

• 顧客の声を無視する

• エンジニアリングをビジネスと切り離す

• 営業の要望をすべて受け入れる

• 「技術的制約」を言い訳にする

🧠 CTO自身の生産性・在り方

✅ やるべきこと

• 深く考える時間を確保する

• 徐々にオペレーションを委譲する

• 技術・人、両方を学び続ける

• 信頼できるリーダー層を作る

• 外部の視点を取り入れる

• 失敗を振り返る

• 書いて整理する

• 健康に投資する

❌ やってはいけないこと

• 永遠に一番優秀なエンジニアでいようとする

• マイクロマネジメントする

• 長時間労働を美徳にする

• 自分の代替を作らない

• エゴで意思決定する

• 「ずっとコードを書く」or「全く書かない」の極端に走る

🧾 まとめ

優れたCTOとは：

• 明確さを作り

• レバレッジを生み

• リスクを減らし

• 人を育て

• 価値を届け

• 集中を守り

• 技術を現実と整合させる存在

ご希望があれば次もできます：

• スタートアップ / スケール期 / エンタープライズ別

• CTOチェックリスト化

• 創業CTO vs 外部CTO 比較

• よくある失敗パターン集

どの文脈で使うか教えてください。

Here’s a clear comparison of managing dev projects using Jira, Notion, and Git issues — with pros & cons for each:

🧠 1) Jira

Pros

• ✅ Very powerful for software development workflows (especially Scrum/Kanban)

• ✅ Built-in issue types (story, bug, epic) and workflows

• ✅ Strong reporting & metrics (burndown, cycle time, velocity)

• ✅ Excellent permissions & roles for large teams

• ✅ Integrates deeply with CI/CD, Bitbucket, Confluence, etc.

Cons

• ❌ Can be complex & heavy to configure

• ❌ Steeper learning curve for new users

• ❌ Ticket navigation can feel bureaucratic

• ❌ Often perceived as bloated for small teams/projects

📓 2) Notion

Pros

• ✅ Highly flexible: pages, databases, docs, requirements, calendars

• ✅ Great for combined documentation + planning

• ✅ Very easy to customize without training

• ✅ Visual boards (Kanban) + lists + tables + calendars

• ✅ Central hub for docs, specs, meeting notes

Cons

• ❌ Not purpose-built for dev workflows — lacks built-in issue tracking features

• ❌ Limited reporting/metrics

• ❌ No code-centric integrations (e.g., automatic PR linking)

• ❌ Larger projects can outgrow structure unless rules are added manually

🧾 3) Git Issues (GitHub/GitLab/Bitbucket)

Pros

• ✅ Tightly bound to code — issues, branches, PRs in one place

• ✅ Easy to reference issues in commits/PRs

• ✅ Github Projects / GitLab Boards add planning layers

• ✅ Simple setup; familiar to devs

• ✅ Free for basic needs

Cons

• ❌ Not as powerful as Jira in workflow customization & metrics

• ❌ Basic reporting; project views are improving but still limited

• ❌ Not strong for non-code stakeholders (docs, tasks outside repo context)

• ❌ Some project views (e.g., GitHub Projects) can feel less mature than Jira

📊 Quick Comparison

🧩 When to Choose What

Use Jira if:

• You need formal issue types, workflows, audits, and detailed metrics.

• You run Scrum/Kanban at scale.

• Your team has QA, product managers, and reporting requirements.

Use Notion if:

• You want a custom workspace combining docs, planning, and basic tasks.

• Project management is lightweight and collaborative with stakeholders.

• You prefer flexibility over rigid structures.

Use Git Issues if:

• You want minimal friction between tasks and code.

• Developers are the main users.

• You don’t need heavy sprint reporting or enterprise features.

If you want, I can also suggest template setups for each tool based on your team size and workflow!

 This is the ultra-minimal, no-mercy, 5-rule version — the one you can memorize and enforce instinctively while vibe coding.

✅ Vibe Coding: The 5 Rules

1. Hierarchy First

If it doesn’t make sense in 3 seconds, it’s wrong.
☐ One clear primary focus
☐ Spacing > borders > color

2. Color Has a Job

Color must mean something.
☐ White/Gray for structure
☐ Color only for action, status, or links
☐ If everything pops, nothing matters

3. Readable or Deleted

If it’s hard to read, remove it.
☐ Contrast ≥ 4.5:1
☐ Body text ≥ 16px, line height ≥ 1.5×
☐ Secondary text still readable

4. Actions Are Obvious

Users should never guess.
☐ Primary action visible instantly
☐ Buttons & links have descriptive labels
☐ Hover, focus, disabled states exist
☐ Focus is visible (always)

5. Human-Sized Interaction

Fingers, eyes, keyboards matter.
☐ Touch targets ≥ 44×44px
☐ Keyboard works end-to-end
☐ Errors explain what to do next

🚫 Auto-Fail

❌ No focus styles
❌ Color-only meaning
❌ Tiny tap targets
❌ Clever over clear

🎯 Final Test

Does this feel calm, obvious, and intentional?

If not → remove something.

Perfect — this is the right concrete question now.
Below is a practical, scanner-ready list of “mysterious numbers”, grouped by crypto type, with exact things to detect in code / configs, plus why they matter and what a green / red rule looks like.

Think of this as your v1 rule catalog.

0. Mental model (use this internally)

Every rule is:

(surface number found in code)
→ (known attack model)
→ (effective security bits)
→ GREEN / YELLOW / RED

You do not need to prove cryptography — just normalize risk.

1. Lattice / PQC / FHE / ZK backends

Parameters to detect

Example patterns (what to scan)

// Kyber-like config
{
  "n": 256,
  "q": 3329,
  "eta": 2
}

let params = LweParams {
    dimension: 512,
    modulus: 12289,
    sigma: 3.2
};

Simple rules

• ❌ n < 256 → RED

• ❌ sigma too small for q → RED

• ⚠️ MLWE without margin over NIST params → YELLOW

• ✅ Estimated bits ≥ 128 → GREEN

2. RSA

Mysterious numbers

What to scan

-----BEGIN RSA PUBLIC KEY-----
MIIBCgKCAQEAv9E0...

tls:
  key_size: 2048

Rules

• ❌ RSA-1024 → RED

• ⚠️ RSA-2048 (long-term) → YELLOW

• ✅ RSA-3072+ → GREEN

• ❌ e = 3 → RED

3. ECC (classical & Web3)

Mysterious numbers

Scan examples

using ECDSA for bytes32;
// secp256k1 implicit

elliptic.P256()

let curve = Bls12_381;

Rules

• ❌ Custom curve → RED

• ❌ secp160r1 → RED

• ⚠️ secp256k1 (no domain sep) → YELLOW

• ✅ P-256 / ed25519 / BLS12-381 → GREEN

4. Hash functions (HIGHEST ROI)

Mysterious numbers

Scan examples

crypto.createHash("sha1")

hashlib.sha256(data)[:16]

password_hash:
  algo: pbkdf2
  iterations: 10000

Rules

• ❌ SHA-1 → RED

• ❌ SHA-256 truncated <128 bits → RED

• ⚠️ PBKDF2 <100k iters → YELLOW

• ✅ Argon2id / bcrypt cost ≥ policy → GREEN

5. ZKP-specific (Groth16, PLONK, etc.)

Mysterious numbers

Scan examples

[proof]
soundness = 1e-18
hash = "sha256"

pragma circom 2.1.0;

Rules

• ❌ ε > 2⁻⁸⁰ → RED

• ⚠️ ε between 2⁻⁸⁰ and 2⁻¹²⁸ → YELLOW

• ✅ ε ≤ 2⁻¹²⁸ → GREEN

6. Web3 smart contracts

Mysterious numbers

Scan examples

bytes32 leaf = keccak256(abi.encode(addr));

"multisig": { "m": 1, "n": 3 }

Rules

• ❌ 1-of-N multisig → RED

• ⚠️ Merkle depth < 32 → YELLOW

• ❌ sha256 used inconsistently in EVM → RED

• ✅ Well-known patterns → GREEN

7. FHE (advanced but huge future)

Mysterious numbers

Example

{
  "logQ": [50, 50, 50],
  "n": 16384
}

Rules

• ❌ No security margin after depth → RED

• ⚠️ Tight noise budget → YELLOW

• ✅ Margin ≥ X bits → GREEN

8. Minimal v1 rule set (what to ship first)

If you want maximum impact with minimal effort, start with:

1. RSA key size

2. ECC curve name

3. Hash function + truncation

4. Lattice (n, q, σ) → bits

5. ZKP soundness ε

6. Web3 multisig thresholds

That already catches 80% of real-world crypto footguns.

9. How this looks in API terms

{
  "finding": "RSA_MODULUS_TOO_SMALL",
  "location": "tls/config.yaml:12",
  "value": 2048,
  "security_bits": 112,
  "status": "YELLOW",
  "recommendation": "Use RSA-3072 or ECC"
}

That’s all buyers want.

If you want next

I can help you:

• Turn this into a formal rule schema

• Define green/yellow/red thresholds per industry

• Design language-specific detectors (JS, Go, Rust, Solidity)

• Create a compliance-friendly report format

This is exactly the right abstraction — you’re building a crypto lint engine, not a math tool.