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Hacker News - Newest: "AI"

AI can't read an investor deck AI as an attorney? Student uses ChatGPT, Gemini to sue UW over alleged racial discrimination Hacking MCP Servers in AI Systems – The Rug Pull: Tool Changes After Approval GitHub - MeepCastana/KubeezCut: Free Web based video editor Can AI judge journalism? A Thiel-backed startup says yes, even if it risks chilling whistleblowers Coming soon: 10 Things That Matter in AI Right Now DARPA built an AI to fact-check enemy weapons claims What explains heterogeneity in AI adoption? When AI Meets Muscle: Context-Aware Electrical Stimulation Promises a New Way to Guide Human Movements - Department of Computer Science AI Changed How We Build. It Did Not Change What Matters. Linux rules on using AI-generated code - Copilot is OK, but humans must take 'full responsibility for the… Meta spins up AI version of Mark Zuckerberg to engage with employees Code Mode: Let Your AI Write Programs, Not Just Call Tools | TanStack Blog GitHub - Delavalom/graft: Go framework for building AI agents. Type-safe tools, multi-provider (OpenAI, Anthropic, Gemini, Bedrock), zero vendor SDKs. India's TCS tops estimates, says new AI models did not dent services demand Gen Z's fading AI hype Strong feeling: we are in a folded AI reality GitHub - machinarii/total-recall-catalog: A reference catalog of latest knowledge retrieval, memory & RAG systems GitHub - mensfeld/code-on-incus: Give each AI agent its own isolated machine with root, Docker, and systemd. Active defense detects and stops threats automatically.. Quantization, LoRA, and the 8% Problem: Benchmarking Local LLMs for Production AI Iran war: We spoke to the man making Lego-style AI videos that experts say are powerful propaganda Powell, Bessent discussed Anthropic's Mythos AI cyber threat with major U.S. banks GitHub - immartian/bellamem: Persistent belief-graph memory for AI agents. Retrieves decisive context by importance — not recency, not RAG, not /compact. recursive-mode: The Repo-Native Operating System for AI Engineering After the attack on Sam Altman's home, will AI CEO's go on the offensive? The biggest advance in AI since the LLM Opus 4.6 vs GPT 5.4 One Prompt Unity World Generation Test “AI polls” are fake polls Client Challenge Can AI be a 'child of God'? Inside Anthropic's meeting with Christian leaders
GitHub - bjcoombs/ai-native-toolkit: Claude Code configur...
ben30 · 2026-05-22 · via Hacker News - Newest: "AI"

A Claude Code plugin: skills, agents, and commands for AI-native development. Runs locally in your Claude Code session, against your own codebase, using whichever model you're already paying for - nothing leaves your machine beyond what Claude Code itself sends.

The headline pieces are two skills:

  • /assess - score any codebase's readiness for AI agent contributors against a 7-layer contract model, with a Codecov-style complexity hotspot SVG. Generates a report + treemap and opens a PR in the target repo.
  • /huddle - structured multi-perspective deliberation using Six Thinking Hats with Fibonacci team sizing (solo -> debate -> huddle -> panel -> board).

What /assess produces

Example complexity hotspot from a real codebase

Real output from /ai-native-toolkit:assess run against a ~150k-LOC private monorepo (file paths sanitized). Size encodes lines of code, hue encodes cyclomatic complexity (red = high), saturation encodes recent git churn (vivid = active). Vivid red blocks are the migration risk an agent (or human) is most likely to break next week. Hover any block for its LOC, CCN, and recent commit count.

Alongside the SVG you get an assess-report.md: a 0-7 layered readiness score (breadcrumbs, type safety, linters, architecture tests, CI, coverage, review bots, AI project management), three concrete leverage actions naming specific files, and a stats sidecar (complexity-stats.json) with percentiles and ranked hotspot lists.

The skill runs locally - lizard, optional scc, and git log do the analysis in your Claude Code session. No data leaves the machine.

Portability split. The framework pieces (/assess, /huddle, /6hats, /understand and their agents) are portable and work in any Claude Code session. The workflow commands (/tm, /fix-pr, /fix-develop) bake in one author's daily setup: a <repo>-main/ + worktree/ layout, GitHub + gh CLI, Task Master, CodeRabbit/claude[bot] review threads, and the Agent Teams capability flag. See Adapting before relying on them in a different setup.

Install

From inside a Claude Code session (not a shell - /plugin is a Claude Code command):

/plugin marketplace add https://github.com/bjcoombs/ai-native-toolkit
/plugin install ai-native-toolkit@ai-native-toolkit

Skills appear namespaced: /ai-native-toolkit:assess, /ai-native-toolkit:huddle. Update with /plugin update ai-native-toolkit. Remove with /plugin remove ai-native-toolkit. The plugin doesn't touch your existing ~/.claude/ files.

If you'd rather have this repo be your entire Claude Code config, see Full clone install below.

Try it

Assess a codebase

/ai-native-toolkit:assess

Runs against the current directory (or pass a path). Produces:

  • .assess/assess-report.md - layered score, top 3 leverage actions, hotspot callouts
  • .assess/complexity-heatmap.svg - the treemap shown above
  • .assess/complexity-stats.json - percentiles and ranked file lists that feed the report

After writing, the skill asks whether to open a PR in the target repo with both files.

Run a huddle on a hard decision

/ai-native-toolkit:huddle Should we migrate the monolith to microservices?

Spawns a Fibonacci-sized team (default 3) that cycles through De Bono's six hats - facts, feelings, risks, benefits, alternatives, synthesis - and returns a structured recommendation. Use /6hats <q> for a faster solo variant.

What's in the box

Skills (auto-discovered by Claude Code)

Skill Description
/assess Layered AI-readiness assessment (0-7 contract model) plus a Codecov-style complexity hotspot SVG. Ships complexity-treemap.py so the agent runs the treemap with no external setup. Generated PRs include a self-install footer so reviewers can adopt the plugin.
/huddle Multi-perspective deliberation using Six Thinking Hats with Fibonacci team sizing (solo -> debate -> huddle -> panel -> board). Three execution modes: solo flat-parallel, phased sub-agent (default fallback), and team mode (needs Agent Teams capability flag).

Commands (slash-only, no bundled assets)

Portable:

Command Description
/6hats <question> Solo Six Hats analysis - alias for /huddle at team size 1
/understand <thing> Deep understanding mode (nemawashi) - exhaustive context-gathering before action

Workflow (personal setup, opt-in - see Adapting):

Command Description
/tm Task Master orchestration - context-aware: starts, reviews, or cleans up tasks based on current state
/tm-marathon-config-example Reference configuration block to drop into a project's CLAUDE.md for marathon-mode /tm
/fix-pr Autonomous PR fixing loop - iterates on CI failures and review comments until green
/fix-develop Autonomous fix loop for failing CI on the repo's default branch

Agents (invoked by skills, or directly via Task(subagent_type=...))

The Six Hats team that /huddle and /6hats orchestrate:

Agent Role
white-hat Facts and evidence
red-hat Gut feelings and emotional drivers
black-hat Risks and critical analysis
yellow-hat Benefits and opportunities
green-hat Creative alternatives
blue-hat Synthesis and recommendation
scribe Structures hat output into actionable documentation

Why Six Hats?

A blind spot detector for high-stakes decisions, based on Edward de Bono's method.

Cost: 5-10x the tokens of a single prompt. Six parallel agents plus synthesis adds up.

Benefit: Catches the question you didn't know to ask. Black Hat might reveal your "performance optimization" is really about deployment fear. Green Hat might find the lazy solution that actually works.

Real example: an earlier draft of this README was reviewed via /6hats review please. Black Hat called it "a 13-point solution to a 2-point problem" with "rigged comparisons" and "zero evidence." That critique led to a rewrite. A single prompt wouldn't have been that harsh.

Use it for: architecture decisions you can't easily reverse, "should we..." questions where you suspect you're asking the wrong question, decisions where being wrong costs 100x more than the analysis, when you want pushback rather than validation.

Skip it for: routine decisions, debugging, implementation details, anything you can reverse. For those, a single well-crafted prompt is enough:

Help me decide [X]. Be opinionated. If this is a bad idea, say so directly.
What am I not considering? What's the lazy solution that might work?

Adapting for your workflow

The framework pieces (/assess, /huddle, /6hats, /understand and their agents) are reusable as-is. The workflow commands embed assumptions you will likely need to override:

  • Directory layout - commands/tm.md, commands/fix-pr.md, commands/fix-develop.md all assume ~/dev/github.com/<org>/<repo>/<repo>-main/ + sibling worktree/. Edit the path patterns to match your structure.
  • Default branch - /fix-develop derives the branch via gh repo view --json defaultBranchRef. /tm uses a $BASE_BRANCH variable. Other commands may still reference develop in prose; check before relying on them on a main-default repo.
  • Required external tools - gh CLI for GitHub, Task Master for /tm, optional Agent Teams capability flag ($CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS) for /tm marathon mode.
  • Review-bot conventions - PR-loop logic in /tm, /fix-pr, /fix-develop distinguishes CodeRabbit, claude[bot], and human threads. Adjust if your repo uses different bots.
  • CLAUDE.md - your global / project CLAUDE.md references to the directory structure need to match.

Git workflow assumed by /tm, /fix-pr, /fix-develop

~/dev/github.com/<org>/<repo>/
├── <repo>-main/                    # SACRED - always on default branch, never modified
└── worktree/
    ├── <tag>/                      # Task Master tag folder (nested)
    │   ├── 1--create-schema/       # Task worktree
    │   └── 2--add-api/             # Another task worktree
    └── fix-login-bug/              # Non-TM worktree (flat)

The key principle: never work directly on the default branch. <repo>-main/ stays on develop/main and clean; all work happens in worktrees. The /tm command handles worktree creation and cleanup automatically. To create one by hand:

cd ~/dev/github.com/<org>/<repo>/<repo>-main
git checkout develop && git pull origin develop
git branch fix-login-bug
git worktree add ../worktree/fix-login-bug fix-login-bug
cd ../worktree/fix-login-bug
# work, commit, push, create PR

Full clone install

If you want this repo to be your entire Claude Code config (instead of installing it as a plugin):

git clone git@github.com:bjcoombs/ai-native-toolkit.git ~/.claude/

If ~/.claude/ already exists and you only want the skills/commands/agents:

git clone git@github.com:bjcoombs/ai-native-toolkit.git /tmp/ai-native-toolkit
cp -r /tmp/ai-native-toolkit/skills   ~/.claude/
cp -r /tmp/ai-native-toolkit/agents   ~/.claude/
cp -r /tmp/ai-native-toolkit/commands ~/.claude/

Repository structure

ai-native-toolkit/
├── README.md
├── .claude-plugin/
│   ├── plugin.json                    # Plugin manifest (enables /plugin install)
│   └── marketplace.json               # Marketplace entry (enables /plugin marketplace add)
├── skills/
│   ├── assess/
│   │   ├── SKILL.md                   # Codebase readiness assessment + complexity hotspot
│   │   └── scripts/
│   │       └── complexity-treemap.py  # Codecov-style hotspot SVG generator
│   └── huddle/
│       └── SKILL.md                   # Multi-lens Six Hats deliberation
├── commands/
│   ├── tm.md
│   ├── tm-marathon-config-example.md
│   ├── 6hats.md
│   ├── understand.md
│   ├── fix-pr.md
│   └── fix-develop.md
├── agents/
│   ├── white-hat.md
│   ├── red-hat.md
│   ├── black-hat.md
│   ├── yellow-hat.md
│   ├── green-hat.md
│   ├── blue-hat.md
│   └── scribe.md
└── docs/
    └── example-heatmap.svg            # Sanitized real-world /assess output (README hero)

License

Licensed under the Apache License, Version 2.0 - see LICENSE for the full text.

  • The Six Thinking Hats method is the intellectual property of Edward de Bono. Licensing covers only this implementation, not the underlying methodology.
  • The Task Master commands are designed for use with Claude Task Master by Eyal Toledano.