Report by The Next New Thing.
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Instead of wading through “Great question!” and three paragraphs to find the one command you need, this makes your coding assistant put the action first, number the steps, and stop with a single next step.
View on GitHub →Action first, steps numbered, lists capped at five, no preamble or closers. The whole skill is a single SKILL.md you can fork and edit.
The README shows the same auth fix twice: a chatty paragraph versus one command, three numbered steps and one next action.
Explains fully when asked to explain, confirms before destructive actions, and after three failed fixes stops to name the assumption that may be wrong.
Explains fully — still no preamble, still no closer, but the body runs as long as the topic needs, with headers to skim back.
rm -rf, force push, schema migration, dropping a table: it confirms before acting. Safety wins over brevity.
Stops iterating on code, names the assumption that might be wrong, and asks one diagnostic question.
One short clarifying question beats guessing and rewriting.
The biggest HN thread in this week’s lineup, three days before the show, 371 comments. Top comment: “Claude (in particular) is a terrible writer… a whole cottage industry of skills and CLAUDE.md instructions trying to push it toward writing better.” The pushback is sharp — one commenter with ADHD says it “feels weird when people who obviously do not have it, claim to have it,” another says the conciseness only lasts “a few turns at most.” Bonus: repo issue #64, titled “i have adhd,” body in full: “i have adhd, the README is too long, didnt read.”
Open the thread on Hacker News →Think of it as a full engineering department bolted onto your AI coding assistant: it plans before it builds, writes tests first, reviews its own work with fresh eyes, remembers what it learned, and scans its own setup for security holes.
View on GitHub →Planning, review, build repair and security specialists as one Claude Code plugin; Codex supported, with adapters for Cursor, Gemini, Copilot and more.
Plans become editable artifacts, test-driven work runs as a gated red-green-refactor loop with evidence, and a separate reviewer checks the code.
AgentShield audits your agent, hook, MCP (the plug for outside tools), permission and secret surfaces; GateGuard blocks destructive shell commands first.
AI agent security scanner. Detect vulnerabilities in agent configurations, MCP servers, and tool permissions. Available as CLI, GitHub Action, ECC plugin, and GitHub App integration. 🛡️
JARVIS: a real-time agentic intelligence-gathering platform powered by autonomous web scraping & OSINT, streamed via Meta Ray-Ban smart glasses
Multi-agent orchestration for Claude Code — decompose tasks, coordinate agents, visualize everything in a rich terminal UI
/ecc:plan “describe the feature”, then the tdd-workflow skill writes the failing test before the code.
Reproduce it with a failing test first, then use tdd-workflow to implement the fix and prove it passes.
/code-review runs a fresh-context reviewer that never saw the code being written.
/save-session or /learn-eval distils the session into summaries and reusable patterns; /resume-session picks it back up.
/security-scan with AgentShield checks your own hooks, MCP servers, permissions and secrets.
Imagine a veteran programmer who looks at fifty lines of code and quietly replaces them with one. Ponytail puts that instinct inside your AI coding agent, so it stops over-building and writes only what the job actually needs.
View on GitHub →Does this need to exist? Already in the codebase? Standard library? Native feature? Installed dependency? One line? Only then the minimum that works.
−54% lines, −22% tokens, −20% cost, −27% time versus no skill on real Claude Code sessions; the old 80–94% claim was corrected after issue #126.
Validation, error handling, security and accessibility are never cut; it scored 100% on the adversarial safety test where a bare one-liner prompt scored 95%.
Set the intensity or turn it off; no argument reports the current level. “ultra exists for when the codebase has wronged you personally.”
Reviews the current diff for over-engineering and hands back a delete-list.
Audits the whole repo for over-engineering, not just the diff.
Harvests the shortcuts you deferred into a ledger, so “later” doesn’t become “never.”
Shows the measured impact scoreboard: less code, less cost, more speed.
Colin Eberhardt of Scott Logic filed the benchmark critique directly against the repo, and the owner answered: “You’re right the headline claim is too strong, I’ll make it modest once the numbers are fair… Part of that 80–94% was a chatty-baseline artifact… Conceded.” The README dropped from “80–94% less code” to “~54%” as a result. Related: issue #65, where a user measured gpt-4.1-mini falling from 15/15 to 10/15 test passes with ponytail on.
Open the issue on GitHub →Imagine giving your AI helper a big key ring. One connection lets it use thousands of apps, such as Gmail and Notion, to do everyday chores for you.
zapier.com/mcpIf you write software, this is the same key ring handed to your program instead of your chatbot — a few lines of code and your app can use those thousands of apps too.
zapier.com/sdkYou can use it to fix a quiet problem: Claude Code is told your project-instructions file “may or may not be relevant,” so it ignores parts of it. One of these five add-ons rewrites that file so each rule only fires when it applies.
View on GitHub →Rewrites your CLAUDE.md so each rule sits in an <important if> block with a narrow trigger; project identity, map and stack stay bare at the top.
Explains the current topic with concise diagrams, code-shape sketches and focused HTML artifacts. Added 2026-08-09, the newest of the five.
Interviews you about your codebase, then builds a sensor-controller-actuator loop as local components plus a scheduled coding-agent workflow.
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
ACP is the Agent Control Plane - a distributed agent scheduler optimized for simplicity, clarity, and control. It is designed for outer-loop agents that run without supervision, and make asynchronous tool calls like requesting human feedback on key operations. Full MCP support.
Rewrites your CLAUDE.md using <important if> blocks to improve instruction adherence.
Narrows React component prop types to match live code paths instead of Storybook, test or mock-only states.
Builds a repo-local skill plus an iterated coding-agent GitHub Actions workflow, prompt, memory file and reference templates.
Interviews you to design a sensor-controller-actuator loop for your codebase, then builds it plus a scheduled coding-agent workflow.
Explains the current topic with concise diagrams, code-shape sketches and focused HTML artifacts.
Issue #1, still open with no reply five months later: following the README’s install line pulled in improve-claude-md from Dex Horthy’s personal repo instead of humanlayer/skills. Small, but it explains why a 3.7k-star repo with an 873k-view tweet has almost no other footprint — people find the skills, not the repo.
Open the issue on GitHub →Codex is OpenAI’s AI coding assistant, and this is its official plugin catalog. Each plugin teaches Codex to work with one service — Figma designs, Notion docs, Stripe payments, Slack messages — and most were written by those companies themselves.
View on GitHub →Figma, Notion, Stripe, Shopify, Adobe, Canva, NVIDIA and Slack each authored their own; OpenAI wrote the other 24, including Gmail, GitHub, Outlook and Teams.
A .codex-plugin/plugin.json manifest per folder, plus optional skills (how-to guides), an MCP server (the plug letting an AI call a service), agents and hooks.
The default ‘Codex official’ list has 65 entries (27 developer tools, 12 productivity, 9 creativity); API-key users get a separate 49-entry list.
Lightweight coding agent that runs in your terminal
Robust Speech Recognition via Large-Scale Weak Supervision
Examples and guides for using the OpenAI API
Inspect a Figma design and implement it in code, create Code Connect templates for your components, or build a screen in Figma — the plugin’s own suggested prompts.
Turn specs into implementation plans, synthesize research into structured docs, prep meeting materials, and capture decisions into Notion pages.
Create products, prices and payment links; retrieve and manage payments, subscriptions, invoices, refunds, disputes and customers.
Jesse Vincent’s skills framework, bundled in OpenAI’s own marketplace: brainstorming, planning, test-driven development, debugging and code review.
A community PR to sync the bundled superpowers plugin to its upstream v6.0.3 has sat open since June with 14 comments: “doesn’t a lack of rapid plugin updates directly impact the user experience regarding the latest features?” Recent churn — PR #388 “Sync curated plugins and remove app-only integrations,” PR #390 “Restore missing top-50 curated plugins” — shows the curated list is still being reshuffled.
Open the pull request on GitHub →Instead of opening a drawing tool, you tell your AI coding assistant to map how your system works. Archify hands back a polished, interactive diagram as one HTML file you can click through, export as an image, or share.
View on GitHub →One npx command installs it for Cursor, Claude Code, Codex CLI and OpenCode, or upload archify.zip to Claude.ai. No repository needed — describe it in chat.
Schema, layout, route and label checks must all pass before a new diagram replaces the last good one; a failure returns a repair receipt, not a stack trace.
The result is one self-contained HTML file; the Export menu adds PNG, SVG, WebM and 1200×630 share cards, including a Route Share Card for one traced path.
Browser-native hive-mind for CLI coding agents — Claude Code, Codex, Gemini, and OpenCode collaborate as real PTY processes via a team protocol.
Prove and remove accidental codebase complexity without breaking behavior.
Planning → verifiable goals → fresh-session implementation for AI coding agents. Based on mattpocock/skills v1.1.
Components, services, storage and boundaries. Give it the scope, core components and primary path.
CI/CD pipelines, approvals, tool calls and runbooks. Give it participants, order, branches and exceptions.
API calls, cache fallback, auth and async traces over time. Give it callers, callees, returns and timing.
Pipelines, lineage, PII boundaries and consumers. Give it sources, transforms, stores and boundaries.
States, retries, waits and terminal outcomes. Give it states, events, retry and cancellation paths.
A repo whose pitch is “the validator fails closed” silently accepted a wrong route: the reporter’s API → Queue arrow rendered as Cache → Queue (“the first person who saw the diagram asked ‘why is Redis connected to SQS?’”). The maintainer’s reply: “A diagram that silently makes API → Queue look like Cache → Queue is worse than a failed render” — it became a hard error with three regression tests.
Open the issue on GitHub →Give your AI coding assistant a side room for the messy work. Context Mode runs web fetches, log reads and file scans outside the assistant’s limited memory, hands back only the answer, and reminds it where it left off after that memory resets.
View on GitHub →Raw tool output stays in a sandbox (a walled-off side process); only the result enters the chat. A 56 KB browser snapshot becomes 299 bytes.
File edits, git steps, tasks and decisions go into a local SQLite log; when the chat compacts, it searches that log and restores only the relevant bits.
Claude Code, Codex CLI, Cursor, OpenCode, Zed and more. No telemetry, no cloud sync, no account; Elastic License 2.0 bars resale as a hosted service.
Hatice is an autonomous coding agent orchestration system.
Your browser. Your sessions. Your agent.
Secure autonomous AI agents in 60 seconds.
Research a GitHub repo’s architecture, stack, contributors, issues and activity: 5 calls, 62 KB of context instead of 986 KB (94% saved).
Clone a repo and analyze the last 500 commits — top contributors, frequency by month, most-changed files — in one call, 5.6 KB of context.
Fetch the Hacker News front page, extract titles, scores and domains, group by domain: one call, 3.2 KB of context.
Fetch the React useEffect docs, index them, and find the cleanup pattern with code examples: 2 calls, 1.8 KB of context.
Start a multi-step task, let the chat compact after 20+ tool calls, and the model continues from your last prompt with tasks, files and decisions intact.
The README’s HN badge checks out, and the thread is the real debate. Skeptics: “the claimed 98% context savings are noise without benchmarks of harness performance with and without context mode”; and on the failure mode, “if it writes a git log one-liner when you needed specific commit messages, that information is gone.” Supporters: “I am a happy user of this and have recommended my team also install it.” Inside the repo, issue #1031 measured that context-mode’s own tool definitions cost about 6,219 resident tokens per call — no maintainer reply.
Open the thread on Hacker News →Think of it as a veteran engineer’s 25 daily how-to guides for an AI coding assistant. The most-used one makes the assistant interview you about what you want before it builds anything, so it stops guessing wrong.
View on GitHub →/grill-me interviews you relentlessly about a plan until every branch is resolved. Matt calls the grilling skills his most popular.
/grill-with-docs builds a CONTEXT.md glossary so the agent stops using “20 words where 1 will do.” Matt: the single coolest technique in the repo.
Install as a managed Claude Code plugin from the official marketplace, or copy editable files into your project with npx skills add mattpocock/skills.
A 'CSS reset' for TypeScript, improving types for common JavaScript API's
Orchestrate sandboxed coding agents in TypeScript with sandcastle.run()
AI coding jargon, explained in plain English.
Run /grill-me or /grill-with-docs first. The agent asks you detailed questions about what you’re building until you and it are aligned.
/grill-with-docs builds a CONTEXT.md glossary of your project’s terms so the agent stops using 20 words where 1 will do.
/tdd makes the agent write a failing test first, then fix it. /diagnosing-bugs wraps debugging into a disciplined, phase-gated loop.
/improve-codebase-architecture surveys the codebase for simplification candidates and hands you the list. Matt suggests running it every few days.
A small, pointed thread from ten days ago, mostly skeptical: “If I had a nickel for every dev who has written a ‘productivity suite’ of skills…”; “all this could be one moderately-sized CLAUDE.md”; “Christ, this is exhausting.” The defense: “Matt Pocock is still a nice guy with reasonable opinions and shares a lot with us.” Inside the repo: issue #831 “Can we get the old grill-me back?” (34 reactions) and #1071 “Skill Bloating and Regressions,” filed today: “why are there 3 grilling skills?”
Open the thread on Hacker News →Give it a paragraph that sounds like a chatbot wrote it and it hands back the same points in plain, human-sounding prose. It is one set of directions your AI assistant follows, built from Wikipedia’s list of AI-writing tells.
View on GitHub →“Not X but Y,” one-line closers, forced triads, dashes everywhere, “delve”-type words. The first five justify an edit on a single sighting.
Names, numbers, dates and quotes must come from your source. If a sentence needs a detail that is missing, it asks instead of making one up.
Paste 2–3 paragraphs of your own writing and it follows your rhythm, word choice and punctuation, dashes included.
A Claude Code skill that gives the agent persistent memory of its mistakes via a per-repo markdown scratchpad.
Stop hook for Claude Code that keeps the agent working until all plans and user requests are 100% complete
Claude Code / Codex skill: reverse engineer a product & technical spec from a git branch
“It’s not just X, it’s Y,” a dramatic one-line closer after every section, “Let’s dive in.” State the point directly.
Forced triads, the same sentence opening three times, dashes as the universal connector. Use the number of items the meaning needs.
“Delve,” “testament,” “marking a pivotal moment,” “experts believe.” Keep the fact, drop the significance.
Bold as decoration, title-case headings with emojis, curly quotes. Sentence case; turn a labeled list into prose.
“Great question! I hope this helps!”, knowledge-limit disclaimers, a heading repeated in the first sentence. Remove the wrapper.
The recurring complaint — a user runs the output through GPTZero and it still says 100% AI — and the owner’s blunt answer: “There is no skill or prompt that will enable an LLM to evade Pangram detection (I have tried!), and even if it were possible, avoiding detection from AI detection tools is not a goal of this skill.” That one line reframes what 46,000 people starred. Same theme in #263, closed the same day it was opened.
Open the issue on GitHub →Imagine a menu of ready-made workflows for OpenAI’s Codex coding assistant: deploy this site, fix the failing build check, read this PDF, turn a Figma design into code. You install one by name and Codex knows how to do that job.
View on GitHub →The README’s first line points to openai/plugins (this week’s #5). The 39 curated and 5 system skill folders are still here to read and install.
skill-installer, skill-creator, plugin-creator, imagegen and openai-docs live in .system and are installed automatically in the latest Codex.
Curated folders cover Vercel, Netlify, Render and Cloudflare deploys, fixing failing GitHub CI, Figma, Notion, Linear, Sentry, Playwright and PDFs.
Lightweight coding agent that runs in your terminal
Robust Speech Recognition via Large-Scale Weak Supervision
Examples and guides for using the OpenAI API
vercel-deploy, netlify-deploy, render-deploy and cloudflare-deploy push a project live; yeet stages, commits, pushes and opens a pull request in one flow.
gh-fix-ci reads failing GitHub Actions logs, summarizes the failure and drafts a fix plan; gh-address-comments works through PR review comments.
figma-implement-design turns a Figma file into application code with 1:1 visual fidelity; a family of figma-* skills covers design systems and libraries.
notion-*, linear and sentry skills plus pdf, playwright (drive a real browser), screenshot, transcribe and speech.
migrate-to-codex carries instruction files, skills, agents and MCP config from .claude/ into Codex’s own files.
An official curated skill — the same playwright-interactive Steinberger recommended — depends on a Codex feature that was removed, so it no longer runs; the reporter closed it themselves in July with “this repository has been obsoleted.” 296 issues sit open with at most four comments on any of them, and OpenAI’s own docs still pointed users at the repo two months after the deprecation notice.
Open the issue on GitHub →A single-page dashboard for monitoring AI service credits, cloud billing, and account balances across OpenRouter, Anthropic, xAI, Railway, RunPod, AWS, and more.
View on GitHub →I got tired of having fifteen browser tabs open just to find out how much money I had left — OpenRouter, Anthropic prepaid, xAI, Railway, RunPod, Neon, AWS — none of them telling you you're empty until a job dies at 2am, so I built Reckoner. The web dashboard covers 19 AI and cloud providers; the iOS app with no backend is what I actually use daily, with Keychain credentials, FaceID, and per-provider low-balance alerts.
Building something? Send it to hi@TheNextNewThing.ai
23 agent skills for Claude Code and Codex, rewritten for someone who won't read the code and tied together by one orchestrator session that runs the rest.
View on GitHub →I don't write any code myself, and this skills collection is built for non-engineers and newbies rather than engineers — it has become essential to my workflow. The orchestrate skill is the one I couldn't live without, but they work best as a group, and installing the team-workflow pack sets you up well from day one.
Building something? Send it to hi@TheNextNewThing.ai
A local Go proxy that lets Claude Code run GPT/Codex models through your ChatGPT subscription — same terminal, same tool use, different model underneath.
View on GitHub →A proxy built in Go so you can use GPT models inside Claude.
Building something? Send it to hi@TheNextNewThing.ai
A synthetic customer panel: build 5–15 researched customer composites from your own evidence, have each one read your draft alone, and get a scored verdict before it ships.
View on GitHub →I run a one-person AI consultancy for small businesses on Long Island and built AI Marketing Panel as a synthetic customer panel — you build AI personas from dossiers, calibrate them against real customer evidence, then score marketing copy ship or no-ship before it goes out. The mechanical parts are deterministic scripts; the AI only does the judgment, and it runs under Claude Code, Codex CLI, or any harness that reads skills.
Building something? Send it to hi@TheNextNewThing.ai
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