GITHUB HOT REPOS · SEPTEMBER 11, 2026

Hot GitHub repos
of the week.

Report by The Next New Thing.

Scroll for the report ↓

Ayghri · Output-style skill · MIT

Stop scrolling past chit-chat to find the one command you need. This makes your AI assistant put the answer first and stop.

ELI5

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 →
Stars
40.5k
Language
Python
Rules
10
GitHub repo card for ayghri/i-have-adhd — click to open the repo
Highlights
  • Ten rules, one file

    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 before/after is the whole pitch

    The README shows the same auth fix twice: a chatty paragraph versus one command, three numbered steps and one next action.

  • Knows when to break its own rules

    Explains fully when asked to explain, confirms before destructive actions, and after three failed fixes stops to name the assumption that may be wrong.

Star growth over time for ayghri/i-have-adhd
When it breaks its own rules
  1. You ask it to explain

    Explains fully — still no preamble, still no closer, but the body runs as long as the topic needs, with headers to skim back.

  2. A destructive step is next

    rm -rf, force push, schema migration, dropping a table: it confirms before acting. Safety wins over brevity.

  3. Three fixes in a row failed

    Stops iterating on code, names the assumption that might be wrong, and asks one diagnostic question.

  4. The request is ambiguous

    One short clarifying question beats guessing and rewriting.

@RoundtableSpace — 158 likes, 51,605 views. Third party. “It’s not really just an ADHD thing. It’s just how most people want answers” — and the exact one-line install.
AICodeKing installs it through the plugin marketplace and shows the action-first, numbered-steps output on real coding prompts — 11,534 views. The only i-have-adhd video over 2,000 views.
On Hacker News
Hacker News, 535 points: is this a skill or just one line in CLAUDE.md?

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 →
affaan-m · Agent harness toolkit · MIT

Give your AI coder a whole engineering process — plan, test, build, review, remember — installed once instead of re-explained in every prompt.

ELI5

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 →
Stars
256k
Language
JavaScript
Skills
291
GitHub repo card for affaan-m/ECC — click to open the repo
Highlights
  • 68 agents, 291 skills, hooks and memory

    Planning, review, build repair and security specialists as one Claude Code plugin; Codex supported, with adapters for Cursor, Gemini, Copilot and more.

  • Fresh-eyes review and test-first gates

    Plans become editable artifacts, test-driven work runs as a gated red-green-refactor loop with evidence, and a separate reviewer checks the code.

  • Scans its own setup for security holes

    AgentShield audits your agent, hook, MCP (the plug for outside tools), permission and secret surfaces; GateGuard blocks destructive shell commands first.

Star growth over time for affaan-m/ECC
Where to start, by job
  1. Building a feature

    /ecc:plan “describe the feature”, then the tdd-workflow skill writes the failing test before the code.

  2. Fixing a bug

    Reproduce it with a failing test first, then use tdd-workflow to implement the fix and prove it passes.

  3. Reviewing new code

    /code-review runs a fresh-context reviewer that never saw the code being written.

  4. Ending a long session

    /save-session or /learn-eval distils the session into summaries and reusable patterns; /resume-session picks it back up.

  5. Auditing your agent config

    /security-scan with AgentShield checks your own hooks, MCP servers, permissions and secrets.

@undefinedKi — 4,210 likes, 817,258 views, 11.5k bookmarks. Third party. “The guy who won Anthropic’s hackathon just gave away his entire Claude Code playbook for free” — names Everything Claude Code and the repo path in the body.
Indie Hacker News, published yesterday — 8,260 views. Frames the number that matters: “16th most-starred repository on all of GitHub, sitting above React and above the Linux kernel,” then walks the v2.2.1 install.
Chapters — click to jump
InsiderForce’s Short — 871,075 views and 31,058 likes in nine days, by far the biggest piece of content about any repo this week. A hype read (“64 specialized AI agents. 262 skills…”) with a comment-for-ebook CTA, but it names ECC.
DietrichGebert · Lazy-senior-dev skill · MIT

Your AI agent writes about half as much code for the same job and skips the parts you never needed — measured ~20% cheaper and ~27% faster too.

ELI5

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 →
Stars
135k
Language
JavaScript
Less code
−54%
GitHub repo card for DietrichGebert/ponytail — click to open the repo
Highlights
  • Seven-rung ladder before writing code

    Does this need to exist? Already in the codebase? Standard library? Native feature? Installed dependency? One line? Only then the minimum that works.

  • Measured, then re-measured honestly

    −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.

  • Lazy about the solution, never about safety

    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%.

Star growth over time for DietrichGebert/ponytail
The six commands
  1. /ponytail lite | full | ultra | off

    Set the intensity or turn it off; no argument reports the current level. “ultra exists for when the codebase has wronged you personally.”

  2. /ponytail-review

    Reviews the current diff for over-engineering and hands back a delete-list.

  3. /ponytail-audit

    Audits the whole repo for over-engineering, not just the diff.

  4. /ponytail-debt

    Harvests the shortcuts you deferred into a ledger, so “later” doesn’t become “never.”

  5. /ponytail-gain

    Shows the measured impact scoreboard: less code, less cost, more speed.

@MilesCranmer (ML researcher, PySR) — 2,633 likes, 378,441 views. “Before ponytail skill / After ponytail skill — it’s crazy how much prompt engineering still helps.” The same person opened issue #808 the same day asking whether the repo violates its own principles.
Better Stack — 260,134 views, 6,932 likes. Explains the decision ladder, runs ponytail against default Claude Code side by side, and has a chapter literally called “A Legitimate Critique” at 4:40.
Chapters — click to jump
On GitHub
The owner conceded the headline number on the record

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 →
HumanLayer · Five Claude Code skills · MIT

Five small add-ons from the HumanLayer team, including one that fixes why Claude Code keeps ignoring your rules.

ELI5

You 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 →
Stars
3.7k
Language
TypeScript
Skills
5
GitHub repo card for humanlayer/skills — click to open the repo
Highlights
  • improve-claude-md fixes rule adherence

    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.

  • show-me explains with pictures

    Explains the current topic with concise diagrams, code-shape sketches and focused HTML artifacts. Added 2026-08-09, the newest of the five.

  • design-control-loop: an agent on a schedule

    Interviews you about your codebase, then builds a sensor-controller-actuator loop as local components plus a scheduled coding-agent workflow.

Star growth over time for humanlayer/skills
The five skills
  1. improve-claude-md

    Rewrites your CLAUDE.md using <important if> blocks to improve instruction adherence.

  2. narrow-react-prop-types

    Narrows React component prop types to match live code paths instead of Storybook, test or mock-only states.

  3. build-iterated-agentic-loop

    Builds a repo-local skill plus an iterated coding-agent GitHub Actions workflow, prompt, memory file and reference templates.

  4. design-control-loop

    Interviews you to design a sensor-controller-actuator loop for your codebase, then builds it plus a scheduled coding-agent workflow.

  5. show-me

    Explains the current topic with concise diagrams, code-shape sketches and focused HTML artifacts.

@mattpocockuk — 7,601 likes, 873,518 views, 11.8k bookmarks. Third party, not HumanLayer. “/show-me is a phenomenal skill… a toolbox of nice ways to look at code.” The link card is the show-me SKILL.md in humanlayer/skills — a skill the README doesn’t even list yet.
On GitHub
Following the README installed a different skill with the same name

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 →
OpenAI · Codex plugin catalog · Mixed licenses

Think of it as the app store for Codex, OpenAI’s AI coding assistant: 62 ready-made plugins so it can work inside Figma, Notion, Stripe and Slack.

ELI5

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 →
Stars
6.4k
Language
JavaScript
Curated plugins
65
GitHub repo card for openai/plugins — click to open the repo
Highlights
  • 62 plugins; 38 written by outside companies

    Figma, Notion, Stripe, Shopify, Adobe, Canva, NVIDIA and Slack each authored their own; OpenAI wrote the other 24, including Gmail, GitHub, Outlook and Teams.

  • One manifest per plugin, plus add-ons

    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.

  • Two curated marketplaces, 10 categories

    The default ‘Codex official’ list has 65 entries (27 developer tools, 12 productivity, 9 creativity); API-key users get a separate 49-entry list.

Star growth over time for openai/plugins
What the plugins actually do
  1. Figma design to code

    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.

  2. Notion specs to plans

    Turn specs into implementation plans, synthesize research into structured docs, prep meeting materials, and capture decisions into Notion pages.

  3. Stripe payments

    Create products, prices and payment links; retrieve and manage payments, subscriptions, invoices, refunds, disputes and customers.

  4. Superpowers workflows

    Jesse Vincent’s skills framework, bundled in OpenAI’s own marketplace: brainstorming, planning, test-driven development, debugging and code review.

Owain Lewis’s full Codex workflow — 52,089 views. The Plugins chapter at 15:05 installs the Linear plugin from this marketplace and uses it for ticket management. Honest flag: it’s about Codex plugins as a feature and never names the repo.
Chapters — click to jump
On GitHub
Issues are turned off, so the frustration lives in a pull request

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 →
Agent skill · System diagrams · MIT

Tell your AI coding assistant to draw how your app works and get a clean, clickable diagram back as a single file — no drawing tool required.

ELI5

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 →
Stars
58.2k
Language
JavaScript
Forks
3.8k
GitHub repo card for tt-a1i/archify — click to open the repo
Highlights
  • One install, four coding assistants

    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.

  • Checks its own drawing before you see it

    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.

  • Exports: PNG, SVG, WebM and share cards

    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.

Star growth over time for tt-a1i/archify
Five diagram types and what each is for
  1. Architecture

    Components, services, storage and boundaries. Give it the scope, core components and primary path.

  2. Workflow

    CI/CD pipelines, approvals, tool calls and runbooks. Give it participants, order, branches and exceptions.

  3. Sequence

    API calls, cache fallback, auth and async traces over time. Give it callers, callees, returns and timing.

  4. Data flow

    Pipelines, lineage, PII boundaries and consumers. Give it sources, transforms, stores and boundaries.

  5. Lifecycle

    States, retries, waits and terminal outcomes. Give it states, events, retry and cancellation paths.

@Voxyz_ai — 618 likes, 60,668 views. Third party. “If I could give Codex / Grok Bot just one skill for drawing system diagrams, I’d pick Archify” — with the install line and the prompt template he uses.
Better Stack, published a week ago — 131,276 views already. Installs the skill, points it at a real codebase, maps one code path, then covers validation, repository evidence, architecture diffs and an Archify-vs-Mermaid comparison with a pros-and-cons chapter.
Chapters — click to jump
On GitHub
The validator that said yes to a wrong arrow

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 →
MCP server · Context saver · Elastic License 2.0

Your AI assistant’s memory fills up with junk. This keeps it out — 98% less clutter — and remembers where you left off.

ELI5

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 →
Stars
22.1k
Language
TypeScript
Context saved
98%
GitHub repo card for mksglu/context-mode — click to open the repo
Highlights
  • 98% less clutter in the assistant’s memory

    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.

  • Picks up where you left off after a reset

    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.

  • 17 coding tools, nothing leaves your machine

    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.

Star growth over time for mksglu/context-mode
Prompts from the README’s Try It section
  1. Deep repo research

    Research a GitHub repo’s architecture, stack, contributors, issues and activity: 5 calls, 62 KB of context instead of 986 KB (94% saved).

  2. Git history analysis

    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.

  3. Web scraping

    Fetch the Hacker News front page, extract titles, scores and domains, group by domain: one call, 3.2 KB of context.

  4. Documentation search

    Fetch the React useEffect docs, index them, and find the cleanup pattern with code examples: 2 calls, 1.8 KB of context.

  5. Session continuity

    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.

Better Stack — 151,942 views, 4,249 likes. The definitive explainer, two weeks after the Hacker News #1: the token-waste math, how the sandbox works, session checkpoints, install, and a live log-analysis demo with a cost review.
Chapters — click to jump
Github Awesome’s 25-second pitch — 63,322 views. “315 kilobytes shrunk to 5.4 — stretching sessions from 30 minutes to nearly 3 hours.”
On Hacker News
Hacker News #1, 570 points: “the claimed 98% savings are noise without benchmarks”

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 →
Matt Pocock · Skills collection · MIT

Before your AI writes a line of code, this makes it interview you first — a veteran engineer’s 25 daily workflows, free.

ELI5

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 →
Stars
259.5k
Language
Shell
Skills
25
GitHub repo card for mattpocock/skills — click to open the repo
Highlights
  • Grill me before you build

    /grill-me interviews you relentlessly about a plan until every branch is resolved. Matt calls the grilling skills his most popular.

  • A shared language for your project

    /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.

  • 25 skills, two ways in

    Install as a managed Claude Code plugin from the official marketplace, or copy editable files into your project with npx skills add mattpocock/skills.

Star growth over time for mattpocock/skills
Four ways AI coding goes wrong — and the skill for each
  1. The agent didn’t do what I want

    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.

  2. The agent is way too verbose

    /grill-with-docs builds a CONTEXT.md glossary of your project’s terms so the agent stops using 20 words where 1 will do.

  3. The code doesn’t work

    /tdd makes the agent write a failing test first, then fix it. /diagnosing-bugs wraps debugging into a disciplined, phase-gated loop.

  4. We built a ball of mud

    /improve-codebase-architecture surveys the codebase for simplification candidates and hands you the list. Matt suggests running it every few days.

@theo — 6,124 likes, 649,739 views. Third party. “Gotta say that Matt’s grill-me skill is exceptional and helps a ton with getting agents aligned with my brain” — the flagship skill of this repo, before his 38-minute review.
Theo (t3.gg) — 405,076 views, 7,205 likes, the biggest third-party video. “Matt Pocock’s AI skills list has racked up over 200,000 GitHub stars and the biggest wins came from somewhere unexpected.” Sponsored by Depot.
On Hacker News
Hacker News: “productivity skill sets for Claude are a bit like opinions”

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 →
Siqi Chen · Writing skill · MIT

Paste in something ChatGPT wrote and it comes back sounding like a person wrote it — same facts, none of the tells.

ELI5

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 →
Stars
46.8k
Language
Python
AI tells caught
25
GitHub repo card for blader/humanizer — click to open the repo
Highlights
  • 25 AI tells, ranked by strength

    “Not X but Y,” one-line closers, forced triads, dashes everywhere, “delve”-type words. The first five justify an edit on a single sighting.

  • Never invents a fact

    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.

  • Matches your voice

    Paste 2–3 paragraphs of your own writing and it follows your rhythm, word choice and punctuation, dashes included.

Star growth over time for blader/humanizer
What Humanizer catches
  1. Staging instead of stating

    “It’s not just X, it’s Y,” a dramatic one-line closer after every section, “Let’s dive in.” State the point directly.

  2. Rhythm by rule

    Forced triads, the same sentence opening three times, dashes as the universal connector. Use the number of items the meaning needs.

  3. Inflation and borrowed authority

    “Delve,” “testament,” “marking a pivotal moment,” “experts believe.” Keep the fact, drop the significance.

  4. Formatting by rule

    Bold as decoration, title-case headings with emojis, curly quotes. Sentence case; turn a labeled list into prose.

  5. Leftovers from the chat

    “Great question! I hope this helps!”, knowledge-limit disclaimers, a heading repeated in the first sentence. Remove the wrapper.

@MushtaqBilalPhD — step one of an academic-writing thread whose root post has 2,297 likes and 339,000 views. This is the tweet that names the repo: “you’ll need a Claude Skill called humanizer… it detects 29 patterns of AI writing and removes them.”
Superbash wires Humanizer into their own auto-summary pipeline, shows a live before/after, then makes the point the others miss: removing AI patterns “gives you neutral text, not your text” — you also need a style file. 8,135 views.
Chapters — click to jump
On GitHub
The owner: “avoiding detection from AI detection tools is not a goal of this skill”

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 →
OpenAI · Codex skills catalog · Per-skill licenses

OpenAI’s official catalog of ready-made jobs for its Codex coding agent — deploy a site, fix a broken build, pull a Figma design — with a “we’ve moved” sign on the door.

ELI5

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 →
Stars
26.9k
Language
Python
Status
Deprecated
GitHub repo card for openai/skills — click to open the repo
Highlights
  • Deprecated, but still the reference

    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.

  • Five skills ship inside Codex

    skill-installer, skill-creator, plugin-creator, imagegen and openai-docs live in .system and are installed automatically in the latest Codex.

  • Real jobs, not toy demos

    Curated folders cover Vercel, Netlify, Render and Cloudflare deploys, fixing failing GitHub CI, Figma, Notion, Linear, Sentry, Playwright and PDFs.

Star growth over time for openai/skills
What’s in the curated catalog
  1. Ship it

    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.

  2. Fix the build

    gh-fix-ci reads failing GitHub Actions logs, summarizes the failure and drafts a fix plan; gh-address-comments works through PR review comments.

  3. Design to code

    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.

  4. Work with your tools

    notion-*, linear and sentry skills plus pdf, playwright (drive a real browser), screenshot, transcribe and speech.

  5. Move from Claude Code

    migrate-to-codex carries instruction files, skills, agents and MCP config from .claude/ into Codex’s own files.

@steipete (Peter Steinberger, OpenClaw) — 2,151 likes, 185,532 views. “TIL: there’s a whole bunch of interesting skills in the oss codex repo” — links the .curated folder and installs playwright-interactive, the same skill issue #386 later reports as broken.
JeredBlu’s day-of-launch walkthrough — 27,700 views. skill-installer, installing from GitHub, and moving existing Claude skills over unchanged: “Open standards win.”
Chapters — click to jump
On GitHub
“Closing this since this repository has been obsoleted”

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 →

First Stars

FROM THE AUDIENCE

Fifteen billing tabs become one dashboard that warns you before a job dies.

WHAT IT IS

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 →
Stars
4
Language
Python
Started
Nov 2024
Sent in by Samuel

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.

Your turn

Building something? Send it to hi@TheNextNewThing.ai

FROM THE AUDIENCE

Agent skills written for people who never read the code.

WHAT IT IS

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 →
Stars
3
Language
Python
Started
Jun 2026
Sent in by Tim

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.

Your turn

Building something? Send it to hi@TheNextNewThing.ai

FROM THE AUDIENCE

Keep Claude Code's terminal, swap in a GPT model underneath.

WHAT IT IS

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 →
Stars
1
Language
Go
Started
Aug 2026
Sent in by Hunter

A proxy built in Go so you can use GPT models inside Claude.

Your turn

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FROM THE AUDIENCE

A focus group for your marketing copy before a single customer sees it.

WHAT IT IS

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.

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Stars
1
Language
TypeScript
Started
Aug 2026
Sent in by Rico

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.

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Building something? Send it to hi@TheNextNewThing.ai

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