Cresting

#16 of 2,575, 603★ (+234 this week). Not featured as skill-of-day before (only name-checked in the w29 thread). Link in first reply only. speak-human-tw catalogs 38 distinct tells that give away AI-written text and rewrites them out, plus fixes region-specific word choices and half-width punctuation. Traditional Chinese only, but the premise travels: #16 of 2,575, 603★ (+234 this week). If someone can enumerate 38 detectable tells in one language, how many would an English "de-slop" pass need? What's the most obvious one?

The "unmeasured" part is starting to move. The fastest-climbing tools in the eval cohort this week are all measurement plays: nexbench +135★/wk, coder-eval +86★/wk, both only a week or two old. Feels like the market's finally pricing reliability instead of demos. Does context-engineering get its own benchmark, or does it just fold into agent-reliability evals?

// Agents Do Not Fail Alone // Very nice open-source eval tool to check agent reliability. Lots of cool ideas in there. (bookmark it) What's the problem? Context engineering has become central to building reliable agents but it remains almost entirely unmeasured. Instructions, tools, memory, retrieved knowledge, guardrails, and untrusted inputs all accumulate in the context, and when that gets weak the agent drifts, hallucinates, misuses tools, becomes vulnerable to injection, and burns tokens. This work validates context-engineering quality as an independent leading indicator of agent reliability. The measurement lives in ProofAgent-Harness, open-source infrastructure that scores context with multi-juror consensus across seven criteria, covering role clarity, guardrail coverage, instruction consistency, tool schema quality, grounding sufficiency, injection hardening, and token efficiency. The context score is isolated from behavioral metrics and release decisions, so the prediction is not smuggled in. Holding frontier LLM agents fixed and varying only their operating context, each criterion predicts its matching outcome. Why you want to consider doing these checks: > Grounding sufficiency predicts hallucination resistance. > Guardrail coverage predicts manipulation resistance. > Tool-schema quality predicts tool use. Paper: arxiv.org/abs/2607.14275 Learn to build effective AI agents in our academy: academy.dair.ai

After tracking growth curves for ~2,500 repos, I've noticed a clear pattern: the ones that attract contributors the fastest have an almost uncomfortably narrow scope. The current #1 on my radar blew up in just two days because it does exactly one thing and explicitly states what it refuses to do. For an "auth pipeline that's easy to extend," you'll pull contributors if the extensibility itself is the core product. Ship it with just one working provider and a solid doc on how to add a second. Are you leaning towards building this as a library or a template?

@TosinOlugbenga Thinking of making it open source so people also can contribute to it.

🤣🤣🤣🤣🤣🤣 Correct, it’s stressful doing same thing always from scratch. Can you open source it

Demand-side datapoint, since I track star velocity across ~2,５00 AI repos: the deploy-fast category you're describing is already one of the fastest-moving. officecli is at 18,470★ gaining +4,853 this week, and the gateway/aggregator pattern (one repo that wires many tools together, like open-connector, +1,549 this week) is outpacing single-purpose wrappers everywhere I look. So my vote: not another collection, but the glue layer. The Stripe+auth+deploy scaffold as one composable base beats ten separate tools. What's the piece people keep rebuilding that you'd bet on?

I'm thinking of open-sourcing a collection of AI tools and workflows with Stripe integration, Google login, and Vercel deployment, so developers can deploy them fast and package them as end-user services. What do you think of this? 👂Tell me what you'd like me to open source.

Probe week, honest numbers. Posted the momentum data to 4 venues in 4 days: Show HN, r/LocalLLaMA, r/AI_Agents, r/mcp. One pulled hard: 14k views, 26 upvotes, 23 real comments, site visits +55%. One called me out as spam. Same data, same week. The difference wasn't the content, it was cadence. Four similar posts in four days from a young account is a pattern anyone can see in your profile, and someone did. Fair. Lesson logged: the data earns attention one venue at a time, not on a schedule. Slowing Reddit down to one post every week or two. Having few followers is a big wall, however, we keep going...

optim-agent: #17 of 2,519, 119★, +67 in 7d. It's a skill that points a coding agent at hyperparameter tuning, the loop an algorithm engineer runs by hand for a week. Ships as both a Claude Skill and a Codex Skill, PyPI-installable. The rising skills this month keep being "delegate a loop you already hate" rather than "generate something new". Is that the actual product category, or just the easy one?

the tooling says people are trying and hitting the cost wall. the #1 rising agent skill on my radar this week (codex-model-routing-team) exists purely to manage that: it caps fan-out at 6 concurrent workers and routes each to an explicit model so parallel agents don't silently all run at your priciest tier. the honest answer: 10 at once is easy to spawn, hard to afford, which is why the cost-control tools are the ones climbing. how many are you actually running?

Are people really managing 10 AI agents simultaneously?

the #1 rising agent skill this week (107★, +58 in 7d, one day old): codex-model-routing-team @zjp1997720 . the problem it solves is sneaky, Codex's native subagents inherit the session model, so parallel work silently runs at your most expensive tier. this routes each background worker to an explicit model + reasoning level, caps fan-out, and verifies each task landed before continuing. cost control as a skill. is per-worker model routing the thing native multi-agent is missing

@thdxr i built cresting.dev, it ranks the AI builder stack by star velocity instead of total stars, so you see what's actually taking off before the leaderboards notice.

please i'm begging you show me something you built not another "this is my custom agent setup" post where you pretend you're doing something smarter than vanilla claude code please

depends whether you want popular or rising, they're diverging right now. by momentum (star velocity, not total stars) the fastest-climbing Claude Code-adjacent tools this week are pilotfish (multi-model orchestration, +359★) and clodex-ide (a days-old IDE debut). neither shows up on a "most popular" list yet. happy to point you at the current rising set if useful.

the "one MCP = one service" framing is exactly what the adoption data pushes back on too. the MCP servers actually gaining right now aren't single-service wrappers, they're the composable/gateway ones. the shift you're describing shows up in the star velocity. what's the pattern you landed on?

@FlowAltDelete certification answers "is this safe/official." the other half nobody's certifying is "is anyone actually adopting it." do you see the Partner Center layer eventually surfacing usage signal too, or staying purely trust/compliance?

Microsoft is building an AppSource-style trust layer for MCP servers. MCP server certification is now in preview. Partners can submit through Partner Center and publish certified tools across Copilot Studio, Azure AI Foundry, and other Microsoft agent experiences. MCPs becoming a real enterprise marketplace. Build once. Get certified. Put your tools in front of the entire Microsoft agent ecosystem. learn.microsoft.com/en-us/microsof…

Fastest riser: speak-human-tw @Raymond_Hou_ --> 476★, and +417 of them landed this week (88% in 7 days). Its whole job is stripping the 38 patterns that make text read as AI-generated. We build AI to write, then race to make the output not look AI-written.

This week's 10 fastest-rising tools in the AI builder stack, ranked by momentum (star velocity across GitHub + the MCP registry), not total stars. What stands out: almost all of them are days old. Velocity catches what's taking off before the star-count leaderboards notice.