{ "schema_version": "1.1", "id": "atlas-skills", "slug": "skills", "title": "Skills", "url": "https://feed7.dev/atlas/skills", "current_answer": null, "implementation_consequence": null, "agent_context": null, "confidence": "auto_collected", "last_verified": null, "last_updated": null, "evidence": [ { "schema_version": "1.1", "id": "auto-629c51aa89", "slug": "citrolabs-ego-lite-629c51aa89", "url": "https://feed7.dev/p/citrolabs-ego-lite-629c51aa89", "title": "citrolabs/ego-lite", "why_included": "Isolated browser spaces can let parallel agents reuse authenticated sessions without taking over active tabs on macOS.", "summary": "ego lite lets Codex, Claude Code, and other agents automate logged-in web sessions in isolated browser spaces without taking over your active tabs. It is macOS-only today.", "practical_implication": "For authenticated browser work, consider whether shared local state and parallel spaces can replace separate automation browsers. The project reports workflows finishing up to 2.5× faster than Vercel’s agent-browser on four complex tasks.", "agent_context": "ego lite is a macOS browser that gives each agent an isolated **Space** while retaining access to migrated Chrome logins, cookies, extensions, and bookmarks. Its skill exposes page actions as JavaScript functions.\n\nFor authenticated browser work, consider whether shared local state and parallel spaces can replace separate automation browsers. The project reports workflows finishing **up to 2.5× faster** than Vercel’s agent-browser on four complex tasks.\n\nThe comparison is project-reported and covers only **four tasks**. Windows and Linux remain on the roadmap, and the browser itself is a separate free download rather than part of the MIT-licensed repository.", "source": { "name": "GitHub", "url": "https://github.com/citrolabs/ego-lite", "published_at": "2026-07-24T00:00:00.000Z" }, "source_class": "tool", "content_type": "GitHub Repo", "layer": "tools", "domains": [ "coding" ], "topics": [ "computer-use", "coding-agents", "skills" ], "verification": { "status": "needs_review", "label": "Needs Review", "method": "unverified", "verified_at": null }, "uncertainty": [ "Automatically selected from source material; feed7 has not independently tested the claim." ], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-24T00:00:00.000Z", "modified_at": "2026-07-24T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/citrolabs-ego-lite-629c51aa89", "json": "https://feed7.dev/p/citrolabs-ego-lite-629c51aa89.json", "markdown": "https://feed7.dev/p/citrolabs-ego-lite-629c51aa89.md" } }, { "schema_version": "1.1", "id": "auto-5077dc1992", "slug": "how-we-set-up-our-cloud-agent-environment-5077dc1992", "url": "https://feed7.dev/p/how-we-set-up-our-cloud-agent-environment-5077dc1992", "title": "How we set up our cloud agent environment", "why_included": "Treat the dev environment as agent infrastructure with Linux parity, one discoverable CLI, end-to-end tests, and automated repair.", "summary": "Cursor’s cloud-agent adoption grew after it treated the dev environment as agent infrastructure: Linux parity, one discoverable CLI, end-to-end testing, and automated repair.", "practical_implication": "Treat the development environment as part of the agent harness. Match cloud and local dependencies, replace fragile command sequences with one discoverable interface, expose verification workflows, and let supervisors own long-running processes.", "agent_context": "Cursor moved its monorepo onto agent-ready Linux VMs, centralized startup and utilities in an internal CLI, and added end-to-end computer-use testing. Cloud agents went from **roughly 1 in 10 merged PRs in December** to **more than half today**.\n\nTreat the development environment as part of the agent harness. Match cloud and local dependencies, replace fragile command sequences with one discoverable interface, expose verification workflows, and let supervisors own long-running processes.\n\nSkills alone only helped at the margins because the underlying commands remained convoluted. Cursor also built **Cursor Cloud MCP** and **Cloud Doctor** to diagnose and repair environment failures, but the adoption figures are internal and do not isolate which change caused the increase.", "source": { "name": "Cursor", "url": "https://cursor.com/blog/cloud-agent-environment", "published_at": "2026-07-30T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Engineering Post", "layer": "agent", "domains": [ "coding" ], "topics": [ "cloud-agents", "harness-engineering", "skills" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-30T00:00:00.000Z", "modified_at": "2026-07-30T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/how-we-set-up-our-cloud-agent-environment-5077dc1992", "json": "https://feed7.dev/p/how-we-set-up-our-cloud-agent-environment-5077dc1992.json", "markdown": "https://feed7.dev/p/how-we-set-up-our-cloud-agent-environment-5077dc1992.md" } }, { "schema_version": "1.1", "id": "auto-2e74d0228d", "slug": "addyosmani-agent-skills-2e74d0228d", "url": "https://feed7.dev/p/addyosmani-agent-skills-2e74d0228d", "title": "addyosmani/agent-skills", "why_included": "Turn specs, TDD, review, and shipping into evidence-gated workflows, but adopt only the skills that fit your repository.", "summary": "This pack turns common engineering practices into portable coding-agent workflows for specs, TDD, review and shipping. Its useful idea is to require evidence at each gate, not merely better prompts.", "practical_implication": "Treat the pack as a menu of enforceable workflows: begin with TDD, debugging or code review, then add broader lifecycle automation after checking how each skill fits your repository. Codex can load it as a native plugin from CLI v0.122+.", "agent_context": "The repository contains **24 Markdown skills**, four specialist personas and **8 lifecycle commands** spanning definition, planning, implementation, verification, review and release. The open skills CLI supports installation across **70+ agents**.\n\nTreat the pack as a menu of enforceable workflows: begin with TDD, debugging or code review, then add broader lifecycle automation after checking how each skill fits your repository. Codex can load it as a native plugin from **CLI v0.122+**.\n\nInstalling an individual skill may omit shared files under the repository-level references directory, leaving supplementary checklists unavailable. The workflows also encode strong process opinions, so teams should review their gates and defaults instead of adopting all 24 blindly.", "source": { "name": "GitHub", "url": "https://github.com/addyosmani/agent-skills", "published_at": "2026-08-09T00:00:00.000Z" }, "source_class": "tool", "content_type": "GitHub Repo", "layer": "agent", "domains": [ "coding" ], "topics": [ "skills", "coding-agents", "harness-engineering" ], "verification": { "status": "needs_review", "label": "Needs Review", "method": "unverified", "verified_at": null }, "uncertainty": [ "Automatically selected from source material; feed7 has not independently tested the claim." ], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-09T00:00:00.000Z", "modified_at": "2026-08-09T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/addyosmani-agent-skills-2e74d0228d", "json": "https://feed7.dev/p/addyosmani-agent-skills-2e74d0228d.json", "markdown": "https://feed7.dev/p/addyosmani-agent-skills-2e74d0228d.md" } }, { "schema_version": "1.1", "id": "auto-b274d6785e", "slug": "new-ways-to-learn-and-teach-with-chatgpt-work-and-codex-b274d6785e", "url": "https://feed7.dev/p/new-ways-to-learn-and-teach-with-chatgpt-work-and-codex-b274d6785e", "title": "New ways to learn and teach with ChatGPT Work and Codex", "why_included": "The announcement names education plugins but provides no plugin list, capabilities, pricing, availability, or integration details.", "summary": "OpenAI is adding education plugins to ChatGPT Work and Codex for teaching, learning, research, and building. The announcement provides no details on capabilities or access.", "practical_implication": "Builders in education may want to examine whether these plugins can supply agents with domain-specific workflows for research, teaching, or project work.", "agent_context": "OpenAI announced **education plugins** for **ChatGPT Work and Codex**, aimed at K–12 teachers, college educators, and students.\n\nBuilders in education may want to examine whether these plugins can supply agents with domain-specific workflows for research, teaching, or project work.\n\nThe supplied announcement names audiences and use cases but gives no plugin list, technical behavior, pricing, availability, or integration details.", "source": { "name": "OpenAI", "url": "https://openai.com/index/learn-teach-chatgpt-work-codex", "published_at": "2026-08-04T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Official Release", "layer": "tools", "domains": [ "research", "coding" ], "topics": [ "skills", "coding-agents" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-04T00:00:00.000Z", "modified_at": "2026-08-04T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/new-ways-to-learn-and-teach-with-chatgpt-work-and-codex-b274d6785e", "json": "https://feed7.dev/p/new-ways-to-learn-and-teach-with-chatgpt-work-and-codex-b274d6785e.json", "markdown": "https://feed7.dev/p/new-ways-to-learn-and-teach-with-chatgpt-work-and-codex-b274d6785e.md" } }, { "schema_version": "1.1", "id": "auto-990ea76696", "slug": "break-it-down-pass-it-on-cross-task-skill-transfer-in-ll-990ea76696", "url": "https://feed7.dev/p/break-it-down-pass-it-on-cross-task-skill-transfer-in-ll-990ea76696", "title": "Break It Down, Pass It On: Cross-Task Skill Transfer in LLM Agents", "why_included": "Store small textual procedures, because subtask-level skills transferred better while whole-task skills often hurt performance.", "summary": "Reusable agent skills transferred better when induced from subtasks and stored as text. Whole-task skills often hurt versus no memory, so retrieval alone is not evidence that a memory helps.", "practical_implication": "Build agent memory from small, reusable procedures and prefer textual guidance unless execution requires code. Before retrieval, score candidates for both task specificity and breadth of relevance rather than assuming any prior solution will help.", "agent_context": "The study compares task-level against subtask-level skill induction and text against code formats. **Task-level skills often fell below the no-memory baseline**, while **subtask-level skills improved performance on average**; text transferred better than code.\n\nBuild agent memory from small, reusable procedures and prefer textual guidance unless execution requires code. Before retrieval, score candidates for both task specificity and breadth of relevance rather than assuming any prior solution will help.\n\nNeither specificity nor abstractness predicted outcomes alone. Their combined **skill utility score** correlated with transfer performance and requires only skills plus task descriptions, but the supplied material gives no tasks, models, or effect sizes for judging generality.", "source": { "name": "arXiv", "url": "https://arxiv.org/abs/2608.20274v1", "published_at": "2026-08-20T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Paper", "layer": "agent", "domains": [], "topics": [ "skills", "agent-memory", "agent-reliability" ], "verification": { "status": "needs_review", "label": "Needs Review", "method": "unverified", "verified_at": null }, "uncertainty": [ "Automatically selected from source material; feed7 has not independently tested the claim." ], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-20T00:00:00.000Z", "modified_at": "2026-08-20T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/break-it-down-pass-it-on-cross-task-skill-transfer-in-ll-990ea76696", "json": "https://feed7.dev/p/break-it-down-pass-it-on-cross-task-skill-transfer-in-ll-990ea76696.json", "markdown": "https://feed7.dev/p/break-it-down-pass-it-on-cross-task-skill-transfer-in-ll-990ea76696.md" } } ], "conflicting_sources": [], "superseded_claims": [], "corpus_evidence": [ { "title": "SafeEvolve: Harness-Policy Co-Evolution from Agent Experience for Safety Alignment", "url": "https://arxiv.org/abs/2609.02786v1", "source_name": "arXiv", "published_at": "2026-09-02T16:19:54+00:00", "summary": "SafeEvolve turns completed agent trajectories into reversible harness updates and policy training, linking runtime safeguards with learned multi-step behavior." }, { "title": "How our agents build on-brand pages with design.md", "url": "https://vercel.com/blog/how-our-agents-build-on-brand-pages-with-design-md", "source_name": "Vercel", "published_at": "2026-08-31T04:00:00+00:00", "summary": "Vercel found that prose alone produced inconsistent agent-made pages, then paired design.md with fixed CSS primitives and repeatable evals to encode brand judgment." }, { "title": "Agents Are Where Microservices Were in 2015 — Roberto Milev & Uday Kanagala, Navan", "url": "https://www.youtube.com/watch?v=32nrHU6zHU8", "source_name": "AI Engineer", "published_at": "2026-08-29T16:30:28+00:00", "summary": "Navan treats production agents as stateful services needing persistence, composable skills, traces, authorization gates, and trajectory-based tests before multi-agent complexity." }, { "title": "AI-Native Organisations Run on Skills: How to Structure and Scale Them — Imad Touil, QuantumBlack", "url": "https://www.youtube.com/watch?v=M05vON8i0aI", "source_name": "AI Engineer", "published_at": "2026-08-28T18:30:04+00:00", "summary": "Shared agent skills can turn team know-how into portable workflows, but unmanaged catalogs create duplication and policy risk. Treat skills as versioned, evaluated dependencies." }, { "title": "On the Maintenance and Co-evolution of Agent Plugins: An Empirical Study of Claude Code Plugin Marketplaces", "url": "https://arxiv.org/abs/2608.28497v1", "source_name": "arXiv", "published_at": "2026-08-28T16:27:35+00:00", "summary": "A study of 8,351 Claude Code plugins finds instruction files and scripts often require coordinated maintenance, treating skills as evolving software rather than static prompts." }, { "title": "RedEvoAgent: Automatic Red-Teaming Agent with Experience-Driven Skill Evolution", "url": "https://arxiv.org/abs/2608.27439v1", "source_name": "arXiv", "published_at": "2026-08-27T17:55:33+00:00", "summary": "RedEvoAgent turns prior jailbreak trajectories into a compact attack skill, then keeps only validated improvements. It offers a more interpretable way to probe tool-using agents for unsafe actions." }, { "title": "How AI Agents Let GTM Teams Scale — Justin Joyce, Cloudflare", "url": "https://www.youtube.com/watch?v=Qw_tC68KKes", "source_name": "AI Engineer", "published_at": "2026-08-26T16:30:09+00:00", "summary": "Cloudflare’s GTM workflow shows a reusable agent pattern: preprocess business data, encode domain knowledge in skills, then separate drafting, verification, and tone." }, { "title": "The Building Blocks of GTM Orchestration — Arman Vaziri, Ramp", "url": "https://www.youtube.com/watch?v=VjEP0xqTUI0", "source_name": "AI Engineer", "published_at": "2026-08-26T14:30:38+00:00", "summary": "Ramp’s GTM agents share a customer-data layer, durable execution, tools, and skills. The reusable pattern is to automate one narrow workflow, then extend the same substrate across teams." }, { "title": "Coding Agents Don't Scale Themselves. Neither Do Your Teams. — Patrick Debois, Tessl", "url": "https://www.youtube.com/watch?v=zCJtYuqwm7E", "source_name": "AI Engineer", "published_at": "2026-08-22T16:00:06+00:00", "summary": "Agent adoption becomes a team-systems problem: improve shared context and harnesses, assign platform ownership, and measure fewer human interventions instead of individual prompt speed." }, { "title": "The Missing Layer: Design Taste in AI Agents — Hassan El Mghari, Together AI", "url": "https://www.youtube.com/watch?v=7GMKdpLsxwU", "source_name": "AI Engineer", "published_at": "2026-08-21T15:00:06+00:00", "summary": "Treat an agent’s first UI as a draft: encode recurring design dislikes, supply visual references, split work into focused prompts, and reserve time for iteration." }, { "title": "Your Fine-Tuned Model Is Tech Debt: A 50x ROI House of Cards — Dan Bjornn, Lease End", "url": "https://www.youtube.com/watch?v=4loPnxvWWhg", "source_name": "AI Engineer", "published_at": "2026-08-20T16:00:22+00:00", "summary": "Lease End replaced a fine-tuned intent classifier with skills and runtime context, cutting production fixes from about a week to under an hour. Higher API spend was offset by lower maintenance cost." }, { "title": "How I automate my own job at Hugging Face using agents — Niels Rogge, Hugging Face", "url": "https://www.youtube.com/watch?v=FLUoowDJg4I", "source_name": "AI Engineer", "published_at": "2026-08-20T15:30:35+00:00", "summary": "Hugging Face automated research-artifact outreach with a CLI, one skill, and a sandbox. The case shows when an agent can replace custom workflow code, but undisclosed automated outreach raises trust questions." }, { "title": "The Era of Compound Engineering — Kieran Klaassen, Every/Cora", "url": "https://www.youtube.com/watch?v=_ehJyfHg1Vk", "source_name": "AI Engineer", "published_at": "2026-08-20T15:00:06+00:00", "summary": "Compound engineering treats each agent mistake and product judgment as reusable context. The useful discipline is to invest in memory and review so the next feature becomes easier, not merely faster." }, { "title": "Prototyping as Leadership: How a CTO Ships with AI Agents — Hursh Agrawal, The Browser Company", "url": "https://www.youtube.com/watch?v=bdHaOXZOhcM", "source_name": "AI Engineer", "published_at": "2026-08-20T14:00:21+00:00", "summary": "A manager can turn fragmented time into agent steering: prepare context, run bounded work overnight, then personally test and review small PRs before involving the team." }, { "title": "Don’t be data poor — Anuj Iravane, Anterior", "url": "https://www.youtube.com/watch?v=XAsb7MIAzm8", "source_name": "AI Engineer", "published_at": "2026-08-19T18:00:17+00:00", "summary": "When production data cannot be retained, generate eval cases backward from sampled labels and reasoning paths, build records in layers, and let domain experts steer the pipeline." }, { "title": "Building an Agentic Video Editor for Mass Consumer — Ekaterina Deyneka, Reelful", "url": "https://www.youtube.com/watch?v=pPj_tjlvYjA", "source_name": "AI Engineer", "published_at": "2026-08-18T14:30:38+00:00", "summary": "Reelful maps the coding-agent pattern onto real-footage editing: analyze media, approve a plan, edit as Remotion code in a sandbox, then verify before rendering." }, { "title": "Bringing agents onto the world wide web — Paul Klein IV, Browserbase", "url": "https://www.youtube.com/watch?v=GqoNrUz8hEU", "source_name": "AI Engineer", "published_at": "2026-08-14T15:00:31+00:00", "summary": "Production browser agents need more than a capable model: a domain harness, compact site knowledge, consistent rendering, secure authentication, and human approval for sensitive actions." }, { "title": "LLM Knowledge Bases: a practical guide — Ben Holmes, Warp", "url": "https://www.youtube.com/watch?v=I3bpdgFJCUY", "source_name": "AI Engineer", "published_at": "2026-08-12T17:00:22+00:00", "summary": "A useful agent knowledge base starts with abundant raw Markdown, then adds controlled tags, backlinks, and generated indexes. Scheduled agents can maintain it, but sync and taxonomy need explicit rules." }, { "title": "Agents, codebases, and teams — Aditya Khandelwal, Amazon AGI Lab", "url": "https://www.youtube.com/watch?v=aeTb5BdmTTc", "source_name": "AI Engineer", "published_at": "2026-08-11T00:03:17+00:00", "summary": "Coding-agent adoption is a team systems problem, not an individual tooling choice. Share effective practices, disclose context progressively, and build automated loops that detect and repair weak output." }, { "title": "The New Primitives: Building AI Native Software — Kwindla Kramer, Daily", "url": "https://www.youtube.com/watch?v=LZuWZRze3MU", "source_name": "AI Engineer", "published_at": "2026-08-07T23:53:33+00:00", "summary": "The talk argues that agents are an early interface, not the endpoint, and demonstrates reusable primitives for AI-native software: persistent subagents, compressed context, loaded skills, generated UI, and voice." }, { "title": "SkillProx: Self-Evolving Agent Skills via Proximal Textual Gradient Descent", "url": "https://arxiv.org/abs/2608.07449v1", "source_name": "arXiv", "published_at": "2026-08-07T17:40:33+00:00", "summary": "SkillProx evolves reusable agent instructions through measured retries, regression rollback, and utility-based pruning, reporting a 3-point accuracy gain over its strongest baseline." }, { "title": "Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents", "url": "https://arxiv.org/abs/2608.06312v1", "source_name": "arXiv", "published_at": "2026-08-06T17:27:23+00:00", "summary": "A structured multi-agent reviewer closed part of the gap on rule-heavy documents, suggesting explicit taxonomies, specialized skills, and verification beat a single generic review pass." }, { "title": "Introducing Agent Plugins 1.0.0", "url": "https://vercel.com/changelog/introducing-agent-plugins-1-0-0", "source_name": "Vercel", "published_at": "2026-08-06T00:00:00+00:00", "summary": "Agent Plugins 1.0.0 packages Skills and MCP servers in a shared format supported by five major agent-client families, reducing client-specific repackaging." }, { "title": "Introducing Agent Plugins", "url": "https://vercel.com/blog/introducing-agent-plugins", "source_name": "Vercel", "published_at": "2026-08-06T00:00:00+00:00", "summary": "Agent Plugins 1.0.0 standardizes how Skills and MCP servers are packaged, letting extension authors target several agent clients with one portable directory." }, { "title": "Your Finance Agent's Bottleneck Is You — Ramana Siddanth Emani, Auditoria AI", "url": "https://www.youtube.com/watch?v=z0sh8HyTrDo", "source_name": "AI Engineer", "published_at": "2026-07-30T03:00:06+00:00", "summary": "Production agent velocity depends less on model swaps than on automating the developer loop: isolate parallel work, encode workflows as skills, connect tools, and keep humans as verifiers." }, { "title": "We Vetted 2000 AI Skills Before They Reached Developers — Lucas Palma, Nubank", "url": "https://www.youtube.com/watch?v=iKQ78wyJEXU", "source_name": "AI Engineer", "published_at": "2026-07-29T22:00:06+00:00", "summary": "Treat agent skills as supply-chain dependencies. Nubank scans them locally and in CI with deterministic rules plus LLM review, then gates marketplace distribution and feeds findings into vulnerability management." }, { "title": "Skills are new features: Building Skill-Centric Harness — Yogendra Miraje, FactSet", "url": "https://www.youtube.com/watch?v=7jjudsEhBtM", "source_name": "AI Engineer", "published_at": "2026-07-29T18:00:06+00:00", "summary": "FactSet treats skills as versioned product features and the harness as their runtime. Routing descriptions, model-specific evals, access controls, and governance matter as libraries grow." }, { "title": "Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills", "url": "https://arxiv.org/abs/2607.22529v1", "source_name": "arXiv", "published_at": "2026-07-24T17:59:22+00:00", "summary": "Skill-SP turns agent skills into units for verifiable self-play: generate tasks, solve them, then update the skill library from execution feedback. The abstract provides no per-benchmark effect sizes." }, { "title": "The Regression Tax: Decomposing Why Skills Help and Hurt LLM Agents", "url": "https://arxiv.org/abs/2607.22520v1", "source_name": "arXiv", "published_at": "2026-07-24T17:50:03+00:00", "summary": "Procedural skills can make an agent fail tasks it previously solved. Evaluate gains and regressions separately, and design skills to preserve input grounding and output verification." }, { "title": "Full Workshop: Setting Yourself Up for Success —Jason Liu, OpenAI Codex", "url": "https://www.youtube.com/watch?v=il1c1a2FufU", "source_name": "AI Engineer", "published_at": "2026-07-24T15:00:06+00:00", "summary": "Persistent Codex workflows become more useful with reusable skills, memory, app-aware context, and scheduled thread check-ins—but computer use needs explicit boundaries and stopping rules." }, { "title": "WTF Is the Context Layer? The Missing Infrastructure for Production Agents — Prukalpa Sankar", "url": "https://www.youtube.com/watch?v=8G_1-3IO4ZQ", "source_name": "AI Engineer", "published_at": "2026-07-14T22:45:06+00:00", "summary": "Atlan’s agent experiments argue for shared, versioned context instead of per-agent memory: a portable layer for business facts, skills, norms, retrieval, and feedback across changing harnesses." }, { "title": "Don't Ship Skills Without Evals — Philipp Schmid, Google DeepMind", "url": "https://www.youtube.com/watch?v=0vphxNt4wyk", "source_name": "AI Engineer", "published_at": "2026-07-14T22:00:06+00:00", "summary": "Agent skills need regression tests, not manual spot checks. Test triggering and output with and without each skill, across repeated trials and the harnesses your team actually uses." }, { "title": "JuliusBrussee/caveman", "url": "https://github.com/JuliusBrussee/caveman", "source_name": "GitHub", "published_at": null, "summary": "Caveman compresses agent inputs and outputs, with local tooling for noisy commands, browsing, memory, and skills. Its own caveat matters: terse workloads can cost more once the skill prompt is included." }, { "title": "EveryInc/compound-engineering-plugin", "url": "https://github.com/EveryInc/compound-engineering-plugin", "source_name": "GitHub", "published_at": null, "summary": "Compound Engineering packages a six-stage agent workflow into 32 skills for Codex, Claude Code, Cursor, and others. Its core idea is to persist plans, reviews, and solved-problem notes as future context." }, { "title": "browser-use/video-use", "url": "https://github.com/browser-use/video-use", "source_name": "GitHub", "published_at": null, "summary": "video-use lets shell-capable coding agents edit from word-level transcripts and targeted visual composites, then render with FFmpeg. Its approval and self-check loop is a useful pattern for media agents." }, { "title": "Leonxlnx/taste-skill", "url": "https://github.com/Leonxlnx/taste-skill", "source_name": null, "published_at": null, "summary": "A set of portable SKILL.md files that push coding agents past generic frontend output: it infers a design language from the brief and tunes variance, motion, and density dials. 850 stars in a day." }, { "title": "alirezarezvani/claude-skills", "url": "https://github.com/alirezarezvani/claude-skills", "source_name": null, "published_at": null, "summary": "A 354-skill catalog for Claude Code and 12 other coding agents, installable via the plugin marketplace, with a script that converts skills to each tool's format and a built-in security auditor." } ] }