# Claude Skills for AI Engineers: 2026 Mid-Year Edition: 11 Skills That Fill Last Year's Gaps > Claude Skills for AI Engineers. _Maximem Team ยท August 10, 2026_ Every gap we named in February has been filled. When we published [the February list](https://www.maximem.ai/blog/claude-skills-for-engineers) of nine Claude skills for AI engineers, we closed it with an honest assessment: five categories did not exist yet as community skills, specifically [context window](https://www.maximem.ai/glossary/context-window) analyzers, cost tracking, production monitoring integrations, [vector database](https://www.maximem.ai/glossary/vector-database) utilities, and API mocking. Five months later, every one of those categories has a real, installable answer, and the ecosystem that held 96,000+ indexed SKILL.md files in February now indexes 2,167,429 on [SkillsMP](https://skillsmp.com/). A quick note on method before the list. We pulled every star count and push date in this piece from the GitHub API on July 9, 2026. Stars move; treat every number here as a snapshot from that day, not a live counter. Where a claim rests on a secondary source or a listing we could not confirm directly, we say so inline instead of quietly citing it. ## What changed since the February list ### Skills became an open standard, not a Claude feature The biggest shift is that "Claude skills" is now a slightly outdated name. The [Agent Skills spec](https://agentskills.io) arrived on December 18, 2025, and since then the same SKILL.md folder format has spread well beyond Claude Code: [OpenAI Codex](https://developers.openai.com/codex/skills) ships official skills documentation \[GAURAV: spot-check this URL renders before publish\], and [Cursor](https://cursor.com), [Gemini CLI](https://github.com/google-gemini/gemini-cli), [GitHub Copilot](https://github.com/features/copilot), [Windsurf](https://windsurf.com), [Cline](https://cline.bot), and [OpenCode](https://opencode.ai) all read the format. One secondary source counts roughly 32 tools supporting the spec by March 2026 \[GAURAV: secondary claim from paperclipped.de; verify or soften before publish\]. The practical consequence for you: one skill folder now serves your whole toolchain, which is a large part of why we argued in May that [skills are the new microservices](https://www.maximem.ai/blog/skills-new-microservices). ### The ecosystem grew roughly 22x and got noisier SkillsMP indexed 96,000+ SKILL.md files when we wrote V1; it indexes 2,167,429 today. That is roughly a 22x jump in five months, and it changed the nature of the problem. Discovery used to be the hard part; now the hard part is curation, because two million files means most of what you find is abandoned or untested. The official [anthropics/skills](https://github.com/anthropics/skills) repo sits at 159k stars and installs in one line with `/plugin marketplace add anthropics/skills`, which makes it the sane starting point. Everything below it, you have to vet, and that vetting is the actual value of this list. ### The nine from February: who aged well We went back and checked our own picks. promptfoo-evaluation grew from 531 stars in February to 1,257 in July, which is a 2.4x rise, and it stays on this list. Daniel Miessler's promptinjection skill remains the canonical prompt-security entry. agent-debugger, which we rated highly in February, lost its job to first-party tooling; the [observability](https://www.maximem.ai/glossary/observability) vendors now ship official plugins that do trace-driven debugging better than a generic skill can, and you will find two of them below. ### The five predicted gaps, mapped to what filled them

Gap named in the February list

Filled by (July 2026)

Context window analyzers

lean-ctx

Cost tracking

claude-api (official Anthropic skill)

Production monitoring integrations

Langfuse and LangSmith plugins

Vector database utilities

vector-db-setup

API mocking

aimock

Alright. Let us make this concrete. ## The 12 skills ### 1\. lean-ctx: context intelligence and token reduction Context bloat is the number one cost and quality problem in long agent sessions, and this is the analyzer we said did not exist in February. lean-ctx is a context runtime plus a Claude Code skill: session caching, AST-aware compression, and 90+ shell patterns for cutting token usage, under the tagline "control what your AI can see." It supports Claude Code, Cursor, and Copilot, and installs with `lean-ctx init --agent claude-code`. - **Link:** [github.com/yvgude/lean-ctx](https://github.com/yvgude/lean-ctx) (docs at leanctx.com) - **Creator:** yvgude - **Stats:** 3,183 stars, 295 forks; created March 23, 2026, pushed July 9, 2026 - **Try:** `Use lean-ctx to audit what this session is loading into context and compress the repo map before we continue.` ### 2\. claude-api: the official pricing and token-counting skill Cost estimation built on hallucinated pricing tables is a silent killer; a model asked about its own API pricing will confidently quote numbers from its training data. claude-api is a new official Anthropic skill in anthropics/skills that gives Claude ground truth on model IDs, pricing, parameters, streaming, tool use, MCP, caching, token counting, and model migration. One honest nuance: this covers estimation, not live spend monitoring. Usage trackers in the ccusage style remain external CLI tools rather than skills, so the cost-tracking gap is filled partially, by the vendor who owns the price list. - **Link:** [claude-api SKILL.md](https://github.com/anthropics/skills/blob/main/skills/claude-api/SKILL.md) - **Creator:** Anthropic - **Stats:** parent repo at 159k stars, 17.6k forks (July 9, 2026) - **Try:** `Using the claude-api skill, estimate monthly cost for 50k conversations/day on Sonnet with prompt caching, and compare against Haiku.` ### 3\. Langfuse Observability Plugin: agent tracing with zero code changes V1 said monitoring integrations did not exist. The way the gap got filled surprised us; the observability vendors built the integrations themselves rather than waiting for the community. The official [Langfuse](https://langfuse.com) plugin for the Claude Code marketplace traces every session to Langfuse, covering turns, generations, tool calls, and token usage, with no code changes, and a companion skill lets Claude query traces, prompts, datasets, and scores through langfuse-cli. The repo is young at 10 stars, but it is first-party and actively pushed, which matters more here than the star count. - **Link:** [github.com/langfuse/claude-observability-plugin](https://github.com/langfuse/claude-observability-plugin) | [integration docs](https://langfuse.com/integrations/developer-tools/claude-code) - **Creator:** Langfuse (official org) - **Stats:** 10 stars, 9 forks; created May 26, 2026, pushed July 7, 2026 - **Try:** `Pull the last 20 Langfuse traces for my support agent and diagnose why tool-call latency spiked yesterday.` ### 4\. Featured: synap, persistent memory for your agents (built by us) Full disclosure before anything else: this is our skill. Maximem builds [Synap](https://www.maximem.ai/synap), and we are including it because it fills the one category no other list covers, not because it is ours; judge it by the same stats we show for everyone else. V1 ended with a warning that every team building production agents eventually hits the [context management](https://www.maximem.ai/glossary/context-management) problem: the agent forgets things mid-conversation, costs spike as context windows balloon, multi-user setups leak state between sessions, and [semantic search](https://www.maximem.ai/glossary/semantic-search) returns irrelevant results even when the embeddings are good. We wrote about why this happens in [The Memory Problem](https://www.maximem.ai/the-memory-problem). The synap skill makes the fix installable. It teaches a coding agent the entire [Synap SDK](https://docs.maximem.ai/getting-started/quickstart): when memory actually fits the use case and when it is overkill, setup with correct defaults for ingestion and retrieval, the User/Customer/Client scoping model for multi-tenant applications, and per-framework integration packages covering [18 frameworks](https://www.maximem.ai/synap/integrations) including [LangChain](https://www.langchain.com), [LangGraph](https://www.langchain.com/langgraph), [LlamaIndex](https://www.llamaindex.ai), [CrewAI](https://www.crewai.com), OpenAI Agents, [AutoGen](https://github.com/microsoft/autogen), [LiveKit](https://livekit.io), [Mastra](https://mastra.ai), the [Vercel AI SDK](https://sdk.vercel.ai), and the Claude Agent SDK. A companion `synap-codex` skill plus a single-file AGENTS.md carries the same knowledge into Codex, Cursor, [Aider](https://aider.chat), Cline, Windsurf, and [Zed](https://zed.dev). The practical difference is small to describe and large in effect. You say "add Synap long-term memory to my LangGraph agent, scoped per user," and the agent writes correct integration code with real SDK calls instead of hallucinating an API surface. On accuracy, the repo cites 92% on LongMemEval and 93.2% on LOCOMO; the [benchmark write-up](https://www.maximem.ai/blog/maximem-synap-updates-higher-benchmark-scores-and-more) has the detail. One framing we would push you to sit with: the evaluation skills on this list answer "is my prompt good?" Memory answers a different question, which is "does my agent remember the right things?" Both matter, and no amount of prompt quality substitutes for state. - **Link:** [skills directory](https://github.com/maximem-ai/maximem_synap_sdk/tree/main/skills) | [install docs](https://docs.maximem.ai/integrations/ai-coding-agents) - **Creator:** Maximem (that is us) - **Stats:** 57 stars, 11 forks; pushed July 6, 2026. Small repo, stated plainly, same as every other entry. - **Install:** `npx degit maximem-ai/maximem_synap_sdk/skills/synap ~/.claude/skills/synap` - **Try:** `Add Synap long-term memory to my LangGraph customer-support agent, scoped per user, with retrieval before each turn.` ### 5\. LangSmith Claude Code plugins: tracing for the LangGraph stack If your team runs on LangGraph, trace access inside the coding agent closes the debugging loop that V1's agent-debugger entry only gestured at. The official [LangChain](https://www.langchain.com) plugins wire Claude Code traces into [LangSmith](https://www.langchain.com/langsmith) with full conversation history, model and provider metadata, and token usage. A community sibling worth knowing about is the LangSmith Fetch skill by OthmanAdi, listed in [ComposioHQ/awesome-claude-skills](https://github.com/ComposioHQ/awesome-claude-skills), which fetches and analyzes LangSmith execution traces to debug LangGraph agents from inside the session. - **Link:** [github.com/langchain-ai/langsmith-claude-code-plugins](https://github.com/langchain-ai/langsmith-claude-code-plugins) - **Creator:** LangChain (official); LangSmith Fetch by OthmanAdi (community) - **Stats:** 56 stars, 10 forks; pushed July 7, 2026 - **Try:** `Fetch the failing LangSmith trace for run and tell me which tool call broke the graph.` ### 6\. vector-db-setup: the vector database boilerplate skill Exactly the "Pinecone and Weaviate helpers" V1 flagged as missing. Part of patricio0312rev's 100+ skill library, it handles setup boilerplate for [Pinecone](https://www.pinecone.io), [Chroma](https://www.trychroma.com), [pgvector](https://github.com/pgvector/pgvector), and [Qdrant](https://qdrant.tech), with embeddings generation via OpenAI, batch processing with rate limiting, and full CRUD per database. One flag we will not hide: the repo has not been pushed since January 12, 2026. The boilerplate it generates is stable enough that staleness matters less than it would for an observability tool, but check the output against current client versions. - **Link:** [github.com/patricio0312rev/skills](https://github.com/patricio0312rev/skills) | [marketplace page](https://claudemarketplaces.com/skills/patricio0312rev/skills/vector-db-setup) - **Creator:** patricio0312rev - **Stats:** 50 stars, 7 forks; pushed January 12, 2026 - **Try:** `Set up pgvector for my RAG pipeline: schema, embedding batch job with rate limiting, and a similarity-search helper.` ### 7\. aimock: mock every AI dependency in CI Deterministic CI for agents, without burning tokens on every test run; V1 called this gap explicitly. aimock, from [CopilotKit](https://www.copilotkit.io), mocks everything an AI app talks to, spanning LLM APIs, MCP, A2A, AG-UI, vector databases, and search, across 12 providers and 14 API surfaces including OpenAI Chat and Responses, Claude, [Gemini](https://ai.google.dev), [Bedrock](https://aws.amazon.com/bedrock/), and Vertex, with SSE streaming and chaos testing built in. Honesty requires a caveat here: aimock is an npm package and mock server, not a SKILL.md file, so treat it as the tool your testing skill drives rather than a skill itself. mcpmarket.com lists a dedicated "API Mocking & Testing" Claude skill \[GAURAV: listing unverified beyond the marketplace page; check manually before citing it\]. - **Link:** [github.com/CopilotKit/aimock](https://github.com/CopilotKit/aimock) - **Creator:** CopilotKit - **Stats:** 639 stars, 45 forks; created March 3, 2026, pushed July 8, 2026 - **Try:** `Wire aimock into my agent's test suite so CI runs against mocked Claude + MCP responses with a chaos scenario for tool timeouts.` ### 8\. Trail of Bits skills: audit-firm-grade security V1's security entries, promptinjection and ai-threat-testing, cover the prompt layer. [Trail of Bits](https://www.trailofbits.com) brings security auditing to the code itself: static analysis with [CodeQL](https://codeql.github.com) and [Semgrep](https://semgrep.dev), variant analysis, code auditing, vulnerability detection, and full audit workflows, published by a firm whose day job is breaking things for money. Trail of Bits created the repo on January 14, 2026, which made it too new for our February list; it has since collected 4,914 stars and ships under a CC-BY-SA-4.0 license. - **Link:** [github.com/trailofbits/skills](https://github.com/trailofbits/skills) - **Creator:** Trail of Bits - **Stats:** 4,914 stars, 429 forks; created January 14, 2026, pushed April 30, 2026 - **Try:** `Run the Trail of Bits audit workflow on my agent's tool-execution layer and write Semgrep rules for the injection patterns you find.` ### 9\. promptfoo-evaluation: the strongest V1 survivor Same skill as V1's number one pick, and the clearest proof that the February list aged well. It generates [Promptfoo](https://www.promptfoo.dev) evaluation configs, LLM-as-judge assertions, custom Python metrics, and regression tests. The repo grew from 531 stars in February to 1,257 in July, with its latest push on July 7, 2026. If you install one thing from this list before your next model swap, install this. - **Link:** [github.com/daymade/claude-code-skills](https://github.com/daymade/claude-code-skills) | [SkillsMP page](https://skillsmp.com/skills/daymade-claude-code-skills-promptfoo-evaluation-skill-md) - **Creator:** daymade - **Stats:** 1,257 stars, 207 forks; pushed July 7, 2026 - **Try:** `Create a promptfoo regression suite for my triage agent before I swap Sonnet for the new model.` ### 10\. Superpowers: the de facto engineering harness Jesse Vincent's Superpowers became the largest community skills framework by a wide margin, sitting at 250,520 stars as of July 9, 2026. It packages a brainstorm, plan, and execute workflow with TDD enforcement, structured debugging, git worktrees, and subagent-driven development, installable as a plugin marketplace with `/plugin marketplace add obra/superpowers-marketplace`. For AI engineers specifically, the subagent and worktree patterns are the most practical on-ramp to multi-agent orchestration available today. Claude Code reportedly ships an experimental built-in Agent Teams feature covering adjacent ground \[GAURAV: community-reported via Shipyard coverage; verify against official Claude Code docs or changelog before publish\]. - **Link:** [github.com/obra/superpowers](https://github.com/obra/superpowers) - **Creator:** Jesse Vincent (obra) - **Stats:** 250,520 stars, 22,219 forks; created October 9, 2025, pushed July 6, 2026 - **Try:** `Use superpowers to brainstorm, write a plan, and execute a refactor of my agent's tool-router with TDD.` ### 11\. wshobson/agents: write once, run in six harnesses The portability argument from earlier in this piece, embodied. Seth Hobson's multi-harness marketplace holds roughly 90 plugins, 199 agents, 161 skills, and 106 commands, all written once in Markdown and read natively by Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, and Gemini CLI. If your team runs more than one coding agent, this is the cleanest existing demonstration that a skill is a portable asset rather than a piece of vendor lock-in. - **Link:** [github.com/wshobson/agents](https://github.com/wshobson/agents) - **Creator:** wshobson (Seth Hobson) - **Stats:** 37,687 stars, 4,039 forks; pushed July 8, 2026 - **Try:** `Install the llm-application-dev plugin from wshobson/agents and use its vector-database-engineer agent to review my retrieval layer.` ### 12\. mcp-builder: the canonical way to wrap an API as an MCP server MCP is now the default integration layer, and every AI engineer eventually wraps an internal API as an MCP server. mcp-builder is the official Anthropic skill that guides that build in Python or TypeScript, with the quality conventions Anthropic wants servers to follow. It also completes a composition worth internalizing: MCP gives the agent access, skills give it behavior, and the two are complements rather than rivals. - **Link:** [mcp-builder in anthropics/skills](https://github.com/anthropics/skills/tree/main/skills/mcp-builder) - **Creator:** Anthropic - **Stats:** parent repo at 159k stars (July 9, 2026) - **Try:** `Use mcp-builder to scaffold a TypeScript MCP server for our internal billing API with auth and three read tools.` ## How to install skills in mid-2026 The mechanics moved on since February, so a short refresher. Three install paths now coexist: plugin marketplaces (`/plugin marketplace add anthropics/skills`, then enable what you want), the classic `~/.claude/skills/` directory for manual installs, and `npx degit` for pulling a single skill folder out of a larger repo. For Codex and Cursor, the same skill usually ships as an AGENTS.md or rules file; check each repo's README for the per-harness path. Claude Cowork on desktop reads the same skills as Claude Code, so one install covers both. V1's [installation guide](https://www.maximem.ai/blog/claude-skills-for-engineers) still covers the basics if you are starting from zero. ## A security note: two million skills means two million attack surfaces Skills execute code and shape agent behavior, and with over two million indexed files the odds that any random skill is safe by default are not in your favor. Before installing, read the SKILL.md and every script it references, prefer skills from named organizations or maintainers with a track record, pin to a commit rather than a moving branch, and run a new skill in an isolated environment before it touches anything real. The [travisvn/awesome-claude-skills](https://github.com/travisvn/awesome-claude-skills) list (13.8k stars) maintains a security section worth reading in full, and published research on weaponized skills shows the threat is practical rather than theoretical \[GAURAV: add the specific "Weaponizing Claude Code Skills" citation link and verify before publish\]. Treat a skill the way you would treat a [prompt injection](https://www.maximem.ai/glossary/prompt-injection) vector, because that is what a malicious one is. ## What is still missing: our gaps for 2027 The gaps-list format worked in February precisely because we could be checked on it later, so we will run it again. Four categories we could not find credible skills for in July 2026: - **Skill version pinning and lockfiles.** Installs track branches, not commits, and nothing in the toolchain resembles a package-lock for skills. - **Eval-in-CI conventions.** promptfoo-evaluation writes the suites, but there is no shared convention for running skill-driven evals as a merge gate. - **Paid skill distribution.** Two million free files and no viable way for a maintainer to charge, which partly explains how many of those files are abandoned. Check back in five months. ## FAQ **Do Claude skills work in Codex and Cursor?** Yes. The Agent Skills spec is an open standard, and Codex, Cursor, Gemini CLI, Copilot, Windsurf, Cline, and OpenCode all read SKILL.md. Some skills ship harness-specific variants, such as an AGENTS.md file, for the best fit. **How many Claude skills are there?** SkillsMP indexed 2,167,429 SKILL.md files as of July 9, 2026, up from 96,000+ in February 2026. Most are low quality or abandoned, which is why curated lists exist. **What is the best Claude skill for agent memory?** The synap skill, which we build, is the only agent-memory skill we found across the directories we checked; it wires the Synap SDK into 18 frameworks with correct scoping. The category has no community-built competitor yet, which we would honestly like to see change. **Skills vs MCP: which do I need?** Both, for different jobs. MCP connects your agent to external systems; skills teach it procedures and judgment. mcp-builder, an official skill for building MCP servers, is itself the neatest illustration of how they compose. ## Where this leaves you Five months ago the honest advice was to install those nine skills and bring your own infrastructure tooling. Today the tooling exists and your job is vetting it, a shift that happened faster than we expected when we wrote V1. The one problem that did not become easier in those five months is state. You can now trace, mock, evaluate, and secure an agent entirely through skills, yet an agent that forgets its user between sessions fails in a way none of those layers can catch. If that is the wall you are hitting, the [Synap docs](https://docs.maximem.ai/getting-started/quickstart) and the [playground](https://synap.maximem.ai/playground) are the fastest way to test whether memory fixes it, and [the founder's calendar](https://cal.com/gaurav-dadhich-maximem-ai/30min) is open if you would rather argue about it live. --- Source: [https://www.maximem.ai/blog/claude-skills-for-engineers-h2-2026](https://www.maximem.ai/blog/claude-skills-for-engineers-h2-2026)