By IvánUpdated 2026-07-273 versions · latest released 2026-07-27agents@clawhub_ivangdavila/agents
Agent FrameworksMemory
According to AlphaMoat, as of 2026-10, Agents has 6,313 downloads and 4 stars — ranked by downloads #4,533 among 10,914 AI skills in this ranking, and #611 of 1,632 in AI Agent.
The list defaults to total downloads and can also be sorted by stars; detail-page rankings use total downloads.
| Platform | Downloads | Stars | Comments |
|---|---|---|---|
| ClawHub ↗ | 3.3K | 4 | - |
| SkillHub ↗ | 6.3K | 4 | — |
Platforms count differently and update at different times.
Designs, debugs, evaluates, and hardens AI agents — the loop, tools, memory, context budget, cost, and escalation — independent of any framework. Use when an agent loops forever, repeats a tool call, drifts from its instructions after many turns, invents tool arguments, stops mid-task, or swallows a tool error silently; when deciding single agent versus several, or which framework to build on; when token cost per task or p95 latency has to come down; when designing tool schemas, retries, timeouts, checkpoints, or human approval; when writing an eval set or a regression suite for agent behavior; when prompt injection, tool abuse, or an over-permissioned action is the risk; and when specifying an agent's purpose, escalation rules, and cost ceiling for a team. Covers memory design, multi-agent handoffs, tracing, and rollout. Not for LangChain APIs (langchain), retrieval pipelines (rag), prompt craft alone (prompting), or agent persona and voice (agent).
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change. Other skills share this name; figures from before the change are attributed to this one, which currently has the most downloads.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-10 | 6.3K | — | 4 |
| 2026-09 | 4.8K | — | 4 |
| 2026-08 | 3.1K | +20.5% | 4 |
| 2026-07 | 2.5K | +19.6% | 4 |
| 2026-06 | 2.1K | +9.4% | 3 |
| 2026-05 | 1.9K | — | 3 |
| 2026-03 | 1.1K | — | 2 |
Records learnings, errors and corrections as Markdown files for later reuse and promotion into workspace memory.
Finds skills in local and remote catalogs from a plain-language request and recommends what to install.
A browser-automation command line skill for AI agents.
Turns corrections and self-review into tiered memory and promotes or archives it by use.
A workspace setup guide for planning proactive follow-up, working memory and review routines for an AI agent.
A guide to creating and updating effective Claude skills.
Reviews a third-party skill's source, permissions, and danger patterns against a checklist before it is installed.
以李白（701—762，字太白，号青莲居士，世称诗仙）的诗学观、意象系统与口吻，进行唐诗赏析、文学评论、考据问答与拟体创作。触发词：李白、李太白、太白、青莲居士、诗仙、谪仙人、将进酒、静夜思、蜀道难、唐诗、盛唐诗人、浪漫主义诗人、拟李白风格、以李白视角、李白会怎么写、用李白口吻。能力：诗词鉴赏与风格DNA解析、诗学观与文学谱系评述、诗句出处考据与真伪边界标注、以李白口吻答问、拟李白体旧体诗（必显式标注非原作）。适用边界：仅用于唐代文学与古典诗歌相关话题，非此类话题不触发；本 skill 为文学普及与研究辅助，不代表李白本人立场，所拟之作均非李白原作；涉及生平佚事、篇目归属等内容不构成学术定论，皆标存疑或待考。
Turns corrections and self-review into tiered memory and promotes or archives it by use.
Creates, inspects, and edits Excel files while keeping formulas, formatting, and workbook structure intact.
A working guide for Word and DOCX documents that keeps styles, numbering, and layout intact after edits.
An AI video generation and orchestration skill across several model APIs.
A data-analysis skill that turns databases, spreadsheets, and raw exports into decision-ready conclusions.
Data as of 2026-10. Downloads and stars are aggregated monthly from public skill registries (ClawHub, SkillHub). See data methodology.