Prerequisites — install only what you need, each source is optional

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<h1>Claudest</h1>

<p>A curated Claude Code plugin marketplace. Everything here is something I personally use, build, and iterate on across real projects. If it's in this marketplace, it works.</p>

![License](https://img.shields.io/badge/license-MIT-blue?style=flat-square)
![Release](https://img.shields.io/github/v/release/gupsammy/claudest?style=flat-square)
![Stars](https://img.shields.io/github/stars/gupsammy/claudest?style=social)

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## ⚡ Installation

Add the marketplace, then install any plugin:

```
/plugin marketplace add gupsammy/claudest
/plugin install claude-memory@claudest
```

To enable auto-updates, run `/plugin`, go to the Marketplaces tab, and toggle auto-update for Claudest.

---

## 📦 Plugins

| Plugin | Version | Skills |
|--------|---------|--------|
| [claude-memory](#claude-memory) | `0.7.8` | recall-conversations · extract-learnings |
| [claude-research](#claude-research) | `0.1.5` | run-research · search-youtube |
| [claude-coding](#claude-coding) | `0.1.13` | commit · push-pr · clean-branches · update-claudemd · make-readme · make-changelog · update-readme |
| [claude-skills](#claude-skills) | `0.2.3` | create-skill · repair-skill · repair-agent · improve-skill · create-cli · create-agent |
| [claude-thinking](#claude-thinking) | `0.1.5` | brainstorm |
| [claude-content](#claude-content) | `0.3.1` | generate-image · compress-video · convert-video · make-gif · share-social · extract-audio |
| [claude-utilities](#claude-utilities) | `0.1.7` | convert-to-markdown |
| [claude-claw](#claude-claw) | `0.2.0` | claw-advisor · create-claw-skill |

---

<a id="claude-memory"></a>

### 🧠 claude-memory &nbsp; ![v0.7.8](https://img.shields.io/badge/v0.7.8-blue?style=flat-square)

Conversation memory for Claude Code. Recall what happened yesterday, last week, or three weeks ago.

LLMs don't carry anything forward between sessions. Every conversation starts blank. Claude Code gives agents core memory (CLAUDE.md files loaded every session), procedural memory (skills and tool definitions), and archival memory (auto memory notes the agent writes for itself). What was missing: recall memory. The ability to search and retrieve actual past conversations.

That's what claude-memory provides. It stores every session in a SQLite database with full-text search (FTS5, BM25 ranking, zero external dependencies) and makes past conversations available to the agent in two ways.

First, automatic context injection. On every session start, a hook queries recent sessions and injects the most recent meaningful one into context. The agent already knows what you worked on last time before you say a word. This is what makes the plan-in-one-session, implement-in-the-next workflow possible.

Second, on-demand search. The `recall-conversations` skill lets the agent (or you) search conversation history by keywords, browse recent sessions, or run structured analyses like retrospectives and gap-finding. Ask "what did we decide about the API design?" and the agent searches your history. The search works because the agent constructs the queries, not you — it extracts keywords, sends them to FTS5, and iterates if the first results aren't good enough. No vector database, no embedding pipeline, no external dependencies. Just SQLite and Python's standard library.

The `extract-learnings` skill is a route from recall memory into archival memory. It reads past conversations, identifies non-obvious insights and gotchas worth preserving, and proposes placing them at the right layer in the memory hierarchy (global CLAUDE.md, repo CLAUDE.md, MEMORY.md, or topic files) with diffs and rationale. Learnings that would otherwise evaporate when context resets get distilled into persistent knowledge.

For the full story behind the architecture: [What I Learned Building a Memory System for My Coding Agent](https://www.reddit.com/r/ClaudeCode/comments/1r1w397/comment/o5294lk/).

```
/plugin install claude-memory@claudest
```

---

<a id="claude-research"></a>

### 🔍 claude-research &nbsp; ![v0.1.5](https://img.shields.io/badge/v0.1.5-blue?style=flat-square)

Cross-platform research skills for Claude Code. Two complementary tools: a multi-source deep research pipeline and a standalone YouTube research toolkit.

`run-research` is an autonomous research agent that queries Reddit, X/Twitter, YouTube, and the web simultaneously, then synthesizes what it finds. It classifies your intent — recommendations, news, prompting techniques, or general exploration — and shapes the queries accordingly. Each source runs in full before synthesis begins. Results are weighted by engagement (upvotes, likes, reposts) because engagement is aggregate human signal. A stats block at the end shows exactly what was searched: thread counts, upvote totals, video counts, transcripts read. Every cited claim traces back to a real source — @handles, subreddit names, channel names — never raw URLs.

Sources are detected at runtime. If reddit-c

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