Claude Code Templates is a command-line tool and catalogue for configuring Anthropic’s Claude Code with agents, commands, settings, hooks, integrations, skills, and project templates. Developers use it to browse and install reusable components for their coding workflows. The catalogue includes many of these Claude Code components.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add davila7/claude-code-templates --skill memory-searchgit clone --depth 1 https://github.com/davila7/claude-code-templatesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/davila7/claude-code-templates/memory-search)<a href="https://agentmods.dev/skills/davila7/claude-code-templates/memory-search"><img src="https://agentmods.dev/badge/skills/davila7/claude-code-templates/memory-search/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/davila7/claude-code-templates/memory-search"><img src="https://agentmods.dev/badge/skills/davila7/claude-code-templates/memory-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00057 | $0.00595 |
| Opus 5.5 | $0.00023 | $0.00238 |
| Sonnet 5.5 | $0.00011 | $0.00119 |
| Haiku 4.5 | $0.00006 | $0.00060 |
Grade A, and why
memory-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Maestro Memory Search
Search your conversation history using semantic, keyword, and symbol matching. Recall past decisions, discussions, and context across sessions. Part of the AI Maestro suite.
Prerequisites
Requires AI Maestro running locally. Memory indexing uses CozoDB for vector search.
# Install memory tools
git clone https://github.com/23blocks-OS/ai-maestro-plugins.git
cd ai-maestro-plugins && ./install-memory-tools.sh
Core Behavior
Before starting any task, search memory for relevant context:
Receive instruction -> Search memory -> Then proceed
Commands
| Command | Description |
|---|---|
memory-search.sh "<query>" |
Hybrid search (recommended) |
memory-search.sh "<query>" --mode semantic |
Find conceptually related |
memory-search.sh "<query>" --mode term |
Exact text matching |
memory-search.sh "<query>" --mode symbol |
Code symbol matching |
memory-search.sh "<query>" --role user |
Only user messages |
memory-search.sh "<query>" --role assistant |
Only assistant messages |
Search Modes
| Mode | Best For |
|---|---|
hybrid (default) |
General search, most cases |
semantic |
Related concepts, different wording |
term |
Exact function/class names |
symbol |
Code identifiers across contexts |
Usage Examples
# User asks to continue previous work
memory-search.sh "authentication"
# Find a specific component discussion
memory-search.sh "PaymentService" --mode term
# Find related design discussions
memory-search.sh "error handling patterns" --mode semantic
# Find code symbol references
memory-search.sh "processPayment" --mode symbol
Combining with Other Skills
For complete context, pair with docs-search and graph-query:
memory-search.sh "feature" # What did we discuss?
docs-search.sh "feature" # What do docs say?
graph-describe.sh ComponentName # What is the structure?
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 76 lines · 57 tokens per session scan A 34d7aa676ade
memory-search is a skill published in the GitHub repository davila7/claude-code-templates (32,842 stars, last pushed yesterday), licensed MIT. It adds 57 tokens to every session and 595 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
memory
Cross-cutting: what AutoCode remembers and how every session starts. Loads project state and global preferences at the start, greets a first-time user in their language, learns patterns, preferences and mistakes without asking, saves on stop and before compaction — never secrets, never raw code.
folded-memory-implementation
Developer implementation guide for building hierarchical (folded) memory into an Agent. Three-layer architecture where recent turns stay detailed, older content compresses into episodes, and the oldest distills into durable semantic facts. Use when compact-memory-implementation is not retaining enough, or when agents…
agent-memory-implementation
Restructures a chaotic or overgrown MEMORY.md into a clean 2-layer architecture based on how Claude Code's autoDream system organizes memory — a lightweight pointer index (always loaded) and topic files (loaded on demand). Stale or superseded memories are deleted or corrected in place — not archived. Use this skill…
compact-memory-implementation
Developer implementation guide for adding compact memory to an Agent — covers fork agent pattern for compaction, trigger strategy, summary format design, and memory restoration in subsequent sessions. Use when a developer asks how to implement compact memory, context compression, or memory persistence in their agent…
harness-step3-session-management
A project setup for keeping an agent’s work state in files between separate chat sessions. It creates a task list, a progress summary, and a script for checking the project environment.
session-dream
End-of-session memory distillation — extracts key decisions, eliminated approaches, new discoveries, and current blockers from the current conversation and writes them to MEMORY.md topic files. Based on Claude Code's autoDream background consolidation service. Activate when the user says "dream", "/dream", "save…