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 lingxling/awesome-skills-cn --skill agent-memory-mcpgit clone --depth 1 https://github.com/lingxling/awesome-skills-cnWrote 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/lingxling/awesome-skills-cn/agent-memory-mcp)<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/agent-memory-mcp"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/agent-memory-mcp/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/lingxling/awesome-skills-cn/agent-memory-mcp"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/agent-memory-mcp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00026 | $0.00618 |
| Opus 5 | $0.00013 | $0.00309 |
| Sonnet 5 | $0.00005 | $0.00124 |
| Haiku 4.5 | $0.00003 | $0.00062 |
Grade A, and why
agent-memory-mcp 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 11d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- agent-memory-mcp — 100% identical, 0 lines differ
- agent-memory-mcp — 100% identical, 0 lines differ
- agent-memory-mcp — 100% identical, 0 lines differ
- agent-memory-mcp — 100% identical, 0 lines differ
- agent-memory-mcp — 100% identical, 0 lines differ
- agent-memory-mcp — 100% identical, 0 lines differ
- agent-memory-mcp — 100% identical, 0 lines differ
- agent-memory-mcp — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Memory Skill
This skill provides a persistent, searchable memory bank that automatically syncs with project documentation. It runs as an MCP server to allow reading/writing/searching of long-term memories.
Prerequisites
- Node.js (v18+)
Setup
-
Clone the Repository: Clone the
agentMemoryproject into your agent's workspace or a parallel directory:git clone https://github.com/webzler/agentMemory.git .agent/skills/agent-memory -
Install Dependencies:
cd .agent/skills/agent-memory npm install npm run compile -
Start the MCP Server: Use the helper script to activate the memory bank for your current project:
npm run start-server <project_id> <absolute_path_to_target_workspace>Example for current directory:
npm run start-server my-project $(pwd)
Capabilities (MCP Tools)
memory_search
Search for memories by query, type, or tags.
- Args:
query(string),type?(string),tags?(string[]) - Usage: "Find all authentication patterns" ->
memory_search({ query: "authentication", type: "pattern" })
memory_write
Record new knowledge or decisions.
- Args:
key(string),type(string),content(string),tags?(string[]) - Usage: "Save this architecture decision" ->
memory_write({ key: "auth-v1", type: "decision", content: "..." })
memory_read
Retrieve specific memory content by key.
- Args:
key(string) - Usage: "Get the auth design" ->
memory_read({ key: "auth-v1" })
memory_stats
View analytics on memory usage.
- Usage: "Show memory statistics" ->
memory_stats({})
Dashboard
This skill includes a standalone dashboard to visualize memory usage.
npm run start-dashboard <absolute_path_to_target_workspace>
Access at: http://localhost:3333
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
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.
- 11d ago First seen · 93 lines · 26 tokens per session scan A 9c5b31f7fd69
agent-memory-mcp is a skill published in the GitHub repository lingxling/awesome-skills-cn (281 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 618 once invoked, about $0.0001 per session on Opus 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
log-session
Append a structured entry to the project's session log (docs/LOGS.md): what was done this session, files touched, decisions taken, and the next step — so the next session (or another person) can pick up the thread without re-reading git history. Run it before /clear, before closing Claude Code, or at any natural…
context-degradation
Diagnosing context FAILURES — lost-in-middle, poisoning, distraction, confusion, and clash patterns with model-agnostic measurement workflows. Use when the user asks to "diagnose context problems", "fix lost-in-middle issues", "debug agent failures", "understand context poisoning", or mentions context degradation…
context-compression
REDUCING context size — summarization strategies, anchored iterative summarization, tokens-per-task optimization, compaction triggers, and probe-based evaluation. Use when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions structured…
context-fundamentals
Foundational theory of context engineering — what context IS, how attention works, progressive disclosure principles, and context budgeting basics. Use when the user asks to "understand context", "explain context windows", "learn context engineering", or discusses context components, attention mechanics, or context…
context-optimization
EXTENDING effective context capacity — KV-cache optimization, observation masking, context partitioning, and retrieval strategies. Use when the user asks to "optimize context", "implement KV-cache", "partition context", "mask observations", or mentions extending context capacity or cache-friendly prompt design. NOT…
memory-integration
Use to maintain context across sessions - integrates episodic-memory for conversation recall and mcpmemory knowledge graph for persistent facts.