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-memorygit 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)<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/agent-memory"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/agent-memory/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"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/agent-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00018 | $0.00711 |
| Opus 5 | $0.00009 | $0.00356 |
| Sonnet 5 | $0.00004 | $0.00142 |
| Haiku 4.5 | $0.00002 | $0.00071 |
Grade A, and why
agent-memory 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 10d 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.
This is a copy
100% identical to agent-memory — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agentMemory Skill
When to Use
Use this skill when you need a hybrid memory system that provides persistent, searchable knowledge management for AI agents.
This skill extends your capabilities by providing a persistent, searchable memory bank that automatically syncs with project documentation.
Prerequisites
- Node.js installed
- Check if
agentMemoryis already installed in the project:ls -la .agentMemory
Setup
-
Install Dependencies:
npm install -
Build the Project:
npm run compile -
Start the Memory Server: You need to run the MCP server to interact with the memory bank.
npm run start-server <project_id> <absolute_path_to_workspace>Note: This skill typically runs as a background process or via an mcp-server configuration. ensuring it is running is key.
Capabilities (MCP Tools)
Once the server is running, you can use these 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({})
Workflow
- Initialization: The first time you run this in a project, it may attempt to import existing markdown memory banks from
.kilocode/,.clinerules/, or.roo/. - Development Loop:
- Before Task: Search memory for relevant context.
- During Task: Use read/search to answer questions.
- After Task: Write new findings to memory.
- Sync: Your writes are automatically synced to standard markdown files in the project.
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.
- 10d ago First seen · 85 lines · 18 tokens per session scan A b630b9e32991
agent-memory is a skill published in the GitHub repository lingxling/awesome-skills-cn (281 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 711 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to agent-memory, differing in 8 lines, and is treated as a copy.
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-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-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-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.