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 renky1025/agent-skills --skill claude-remembergit clone --depth 1 https://github.com/renky1025/agent-skillsWrote 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/renky1025/agent-skills/claude-remember)<a href="https://agentmods.dev/skills/renky1025/agent-skills/claude-remember"><img src="https://agentmods.dev/badge/skills/renky1025/agent-skills/claude-remember/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/renky1025/agent-skills/claude-remember"><img src="https://agentmods.dev/badge/skills/renky1025/agent-skills/claude-remember.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 | $0.00044 | $0.01007 |
| Opus 5 | $0.00022 | $0.00504 |
| Sonnet 5 | $0.00009 | $0.00201 |
| Haiku 4.5 | $0.00004 | $0.00101 |
Grade A, and why
claude-remember 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.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Review
Goal
Review the user's memory landscape and produce a clear report of proposed changes, grouped by action type. Do NOT apply changes — present proposals for user approval.
Memory File Locations
AI agent 的记忆系统通常分多个层级。执行本技能前，先确认当前运行环境的记忆层级：
- 项目级记忆: 存储于项目根目录下的记忆文件（如
MEMORY.md/CLAUDE.md/.cursorrules等），所有协作者共享 - 用户级记忆: 存储于用户主目录下的全局记忆文件，跨项目共享个人偏好
- 运行日报: 每日追加的工作日志（如
YYYY-MM-DD.md） - 自动记忆/云记忆: 系统自动维护的长期记忆（通常不可手动修改）
执行前先扫描: 使用 Glob/Grep 工具确认实际存在的记忆文件路径，不要假设固定的文件命名。
Steps
1. Gather all memory layers
Read all existing memory files from the locations above. Your auto-memory content is already in your system prompt — review it there. Note which team memory sections exist, if any.
Success criteria: You have the contents of all memory layers and can compare them.
2. Classify each auto-memory entry
For each substantive entry in auto-memory, determine the best destination:
| Destination | What belongs there | Examples |
|---|---|---|
| 项目 MEMORY.md | 项目级约定和规范，所有协作者应遵循 | "用 bun 不用 npm"、"API 路由使用 kebab-case"、"测试命令是 bun test" |
| 用户 MEMORY.md | 跨项目的用户个人偏好和习惯 | "偏好简洁回复"、"总是解释 trade-off"、"运行测试前不要提交" |
| 项目日报 | 每日工作日志，仅追加 | 当天完成的工作、技术选型、项目约定变更 |
| 保持在自动记忆 | 临时上下文、工作笔记、不明确归属的条目 | 会话特定观察、不确定的模式 |
Important distinctions:
- 记忆文件存储的是 AI 助手的指令，不包括用户对外部工具的偏好（编辑器主题、IDE 快捷键等）
- Workflow practices (PR conventions, merge strategies, branch naming) are ambiguous — ask the user whether they're personal or team-wide
- When unsure, ask rather than guess
Success criteria: Each entry has a proposed destination or is flagged as ambiguous.
3. Identify cleanup opportunities
Scan across all layers for:
- Duplicates: Auto-memory entries already captured in project or user memory files → propose removing from auto-memory
- Outdated: Memory entries contradicted by newer auto-memory entries → propose updating the older layer
- Conflicts: Contradictions between any two layers → propose resolution, noting which is more recent
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 · 83 lines · 44 tokens per session scan A 7c6bd686409d
claude-remember is a skill published in the GitHub repository renky1025/agent-skills (11 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 1,007 once invoked, about $0.0002 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 2026-08-31.
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