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 | $0.00160 | $0.01402 |
| Opus 5.5 | $0.00064 | $0.00561 |
| Sonnet 5.5 | $0.00032 | $0.00280 |
| Haiku 4.5 | $0.00016 | $0.00140 |
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 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Review
Purpose
Review the user's memory landscape and produce a clear report of proposed changes, grouped by action type. The skill never applies changes by itself — it presents proposals for user approval. It also surfaces duplicates, outdated entries, and cross-layer conflicts.
When to Use
- The user wants to review, organize, or promote their auto-memory entries.
- The user asks what should be promoted into
MEMORY.md, or where an entry belongs. - The user suspects duplicate, stale, or contradictory memory entries.
- Trigger phrases: "review my memories", "organize memories", "promote to MEMORY.md", "clean up memories", "memory review", "check for duplicate memories", "what should go in MEMORY.md", 整理记忆, 检查记忆, 记忆回顾, 清理记忆, 提升到 MEMORY.md。
When NOT to Use
- The user just wants something written to a memory file now -> write it directly with Edit/Write; the review-and-propose flow is unnecessary overhead.
- The user wants to change a tool's own settings (editor theme, IDE keybindings) -> that is not assistant memory; direct them to the tool's configuration.
- The target file does not exist and the user only wants it created -> create it, then optionally offer the review.
Workflow
Each step has a success criterion; do not advance until it is met.
- Gather all memory layers. Read every existing memory file in the layers above. The auto-memory content is already in your system prompt — review it there. Note which team memory sections exist, if any. Success: you hold the contents of all layers and can compare them.
- Classify each auto-memory entry. For every substantive entry, determine the best
destination using the table in
## Decision Rules. Success: each entry has a proposed destination or is flagged as ambiguous. - Identify cleanup opportunities. Scan across all layers for duplicates (already captured in project/user memory), outdated entries (contradicted by newer auto-memory), and conflicts (contradictions between any two layers, noting which is more recent). Success: all cross-layer issues identified.
- Present the report grouped by action type, per
## Output. Success: the user can approve or reject each proposal individually.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed · +30 lines · +116 tokens per session 83a66f725d74
- 1mo 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 15d ago), licensed MIT. It adds 160 tokens to every session and 1,402 once invoked, about $0.0006 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 2026-08-31.
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