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 webzler/agentMemory --skill agentmemorygit clone --depth 1 https://github.com/webzler/agentMemoryWrote 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/webzler/agentmemory/agentmemory)<a href="https://agentmods.dev/skills/webzler/agentmemory/agentmemory"><img src="https://agentmods.dev/badge/skills/webzler/agentmemory/agentmemory/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/webzler/agentmemory/agentmemory"><img src="https://agentmods.dev/badge/skills/webzler/agentmemory/agentmemory.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 | $0.00553 |
| Opus 5.5 | $0.00007 | $0.00221 |
| Sonnet 5.5 | $0.00004 | $0.00111 |
| Haiku 4.5 | $0.00002 | $0.00055 |
Grade A, and why
agentMemory 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.
This is a copy
86% identical to agent-memory — 72 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agentMemory Skill
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.
- 1mo ago First seen · 68 lines · 18 tokens per session scan A 17160641b60b
agentMemory is a skill published in the GitHub repository webzler/agentMemory (33 stars, last pushed 2d ago), licensed MIT. It adds 18 tokens to every session and 553 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to agent-memory, differing in 72 lines, and is treated as a copy.
Other skills, from other repositories
memory-audit-belief-duel
A guided review process for conflicting beliefs or memories. It examines cases where two conclusions cannot both be true, including conflicts between a general rule and a more specific memory.
memory-audit-discoverability
A review guide for checking whether stored memories can be found at the right time. It focuses on where memories are attached, when they are triggered, whether aliases are missing, and whether a parent has too many children.
memory-audit-pattern-extraction
A method for investigating repeated mistakes by comparing related memories and checking whether an earlier reminder failed. It looks at where the reminder was stored, when it was created, and whether it was strong enough to prevent the mistake.
memory-audit-node-decomposition
A method for splitting an oversized knowledge note into smaller notes, each focused on one independent idea. It also explains how to keep useful core information in the original note.
memory-audit
An entry point for reviewing and maintaining an AI agent's stored memories. It describes how to remove repetition, preserve useful reasoning, and update memories when old conclusions no longer fit.
memory-audit-dead-data-purge
A review process for identifying memories that do not change future actions. It tests whether a note contains useful, experience-based guidance or only sounds meaningful without affecting decisions.