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 hifriendbot/cogmemai-mcp --skill cogmemai-memorygit clone --depth 1 https://github.com/hifriendbot/cogmemai-mcpWrote 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/hifriendbot/cogmemai-mcp/cogmemai-memory)<a href="https://agentmods.dev/skills/hifriendbot/cogmemai-mcp/cogmemai-memory"><img src="https://agentmods.dev/badge/skills/hifriendbot/cogmemai-mcp/cogmemai-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/hifriendbot/cogmemai-mcp/cogmemai-memory"><img src="https://agentmods.dev/badge/skills/hifriendbot/cogmemai-mcp/cogmemai-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 | $0.00083 | $0.01775 |
| Opus 5 | $0.00042 | $0.00888 |
| Sonnet 5 | $0.00017 | $0.00355 |
| Haiku 4.5 | $0.00008 | $0.00178 |
Grade A, and why
cogmemai-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 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.
How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CogmemAi Memory Management
CogmemAi gives your Ai assistant persistent memory across sessions. This skill teaches you how to use it effectively — when to save, what to save, and how to keep your memory organized.
Core Workflow
Session Start
Always call get_project_context before responding to the user's first message. This loads your most important memories from previous sessions. Pass an optional context parameter describing the current task for better relevance.
While Working
Save memories when you learn something important:
- Architecture decisions and tech stack details (importance: 8-10)
- User preferences for coding style, tools, workflow (importance: 7-9)
- Bug fixes, gotchas, and workarounds (importance: 6-8)
- Key file paths and project structure (importance: 7-9)
- Patterns and conventions used in the codebase (importance: 6-8)
- Dependency versions and constraints (importance: 5-7)
After Significant Exchanges
Use extract_memories after important conversations instead of manually saving each fact. It automatically identifies what's worth remembering:
- After debugging a tricky bug — captures the root cause and fix
- After discussing architecture — captures decisions and trade-offs
- After the user explains their preferences — captures all stated preferences
- After a planning session — captures key decisions and constraints
This is faster and more thorough than manual save_memory calls for multi-fact exchanges.
Session End
When the session ends or the user says goodbye, save a session summary capturing what was accomplished, decisions made, and next steps.
When to Save a Memory
Save immediately when:
- The user tells you a preference ("always use tabs", "never auto-commit")
- You discover an architecture decision ("auth uses JWT", "DB is PostgreSQL 15")
- You fix a bug and the fix is non-obvious
- You learn a project convention ("components use PascalCase", "tests in tests/")
- The user makes a decision ("we'll use Redis for caching")
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.
- 11d ago First seen · 154 lines · 83 tokens per session scan A 3458e90385fc
cogmemai-memory is a skill published in the GitHub repository hifriendbot/cogmemai-mcp (7 stars, last pushed today), licensed MIT. It adds 83 tokens to every session and 1,775 once invoked, about $0.0004 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.
Other skills, from other repositories
agent-carnet
Use this skill when the user asks to save, recall, find, or organize notes. Triggers on: 'remember this', 'save this', 'note this', 'what did we discuss about...', 'check the notebook', 'find in carnet'. Also use proactively when discovering findings worth preserving across sessions.
neat-freak
Knowledge and governance closeout: reconcile project docs, rule files (CLAUDE.md/AGENTS.md), authorized agent memory, and workspace residue with what the code and runtime actually do, so the next session or the next person starts from one current answer. Trigger when the user names "neat-freak", "洁癖", or "/neat" — and…
mnemos
Task-scoped memory lifecycle — typed MnemoGraph prevents lossy context compaction by treating facts/decisions/code-refs/handoffs as distinct node types with per-type eviction policies.
grimoire
Use when the user says 'update context', 'update claude', 'save library', or after significant project changes.
keep-the-why
Extract and preserve the reasoning code cannot explain - decisions, rejected alternatives, workarounds, incidents, constraints - plus project setup and maintainer interviews. Not for what changed (see Keep a Changelog) - only why. Also the place for complaints, feedback and settings changes about this skill itself.
ln-31-performance-optimizer
Profiles and improves a measured latency, throughput, CPU, memory, or I/O problem. Not for speculative tuning or cosmetic refactoring.