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 phuonghx/aim-cli --skill memory-systemgit clone --depth 1 https://github.com/phuonghx/aim-cliWrote 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/phuonghx/aim-cli/memory-system)<a href="https://agentmods.dev/skills/phuonghx/aim-cli/memory-system"><img src="https://agentmods.dev/badge/skills/phuonghx/aim-cli/memory-system/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/phuonghx/aim-cli/memory-system"><img src="https://agentmods.dev/badge/skills/phuonghx/aim-cli/memory-system.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 | $0.00086 | $0.01439 |
| Opus 5.5 | $0.00034 | $0.00576 |
| Sonnet 5.5 | $0.00017 | $0.00288 |
| Haiku 4.5 | $0.00009 | $0.00144 |
Grade A, and why
memory-system 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory System
Without memory, every session starts from zero — preferences re-explained, conventions re-discovered, decisions re-litigated. This skill fixes that by keeping a small, searchable record on disk. A short index points to detailed topic files, so the agent can pull back exactly what it needs.
The trade is favorable: loading the index costs roughly a thousand tokens, while skipping the re-discovery it replaces saves several thousand.
How It's Laid Out
.aim-agents/memory/
├── MEMORY.md ← the index — a thin set of pointers, ≤200 lines
├── user-preferences.md ← who the user is: role, style, tooling
├── project-conventions.md ← how this project does things
├── tech-decisions.md ← architectural calls already made
├── feedback-history.md ← what landed well or badly
└── <topic>.md ← more topic files as the need arises
The Index File
MEMORY.md is a directory, not a database. Each line is a one-glance summary that names the topic file holding the full story.
Keep it disciplined:
- Cap the whole file at 200 lines.
- Hold each entry to about 150 characters.
- Use the shape:
- [type] summary → topic-file.md. - Stick to four tags:
[user],[feedback],[project],[reference].
A worked example:
# Memory Index
## User
- [user] Works on macOS, lives in the terminal, prefers fish → user-preferences.md
- [user] Staff backend engineer, ~10 yrs in distributed systems → user-preferences.md
- [user] Writes in English; occasional German → user-preferences.md
## Project
- [project] pnpm only — never npm or yarn → project-conventions.md
- [project] React 19 with the compiler on → tech-decisions.md
- [project] Trunk-based; short-lived branches off main → project-conventions.md
## Feedback
- [feedback] Wants answers tight, no preamble → feedback-history.md
- [feedback] Finds long walkthroughs tedious → feedback-history.md
- [feedback] Reaches for tables over bullet lists → feedback-history.md
## Reference
- [reference] Staging gateway listens on 8443 → infrastructure-notes.md
- [reference] Release flow: tag, then CI promotes → project-conventions.md
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 · 159 lines · 86 tokens per session scan A 2e891c40c51d
memory-system is a skill published in the GitHub repository phuonghx/aim-cli (1 stars, last pushed 3mo ago), licensed MIT. It adds 86 tokens to every session and 1,439 once invoked, about $0.0003 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.
Other skills, from other repositories
hindsight-architect
Expert memory architect. Understands your application, identifies where memory adds value, and produces an implementation plan with bank config, tag schema, and code.
hindsight-local
Store user preferences, learnings from tasks, and procedure outcomes. Use to remember what works and recall context before new tasks. (user).
create-agent
Create a new Hindsight-powered subagent with long-term memory. Use when the user wants a specialized agent that learns and remembers across sessions.
hindsight-docs
Complete Hindsight documentation for AI agents. Use this to learn about Hindsight architecture, APIs, configuration, and best practices.
hindsight-memory
Long-term memory for the agent via Hindsight. Use to recall relevant past context before answering, retain durable facts as you learn them, and reflect over accumulated memory for the "why" behind a decision. Load whenever continuity across sessions matters — the user refers to earlier work, states a lasting…
bot-handoff
Keep a durable record of work passed between Bots in the shared Hindsight memory bank. Use when handing work to another Bot, when picking up work another Bot started, and when a finding would help other Bots, including Bots that are not part of the current conversation.