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 ntorga/agent-starter-kit --skill agent-memorygit clone --depth 1 https://github.com/ntorga/agent-starter-kitWrote 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/ntorga/agent-starter-kit/agent-memory)<a href="https://agentmods.dev/skills/ntorga/agent-starter-kit/agent-memory"><img src="https://agentmods.dev/badge/skills/ntorga/agent-starter-kit/agent-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/ntorga/agent-starter-kit/agent-memory"><img src="https://agentmods.dev/badge/skills/ntorga/agent-starter-kit/agent-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 | $0.00011 | $0.02598 |
| Opus 5.5 | $0.00004 | $0.01039 |
| Sonnet 5.5 | $0.00002 | $0.00520 |
| Haiku 4.5 | $0.00001 | $0.00260 |
Grade A, and why
agent-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 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Agents start cold every session — lessons, preferences, and interrupted work vanish when the conversation ends. This skill defines a file-based memory with two layers: long-term memory (project knowledge that feeds every dispatch) and session memory (an interaction log that lets the next session resume with full context). Together they form a loop: feedback given once stays, and interrupted work resumes with its full trail.
Procedure
-
Check for the memory directory. Look for
.memory/at the project root. If it does not exist, create it withlong-term.md(initialized with the six section headers from the long-term schema below) and subdirectories:session/,plan/,todo/,reviews/(all empty). -
Read session memory at session start. List all files in
.memory/session/. For each file with statuspausedorin-progress, read its Current Task and last 5 log entries. Present the list to the user and ask which action to take:- Resume a paused session — that session becomes the current session. If it has an Active Todo, read the todo and include its unchecked items in the summary.
- Start new — create a new session file in
.memory/session/(naming convention below). Any existing paused sessions remain on disk for later. - Switch mid-conversation — pause the current session and resume or start a different one. The user may request this at any point, not just at session start.
- Files with status
doneare stale — delete them silently.
-
Read long-term memory. Read
.memory/long-term.md. This step is read-only — do not modify long-term memory here. -
Record lessons as they surface. Watch for learning signals throughout the session — do not wait for the user to explicitly frame something as "feedback." Three signal tiers govern when to write:
- Strong signal — explicit statement. The user says "I prefer X," "always do Y," "never do Z." Record immediately.
- Medium signal — correction. The user modifies, rejects, or overrides a sub-agent's output. Extract the underlying preference or rule. Before you record a code-related observation, read the relevant files to verify it. Do not record raw claims without checking.
- Weak signal — implicit pattern. The user consistently does X across multiple interactions but has never stated it. Do not record yet — wait for a strong or medium signal to confirm.
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 · 170 lines · 11 tokens per session scan A 356995c53122
agent-memory is a skill published in the GitHub repository ntorga/agent-starter-kit (148 stars, last pushed 1mo ago), licensed MIT. It adds 11 tokens to every session and 2,598 once invoked, about $0.0000 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 2026-09-13.
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