Borrowing it
Nothing to install: this file belongs to neoneye/agent-memory-atlas. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/neoneye/agent-memory-atlas/main/.agents/skills/screen-repository/SKILL.mdgit clone --depth 1 https://github.com/neoneye/agent-memory-atlasWrote 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/neoneye/agent-memory-atlas/screen-repository)<a href="https://agentmods.dev/skills/neoneye/agent-memory-atlas/screen-repository"><img src="https://agentmods.dev/badge/skills/neoneye/agent-memory-atlas/screen-repository/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/neoneye/agent-memory-atlas/screen-repository"><img src="https://agentmods.dev/badge/skills/neoneye/agent-memory-atlas/screen-repository.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 | $0.00054 | $0.02783 |
| Opus 5 | $0.00027 | $0.01392 |
| Sonnet 5 | $0.00011 | $0.00557 |
| Haiku 4.5 | $0.00005 | $0.00278 |
Grade C, and why
screen-repository scanned grade C with 2 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 10d 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
a shell (`curl … | sh`). Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
a shell (`curl … | sh`). How it starts
The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Screen Repository
Run this before add-memory-system and before reanalyze-memory-system, every
time, including on a repository already in the atlas. A re-read clones a newer
commit than the one that was screened, and the newest commit is exactly where a
compromise would arrive.
The threat model, stated plainly
This atlas is produced by cloning strangers' repositories onto a personal machine
and frequently running their build, their tests or their demo. The risk is not
what a project does to its own users. It is what a checkout does to this
laptop, between git clone and the first read.
Two shapes, failing differently:
Auto-executing configuration needs no command typed. An agent-harness hook
firing on session start or before a tool call; a devcontainer postCreateCommand;
a .envrc that direnv sources on cd; a VS Code task with runOn: folderOpen;
a .gitattributes smudge filter that runs during checkout itself. Opening or
entering the directory is enough.
Unpinned dependencies are the 2026 supply-chain shape. A manifest names a
range, a maintainer account is compromised, a new version publishes, and every
machine that installs afterwards executes the attacker's postinstall. The
compromise is not in the repository being read, and reading the repository will
never reveal it.
Pinning does not make a dependency safe. It makes it the same thing today as when someone last looked, which is the only property that can be checked from a clone.
Step 1 — clone without executing anything
git clone --no-checkout --recurse-submodules=no <url> <dir>
cd <dir> && git checkout <ref>
--recurse-submodules=no matters: a submodule pulls another untrusted tree, and
the screen below only sees the outer one. If .gitmodules exists, screen each
submodule separately or leave it uninitialised.
Do not cd into the directory in a shell where direnv is installed until
after step 2, and do not open it in an editor.
Step 2 — run the screen
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.
- 10d ago First seen · 233 lines · 54 tokens per session scan C d7947cf25783
screen-repository is a skill published in the GitHub repository neoneye/agent-memory-atlas (64 stars, last pushed today), licensed MIT. It adds 54 tokens to every session and 2,783 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
thoughtline-memory
Persistent, project-aware memory for AI coding agents. Use thoughtline whenever you make a decision, fix a bug, learn something non-obvious, or need to recall prior work.
Effective Memory
The essential habits for an AI agent with memory — session bookends, learning triggers, verification, safety, and the operational discipline that turns raw recall into compounding intelligence. Pinned, always-injected.
recall
Recall this repository's OwnMem local memory before changing code, and keep it healthy. Use when a repository contains .ownmem/, when past debugging lessons could apply ("have we hit this before", "why is it done this way"), or when the user mentions ownmem, project memory, or recalling across sessions.
init
Install or update OwnMem in the current repository. Use when the user asks to set up OwnMem, add local project memory for coding agents, or refresh an existing OwnMem installation after a version bump.
honcho-integration
Integrate Honcho memory into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, and accessing Honcho's representation.
honcho-memory
Concepts and strategy for using a connected Honcho as persistent memory of the user — the recall/record loop and session and peer design. Start here to understand how Honcho memory works, then connect — via a first-class integration for your environment if one exists (preferred), or raw MCP tools (covered here) or the…