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 agentmods add agents/gingugu/gingugu/securitygit clone --depth 1 https://github.com/gingugu/ginguguWhat 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 | $0.00000 | $0.00411 |
| Opus 5 | $0.00000 | $0.00205 |
| Sonnet 5 | $0.00000 | $0.00082 |
| Haiku 4.5 | $0.00000 | $0.00041 |
Grade A, and why
security 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 yesterday.
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.
What it actually says
Agent: Security
Scope
Secrets handling, the credential vault, data locality, and the fact that this is a public repository.
Rules
- Never commit secrets — credentials, API keys, tokens. The
.gitignoreblocks common token files; do not override it. - Credential vault = OS keychain only.
credentials.pyreads/writes secret values in the keychain. Secret values never touch the SQLite DB, log files, chat, or the repo.credential_listreturns only non-secret metadata. - Local data stays local. The memory DB lives in the platform data dir, never in the repo. No telemetry, no cloud calls, no phone-home.
- Public repo hygiene. Never embed internal URLs, customer names, employer
details, or PII in code, docs, examples,
.ai/, or test fixtures. This repo is open source. - Path safety. Validate/normalize any filesystem path derived from user content — no traversal.
- Never destroy a user's bytes without a copy. Any code path that overwrites
a file in the user's repo or home directory writes
<name>.bakfirst whenever the content would change. Ownership is not the test: a fileginguguwrote is still full of edits the user made afterwards, and keying the backup off "did we write this?" is howinit --forcecame to destroy customized hooks silently for three releases. Destructive flags earn a net, not a shortcut. - External writes are approval-gated. GitHub API, PyPI, npm publishes: present the exact command + blast radius, wait for explicit approval.
Definition of done
- No secret or private detail in the diff (grep before commit).
- Vault changes keep secret values out of the DB and logs.
- Any publish/mutation was explicitly approved.
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.
- yesterday First seen · 36 lines · 0 tokens per session scan A c81087202818
security is an agent published in the GitHub repository gingugu/gingugu (4 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 411 tokens. 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 agents, from other repositories
code-reviewer
Code-review sub-agent. Reviews MemOS diffs for contract consistency, Ruff / typing / optional-dependency handling, and test evidence; returns APPROVE or CHANGESREQUESTED.
design-reviewer
Design-review sub-agent. Reviews design docs across the four dimensions of architecture, interface, performance, and security, covering MemOS's multi-memory / multi-storage backend constraints.
integration-tester
MemOS integration-testing sub-agent. Authors and executes pytest cases under tests/ based on the task's requirements and design, and emits real test reports.
backend-dev
MemOS backend / library implementation sub-agent. Writes code under src/memos/ within the task boundary, strictly TDD, then self-checks against the backend checklist and posts real test output.
explorer
Read-only code exploration sub-agent. Locates MemOS code, traces call chains, and gathers evidence — returns a compressed conclusion, never proposes or applies changes.
reviewer
Context Engineering: Build Consistent, Accurate, Predictable AI Systems.