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 Aiyo28/memento-os --skill memento-lintgit clone --depth 1 https://github.com/Aiyo28/memento-osWrote 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/aiyo28/memento-os/memento-lint)<a href="https://agentmods.dev/skills/aiyo28/memento-os/memento-lint"><img src="https://agentmods.dev/badge/skills/aiyo28/memento-os/memento-lint/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/aiyo28/memento-os/memento-lint"><img src="https://agentmods.dev/badge/skills/aiyo28/memento-os/memento-lint.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 | $0.00100 | $0.00614 |
| Opus 5 | $0.00050 | $0.00307 |
| Sonnet 5 | $0.00020 | $0.00123 |
| Haiku 4.5 | $0.00010 | $0.00061 |
Grade A, and why
lint 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.
What it actually says
/lint
Enforce the reasoning-artifact schema across every _context.md in a vault. Prose templates can't do this — that's the point.
What gets checked
| Rule | Applies to | Required text (case-insensitive) |
|---|---|---|
| R1 | [D], [I] |
invalidates if / invalidates when / dies if |
| R2 | [S] |
Activation: / activates when (use --strict for literal Activation:) |
| R3 | [E] |
fix: |
| R4 | all | # column non-empty |
| R5 | all | priority column non-empty |
| R6 | all | row contains YYYY-MM-DD date |
| R7 | warning | malformed table row (unbalanced backticks, <4 cells); promoted to violation under --strict |
How to invoke
python3 .agents/skills/lint/lint.py [VAULT_ROOT] [--strict] [--ci] [--format json] [--quiet]
Vault root resolution: argument → $MEMENTO_VAULT_ROOT → CWD.
| Flag | Effect |
|---|---|
--strict |
Requires literal Activation: for [S]; promotes R7 warnings to violations |
--ci |
Sugar for --strict --quiet |
--format json |
Machine-readable for hooks/CI |
--quiet |
Suppress per-file OK lines |
| Exit | Meaning |
|---|---|
| 0 | clean |
| 1 | violations found |
| 2 | usage / IO error |
When the agent invokes this
- User says "lint", "validate", "check artifacts".
- Run the script via shell.
- For each violation, ask the user the missing field, then edit the artifact row in place. Re-run lint to confirm clean.
Don't
- Don't auto-fix without user confirmation.
- Don't widen the rules. Adding
"maybe invalidates if"defeats the purpose. - Don't run
--stricton legacy vaults without warning the user — many existing[S]entries useactivates when.
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 · 62 lines · 100 tokens per session scan A 88184d7bc968
lint is a skill published in the GitHub repository Aiyo28/memento-os (4 stars, last pushed 6d ago), licensed MIT. It adds 100 tokens to every session and 614 once invoked, about $0.0005 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
impeccable
Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states.…
tree-ring-memory
Guides AI agents in using Tree Ring Memory for durable recall, project decisions, user preferences, warnings, future seeds, privacy-safe memory capture, and lifecycle-aware forgetting.
memory-to-skill
Turn workflows from your MemSearch memory into reusable skills. Use when the user asks to make/create/extract/distill a skill from what they just did or from past work, review skill candidates, install a distilled skill, or 'turn this into a skill'. Manages MemSearch procedural-memory candidates under…
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.
mnemo-cortex
Installs and wires Mnemo Cortex (local-first persistent memory) into OpenClaw and other MCP-capable agents. Use for cross-session recall, decision history, or multi-agent shared memory.
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.