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.
git clone --depth 1 https://github.com/MnemeHQ/mnemeWrote 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/commands/mnemehq/mneme/check)<a href="https://agentmods.dev/commands/mnemehq/mneme/check"><img src="https://agentmods.dev/badge/commands/mnemehq/mneme/check/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/commands/mnemehq/mneme/check"><img src="https://agentmods.dev/badge/commands/mnemehq/mneme/check.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 | $0.00010 | $0.00206 |
| Opus 5.5 | $0.00004 | $0.00082 |
| Sonnet 5.5 | $0.00002 | $0.00041 |
| Haiku 4.5 | $0.00001 | $0.00021 |
Grade A, and why
check 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- mneme-check — 100% identical, 0 lines differ
What it actually says
Run mneme check against the current project memory.
If the user names a file, check that file's contents against
.mneme/project_memory.json. Otherwise, ask which file or scope.
Use the Bash tool:
mneme check \
--memory .mneme/project_memory.json \
--input <path> \
--query "<scope or task description>" \
--mode strict
Important: The --query argument controls which decisions are retrieved
for checking — it is not just a label. Use a descriptive phrase that matches
the scope of the change (e.g. "storage layer", "auth middleware") rather than
just the file name. Decisions are matched by keyword overlap between the query
and their scope, id, and text fields.
Report PASS / WARN / FAIL clearly. On FAIL, name the violated decision id and quote the anti-pattern or constraint that triggered it.
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 · 27 lines · 10 tokens per session scan A ea551c8b705f
check is a command published in the GitHub repository MnemeHQ/mneme (28 stars, last pushed today), licensed MIT. It adds 10 tokens to every session and 206 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 2026-08-30.
Other commands, from other repositories
learn
Extract patterns and learnings from current session.
rem_help
Each section is printed verbatim by --help for the command it names. The agreed design is issue #1656; change a page there and here together.
export-closedloop-learnings
Exports pending ClosedLoop learnings to global location with deduplication.
compact-prep
Ask the agent to prepare for conversation compaction by updating any relevant state and providing guidance for the compaction agent and to kick off the session there after.
speckit.archive
Archive a feature specification into main project memory after merge, resolving gaps and conflicts.
fire-reflect
After any failure (debug resolution, test failure, approach rotation, stalled loop), capture what was tried, why it failed, and what actually worked as a persistent reflection. Future sessions search these before investigating.