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/mneme-review)<a href="https://agentmods.dev/commands/mnemehq/mneme/mneme-review"><img src="https://agentmods.dev/badge/commands/mnemehq/mneme/mneme-review.svg" alt="Measured on agentmods" 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 | $0.00007 | $0.00250 |
| Opus 5 | $0.00003 | $0.00125 |
| Sonnet 5 | $0.00001 | $0.00050 |
| Haiku 4.5 | $0.00001 | $0.00025 |
Grade A, and why
mneme-review 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 8d 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.
This is a copy
100% identical to review — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Run git diff to capture pending changes, then for each modified file
check the new content against project memory.
Steps:
- Use Bash:
git diff --name-onlyto list modified files. - For each modified file, use Bash:
git show :0:<file>or read the file directly to get the current content. - Run
mneme checkfor each file, using a descriptive--querythat names the file's domain (not just its path):
mneme check \
--memory .mneme/project_memory.json \
--input <file> \
--query "<domain description for this file>" \
--mode strict
- Aggregate verdicts and report:
- Files that PASS
- Files with violations — include decision id and triggered rule
Note on retrieval: Use a meaningful --query per file (e.g. "storage
layer" for db.py, "auth middleware" for auth.py). The file path alone
may not retrieve all relevant decisions — describe the domain of the file,
not just its name.
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.
- 8d ago First seen · 31 lines · 7 tokens per session scan A d592bc45f827
mneme-review is a command published in the GitHub repository MnemeHQ/mneme (20 stars, last pushed yesterday), licensed MIT. It adds 7 tokens to every session and 250 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to review, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
sdd-apply
Implement SDD tasks — writes code following specs and design.
gentle-sdd-ff
Fast-forward all SDD planning phases — proposal through tasks.
sdd-explore
Explore and investigate an idea or feature — reads codebase and compares approaches.
memory-why
Show why a memory recall returned what it did -- BM25 vs vector vs hybrid provenance.
export-closedloop-learnings
Exports pending ClosedLoop learnings to global location with deduplication.
review-branch
Review the current branch's diff against base by dispatching atomic-reviewer. No orchestration loop, no spec required — pre-flight before /commit pr or /commit merge.