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 commands/mnemehq/mneme/mneme-contextgit 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-context)<a href="https://agentmods.dev/commands/mnemehq/mneme/mneme-context"><img src="https://agentmods.dev/badge/commands/mnemehq/mneme/mneme-context.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 | $0.00010 | $0.00141 |
| Opus 5 | $0.00005 | $0.00071 |
| Sonnet 5 | $0.00002 | $0.00028 |
| Haiku 4.5 | $0.00001 | $0.00014 |
Grade A, and why
mneme-context 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 4d 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 context — 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
Use the Bash tool to run:
mneme test_query \
--memory .mneme/project_memory.json \
--query "<user's task description>"
Surface the top decisions and their constraints so the user can see what governs the current work before making edits.
Tip: Use a descriptive query that names the domain of the work (e.g. "database storage layer", "authentication middleware", "API serialization"). Retrieval is keyword-based — the more specific the query, the more relevant the decisions returned. A generic query like "edit" will retrieve few or no decisions.
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.
- 4d ago First seen · 20 lines · 10 tokens per session scan A 038ab0b0c9e0
mneme-context is a command published in the GitHub repository MnemeHQ/mneme (20 stars, last pushed today), licensed MIT. It adds 10 tokens to every session and 141 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to context, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
mempalace-status
Show the current state of your memory palace — wings, rooms, drawer counts, and suggestions.
ingest-l1
L1 analysis loop for the abapwiki knowledge base: for each batch it launches the abap-analyzer sub-agent in parallel, then the adversarial judge abap-deepcheck (separate session), applies only the analyses that pass the fail-closed gate, and commits. Resumes exactly after an interruption. Use this skill to document…
bootstrap-memory
RUN { git ls-files; git ls-files --others --exclude-standard; } 2>/dev/null | sort -u | xargs wc -l 2>/dev/null | sort -rn | head -150 READ README.md.
projects
列出已注册的项目及其直觉（instinct）计数.
context
上下文管理，加载项目信息（会话级，另见 /cc-best:memory 管理持久记忆）.
read-my-mind
Run the user's ToM model in prediction mode (feedback precognition). Read the avatar files in and follow START.md.