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 skills/expandingideas-ai/mnemo-mcp/memory-commitnpx skills add expandingideas-ai/Mnemo-MCP --skill memory-commitgit clone --depth 1 https://github.com/expandingideas-ai/Mnemo-MCPWhat 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 | $0.00072 | $0.01065 |
| Opus 5 | $0.00036 | $0.00532 |
| Sonnet 5 | $0.00014 | $0.00213 |
| Haiku 4.5 | $0.00007 | $0.00106 |
Grade A, and why
memory-commit 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.
This is a copy
100% identical to memory-commit — 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.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Commit
Manual capture of a single decision, preference, fact, skill, task, or
conversation snippet into mnemo. Enforces the "WHY not just WHAT" rule
and chooses the correct context_type so retrieval is precise.
When to Use
Trigger on explicit user signals:
- "remember this", "save this", "save for next time"
- "ghi nho", "luu lai", "nho lai"
- "let's commit this to memory", "store this"
- After a deliberate decision: "we picked X because Y"
- After a stated preference: "I always want X"
- After correcting the agent: high-value capture (prevents repeat)
Do NOT trigger on incidental mentions or speculative options not yet chosen.
Steps
-
Identify the content the user wants to remember:
- Preceding 1-3 messages (default), OR
- A specific quoted span the user references, OR
- The current selection if invoked via slash command
-
Determine
context_typevia this decision tree:Signal context_type "we decided", "we picked X over Y", "going with" decision"I prefer", "I always want", "default to" preference"X is at version Y", env vars, API shapes, file locations fact"to do X you run Y then Z", procedure, how-to skill"todo", "remember to", deadline, "by Friday" tasknone of the above (general context) conversationWhen ambiguous, ask the user one short question. Do not silently default to
conversationfor high-signal content. -
Compose the capture text with WHY included:
- BAD: "Use Polars"
- GOOD: "Use Polars for dataframes (not pandas) because the codebase processes 50M-row datasets and Polars is 20x faster than pandas in our benchmark."
-
Capture via the typed action:
memory(action="capture", text="<composed text>", context_type="<chosen type>", category="<project-name or topic>", tags=["<topic>", "<scope>"]) -
Confirm to the user:
Saved as <context_type>. Memory ID: <id>. (Deduplicated against existing similar memory: <existing_id>.) # if applicable
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 · 112 lines · 72 tokens per session scan A 241884a0c81c
memory-commit is a skill published in the GitHub repository expandingideas-ai/Mnemo-MCP (0 stars, last pushed 2mo ago), licensed MIT. It adds 72 tokens to every session and 1,065 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to memory-commit, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…