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 n24q02m/mnemo-mcp --skill memory-commitgit clone --depth 1 https://github.com/n24q02m/mnemo-mcpWrote 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/n24q02m/mnemo-mcp/memory-commit)<a href="https://agentmods.dev/skills/n24q02m/mnemo-mcp/memory-commit"><img src="https://agentmods.dev/badge/skills/n24q02m/mnemo-mcp/memory-commit/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/n24q02m/mnemo-mcp/memory-commit"><img src="https://agentmods.dev/badge/skills/n24q02m/mnemo-mcp/memory-commit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00072 | $0.01065 |
| Opus 5.5 | $0.00029 | $0.00426 |
| Sonnet 5.5 | $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 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
2 near-identical copies found in the catalogue:
- memory-commit — 100% identical, 0 lines differ
- memory-commit — 100% identical, 0 lines differ
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.
- 1mo ago First seen · 112 lines · 72 tokens per session scan A 241884a0c81c
memory-commit is a skill published in the GitHub repository n24q02m/mnemo-mcp (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 72 tokens to every session and 1,065 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
scrape-batch
Extract many known URLs in one polite, rate-limited pass. Use when the user hands over a list of links, a set of search hits to read in full, or asks to "scrape these pages" / "pull the content from all of them". Drives extract(action="batch"), which fans out with per-domain rate limiting and returns partial results…
compare
Structured comparison of 2+ alternatives with consistent criteria and decision matrix.
research-topic
Multi-step research orchestration. Use when user asks "research X", "summarize current state of Y", "what's the latest on Z", or compares approaches. Calls extract(action="agent") which searches the web, extracts top results, then synthesises a citation-preserving Markdown answer with one configured LLM.
metagit-context-pack
Token-efficient workspace onboarding via tiered context packs — map, repo cards, session digest, objectives, approvals, repomix profiles. Use at session start or when scoping work in a metagit-managed workspace.
fact-check
Verify a claim using adversarial search — find both supporting AND contradicting evidence.
lock-project-stack
Detect a project's manifest (pyproject.toml / package.json / go.mod / Cargo.toml), pin its library set into wet-mcp's Cabinets projectcontext, then route subsequent docs queries to the locked versions automatically.