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/blake-simpson/belmont/notenpx skills add blake-simpson/belmont --skill notegit clone --depth 1 https://github.com/blake-simpson/belmontWhat 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 | $0.00014 | $0.00852 |
| Opus 5 | $0.00007 | $0.00426 |
| Sonnet 5 | $0.00003 | $0.00170 |
| Haiku 4.5 | $0.00001 | $0.00085 |
Grade A, and why
note 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Belmont: Note
You are saving learnings and discoveries to NOTES.md so they persist across sessions and context compactions. This skill captures non-obvious information — environment quirks, workarounds, debugging insights, credential locations — that would otherwise be lost.
Storage Locations
- Feature-level:
{base}/NOTES.md— learnings specific to the current feature - Global:
.belmont/NOTES.md— cross-cutting learnings (env setup, project-wide patterns)
Where {base} is the feature's base path (e.g., .belmont/features/<slug>/).
Step 1: Determine Target
Decide where to save the note:
- If the user specifies "global" or the note is about project-wide setup/environment, use
.belmont/NOTES.md - If the user specifies a feature or you're clearly working in a feature context, use
{base}/NOTES.md - If unclear, ask the user:
Where should this note be saved?
[1] Feature: <feature-name> ({base}/NOTES.md)
[2] Global (.belmont/NOTES.md)
Step 2: Extract Learnings
Gather the content to save:
- If the user provided specific text, use that
- If the user says "save what we learned" or similar, extract non-obvious discoveries from the conversation:
- Environment requirements or quirks
- Workarounds for bugs or limitations
- Debugging insights that took effort to discover
- Credential or config file locations (NEVER save actual secret values)
- Performance findings
- Non-obvious patterns or conventions
Do NOT save:
- Routine task completion notes (that's what PROGRESS.md is for)
- Obvious information that's in the docs
- Actual secret values, tokens, passwords, or API keys — only save their locations
Step 3: Confirm with User
Show the user what will be saved:
Saving to: {target file path}
### {Category}
- {learning 1}
- {learning 2}
Save this? [y/n]
Wait for confirmation before writing.
Step 4: Write to NOTES.md
- If the file doesn't exist: Create it with the header and today's entry:
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.
- 2d ago First seen · 110 lines · 14 tokens per session scan A 808af7037b89
note is a skill published in the GitHub repository blake-simpson/belmont (4 stars, last pushed 14d ago), licensed Apache-2.0. It adds 14 tokens to every session and 852 once invoked, about $0.0001 per session on Opus 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.
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