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/render-examples/nanobot-render/mynpx skills add render-examples/nanobot-render --skill mygit clone --depth 1 https://github.com/render-examples/nanobot-renderWhat 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 | $0.00093 | $0.00667 |
| Opus 5 | $0.00046 | $0.00333 |
| Sonnet 5 | $0.00019 | $0.00133 |
| Haiku 4.5 | $0.00009 | $0.00067 |
Grade A, and why
my 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-Awareness
How to use
- Identify the situation from the categories below
- Call the my tool with the appropriate action
- If set, warn the user before changing impactful settings (model, iterations)
- For detailed examples, read references/examples.md
When to check
When to set
| Situation | Command |
|---|---|
| Large codebase analysis | my(action="set", key="context_window_tokens", value=262144) |
| Switch to a named model preset | my(action="set", key="model_preset", value="<preset-name>") |
| Repetitive simple tasks without a preset | my(action="set", key="model", value="<fast-model>") |
| Long multi-step task | my(action="set", key="max_iterations", value=80) |
Tradeoff: Bias toward stability. Only set when defaults are genuinely insufficient.
Anti-patterns
Constraints
- All modifications in-memory only — restart resets everything
- Prefer
model_presetfor configured model choices. Directmodelchanges clear the active preset and should only be used when no preset exists. - Protected params have type/range validation:
max_iterations(1–100),context_window_tokens(4096–1M),model(non-empty str) - If
tools.my.allow_setis false, check only
Related tools
| Need | Use | Persists? |
|---|---|---|
| Per-session temp state | my(action="set", key="...", value=...) |
No |
| Long-term facts | Memory skill (MEMORY.md, USER.md) |
Yes |
| Permanent config change | Edit config file | Yes |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 75 lines · 93 tokens per session scan A a5482ae2ae81
my is a skill published in the GitHub repository render-examples/nanobot-render (5 stars, last pushed 1mo ago), licensed MIT. It adds 93 tokens to every session and 667 once invoked, about $0.0005 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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