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/optimeta/paideia/derivegit clone --depth 1 https://github.com/OPTIMETA/PAIDEIAWhat 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 | $0.00034 | $0.00632 |
| Opus 5 | $0.00017 | $0.00316 |
| Sonnet 5 | $0.00007 | $0.00126 |
| Haiku 4.5 | $0.00003 | $0.00063 |
Grade A, and why
derive 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Output language
Read INTERFACE_LANG from .course-meta (default en). All user-facing prose — chat output and narrative sections of the generated derivation MD — must be in that language. Keep in English regardless: file paths, slash command names, pattern IDs, LaTeX, and any literal section anchors downstream tools regex on.
Load skills/course-builder/SKILL.md for material locations. Also read course-index/summary.md to resolve the target.
Target: $ARGUMENTS
Procedure:
- Locate the derivation in
converted/textbook/*.mdandconverted/lectures/*.md. If present in both, prefer the textbook (usually cleaner). - If not in materials, derive it from first principles using standard techniques for the course's domain. Cite which earlier results you're using.
- Format as a clean reference markdown file with:
- Starting definitions/assumptions clearly stated
- Each step with a one-line explanation of why
- Boxed final result
- Short physical / mathematical interpretation at the end
- Typical pitfalls (common student errors) listed at bottom
- Save to
derivations/<slug>.md. Slug is lowercase-hyphenated from the target name. - Print (in $INTERFACE_LANG): "Saved
derivations/<slug>.md. Open and read; ask if any step is unclear."
Do NOT quiz or prompt the user — this command is a pure reference-writer. The user explicitly set this up so they can read rather than type.
Format convention (align with existing derivations/ files if any)
The skeleton below uses English labels; if INTERFACE_LANG=ko, translate the bold labels ("Goal", "Starting point", "Step 1 — ...", "Result", "Interpretation", "Pitfalls", "Reference") to natural Korean equivalents. Keep LaTeX, file paths, and equation content unchanged.
# <Target name>
**Goal.** <statement of what we want to derive>
**Starting point.** <definition / law / axiom / earlier result>
---
### Step 1 — <step description>
$$<step equation>$$
<why this step>
### Step 2 — ...
...
---
**Result.**
$$\boxed{\;<final>\;}$$
**Interpretation.** <1-2 sentences on what this means physically/mathematically>
**Pitfalls.**
- <common error 1>
- <common error 2>
**Reference.** <source section in converted/>
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 · 67 lines · 34 tokens per session scan A 39e7cf5d32fd
derive is a command published in the GitHub repository OPTIMETA/PAIDEIA (91 stars, last pushed 1mo ago), licensed MIT. It adds 34 tokens to every session and 632 once invoked, about $0.0002 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 2026-08-30.
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