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/patterngit 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 | $0.00018 | $0.00402 |
| Opus 5 | $0.00009 | $0.00201 |
| Sonnet 5 | $0.00004 | $0.00080 |
| Haiku 4.5 | $0.00002 | $0.00040 |
Grade A, and why
pattern 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.
What it actually says
Output language
Read INTERFACE_LANG from .course-meta (default en). All user-facing prose must be in that language. Keep in English regardless: file paths, slash command names, pattern IDs (P1, P2…), and the pattern-card field labels (Recognition:, Move:, Seen in:, Topic:).
Read course-index/patterns.md. If missing, tell the user to run /analyze first.
Query: $ARGUMENTS
Procedure:
-
Filter patterns:
- If query starts with
§orChor similar: return patterns whose Topic field includes that section - If query matches
P\d+(e.g.,P7): return that single pattern plus cross-references - If query is a keyword (e.g.,
maxwell,fourier,induction): return patterns matching name/recognition/move text (case-insensitive) - If query is
allor empty: return the full list, grouped by part/topic
- If query starts with
-
For each matching pattern, render as a compact card:
[Pk] <name> Recognition: <signal> Move: <operation, 1-2 lines> Seen in: <problem IDs> Topic: <§ numbers> -
End with a prompt (in $INTERFACE_LANG): "Any pattern here that would be hard to recognize at first sight? Tell me its number — we'll drill just that one with
/blind <problem>."
Keep total output under 40 lines. This is a recognition tool, not a tutorial. If the user wants depth on one pattern, they'll ask.
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 · 36 lines · 18 tokens per session scan A 7d5b5092f7c5
pattern is a command published in the GitHub repository OPTIMETA/PAIDEIA (91 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 402 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 2026-08-30.
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