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 cdeust/zetetic-team-subagents --skill structure-discoverygit clone --depth 1 https://github.com/cdeust/zetetic-team-subagentsWrote 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/cdeust/zetetic-team-subagents/structure-discovery)<a href="https://agentmods.dev/skills/cdeust/zetetic-team-subagents/structure-discovery"><img src="https://agentmods.dev/badge/skills/cdeust/zetetic-team-subagents/structure-discovery/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/cdeust/zetetic-team-subagents/structure-discovery"><img src="https://agentmods.dev/badge/skills/cdeust/zetetic-team-subagents/structure-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00065 | $0.00762 |
| Opus 5 | $0.00032 | $0.00381 |
| Sonnet 5 | $0.00013 | $0.00152 |
| Haiku 4.5 | $0.00006 | $0.00076 |
Grade A, and why
structure-discovery 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 9d 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Structure Discovery
Problem shape: many observations, one suspected hidden regularity — an unnamed axis, an unexploited symmetry, an undeciphered format, a topology forced by constraints. The move: make the structure explicit and falsifiable, then let it predict something you haven't seen yet.
Relevant geniuses
| Agent | Use when |
|---|---|
| mendeleev | many known items with a suspected hidden ordering — tabulate, leave explicit gaps, predict the gaps' properties |
| noether | hidden regularity via invariance — find the symmetry group, quotient the search space, treat symmetry breaking as signal |
| kekule | components with known connection constraints, unknown structure — count the bonds and let the count force the shape |
| vonneumann | the problem looks isomorphic to a solved one in another field — find the mapping, import the solution |
| rejewski | black-box system to reconstruct from outputs — structural invariants, exploit the procedure around the algorithm |
| champollion | undeciphered format with a partial parallel (bilingual) sample — anchor known fragments and propagate |
| ventris | no parallel text exists — grid the internal regularities, then test by prediction |
| poincare | qualitative behavior before quantitative solution — topological equivalence, structural stability |
| mandelbrot | pattern repeats across scales; distribution has fat tails being treated as mild randomness |
| ramanujan | need many candidate patterns fast — compute 50+ special cases, conjecture, then mandatory prover handoff (never ship unverified) |
Invocation
- Pick the best-fit agent above. If two or more fit, run
tools/genius-invoker.sh route "<problem>"and take the top ranked match. - Load it:
tools/genius-invoker.sh invoke <agent> "<problem>", then readagents/genius/<agent>.mdin full. - Apply the agent's
<workflow>step by step and answer in its<output-format>. A discovered structure must predict something checkable — a gap's properties, a next case, an invariant — or it is decoration. - ramanujan output is conjecture by definition: chain a prover
(
tools/genius-invoker.sh compose ramanujan lamport -- "<problem>"or hand to dijkstra) before anything ships. - If no shape above matches, use a standard team agent instead.
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.
- 9d ago First seen · 52 lines · 65 tokens per session scan A f00621aa5731
structure-discovery is a skill published in the GitHub repository cdeust/zetetic-team-subagents (7 stars, last pushed today), licensed MIT. It adds 65 tokens to every session and 762 once invoked, about $0.0003 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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