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/dekan-aleksandr/biodiscovery-skills/discovery-toolboxnpx skills add dekan-aleksandr/biodiscovery-skills --skill discovery-toolboxgit clone --depth 1 https://github.com/dekan-aleksandr/biodiscovery-skillsWhat 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 | $0.00122 | $0.02983 |
| Opus 5 | $0.00061 | $0.01491 |
| Sonnet 5 | $0.00024 | $0.00597 |
| Haiku 4.5 | $0.00012 | $0.00298 |
Grade A, and why
discovery-toolbox 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.
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DISCOVERY TOOLBOX
Load on demand, never wholesale. Route to the smallest relevant section. Usually activate 1–3 operators, execute them, then return to the research loop.
The toolbox is a repertoire, not a checklist.
A · VISUAL REASONING
Trigger: shape, heterogeneity, ranking, trajectories, subsets, or individual observations may matter.
- RAW + EXTREMES — inspect representative raw units, argmax/argmin, and matched non-hits.
- RANK / SHAPE VIEW — inspect the full ordered profile or curve, not only thresholds or summary metrics.
- SMALL MULTIPLES / MULTISCALE — repeat the same view across subsets, clades, batches, quality strata, sources, time, and analysis scales.
- RESIDUAL / INFLUENCE VIEW — inspect residual structure and sensitivity to influential observations/groups.
- PREDICTED FIGURES — generate expected visual patterns under competing hypotheses before looking at decisive evidence.
- CALIBRATED VISUAL INFERENCE — important visual-only claim → declare the expected pattern, compare against matched nulls/line-ups, and calibrate the visual reader on pure nulls.
- DISPLAY AUDIT — if a conclusion depends on axes, aspect ratio, encoding, aggregation, or overplotting, compute it directly or re-render appropriately.
B · INSTRUMENT / DETECTABILITY / INFORMATION
Trigger: new screen, weak/null result, uncertain calibration, lossy representation, or multi-fidelity pipeline.
- PERMANENT CALIBRATION HARNESS — reusable screen → maintain
structure-preserving null + spike-in/recovery + empirical decoys/negative controls. - TARGET RELIABILITY — estimate target noise, cross-source agreement, test–retest/version agreement, and effective independent units.
- POINT → CURVE — replace a weak point estimate with signal vs
n / noise / quality / aggregation / threshold / scale. - EXISTENCE / INFORMATION BUDGET — estimate expected detectable hits and whether target information × independent units can support the search space; insufficient capacity → change instrument/design, not estimator.
- TOY TRUTH — reproduce a known answer on a toy slice and/or generative simulation before interpreting large-scale nulls.
- COMMON-CAUSE FIRST — before explaining individual hits, test screen-wide technical or biological causes.
- SIGNAL/NOISE AUTOPSY — assume signal exists; identify what makes it unreadable: target noise, heterogeneity, wrong unit, aggregation, alignment, measurement error, etc.
- DIFFUSE-SIGNAL SWITCH — if individual hits fail but the statistic distribution shifts, use whole-distribution/set-level modeling.
- INFORMATION-LOSS / RATE–DISTORTION AUDIT — vary compression, aggregation, or preprocessing and locate where target-relevant information disappears.
- DPI / PROVENANCE LEAKAGE CHECK — derived features cannot legitimately contain target information unavailable in their declared inputs; violations trigger leakage/provenance investigation.
- MULTI-FIDELITY RECALL — cheap filters must preserve top-k/rank behavior at the real operating threshold and in important subgroups; otherwise hedge pruning aggressiveness.
- UNDETERMINED ≠ NEGATIVE — pipeline failure, missingness, timeout, or non-convergence is a third outcome; track its structure.
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 · 182 lines · 122 tokens per session scan A 658324df1bc5
discovery-toolbox is a skill published in the GitHub repository dekan-aleksandr/biodiscovery-skills (11 stars, last pushed 23d ago), licensed MIT. It adds 122 tokens to every session and 2,983 once invoked, about $0.0006 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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