--- name: Discovery Toolbox slug: discovery-toolbox category: AI Engineering description: Discovery Toolbox routes biological research agents to small sets of operators for weak screens, anomalies, causal claims, and search stagnation. It helps choose the right reasoning move before returning to the research loop. github: "https://github.com/dekan-aleksandr/biodiscovery-skills/tree/main/skills/discovery-toolbox" language: Shell stars: 10 forks: 2 install: "npx degit https://github.com/dekan-aleksandr/biodiscovery-skills/tree/main/skills/discovery-toolbox ~/.claude/skills/discovery-toolbox" installs_to: ~/.claude/skills/discovery-toolbox source_path: skills/discovery-toolbox/SKILL.md collection_size: 2 category_size: 2451 collection_url: "https://dirskills.com/collections/dekan-aleksandr/biodiscovery-skills" added: 2026-08-12T04:42:36.707Z last_synced: 2026-08-12T04:42:36.707Z canonical_url: "https://dirskills.com/skills/discovery-toolbox" --- # Discovery Toolbox Discovery Toolbox routes biological research agents to small sets of operators for weak screens, anomalies, causal claims, and search stagnation. It helps choose the right reasoning move before returning to the research loop. **Install:** ```bash npx degit https://github.com/dekan-aleksandr/biodiscovery-skills/tree/main/skills/discovery-toolbox ~/.claude/skills/discovery-toolbox ``` ## README # 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. --- ## C · SEARCH / REFRAMING **Trigger:** ideas repeat, experiment space freezes, or marginal exploration yield falls. - **DUAL-SPACE SEARCH** — track `hypothesis space` and `experiment/measurement space` separately; change whichever is stagnant. - **LOCAL → STRUCTURAL ANALOGY** — search nearby mechanisms first, then search the relational skeleton of the problem across fields. - **INVERSE SCREEN** — construct the feature/profile that would explain the phenomenon, then search for nearest real examples. - **CHANGE THE UNIT** — move coarser/finer or to another causal/biological unit. - **DECOMPOSE / RECOMBINE** — split composite targets/features; localize the informative component. - **AUXILIARY TARGET** — solve a related quantity that can be measured better, then transfer back. - **EINSTELLUNG BREAK / RANDOM RESTART** — run a branch where the dominant approach is forbidden, or restart in another region of search space. - **BLIND / HIGH-UPSIDE VARIATION** — preserve some exploration not justified by the current model; require justification only before expensive escalation. - **SPRAY-AND-CUT** — lower per-attempt cost → buy more independent attempts rather than only deeper attempts. - **INTERMEDIATE MODEL CHAIN** — build a sequence of simplified models and treat the residual sequence as the discovery object. - **POSE THE NEXT QUESTION** — after each useful screen, ask what sharper question or measurement it newly makes possible. --- ## D · CAUSAL IDENTIFICATION **Trigger:** causal/mechanistic interpretation, comparative/natural experiment, or major covariate adjustment. - **IDENTIFICATION BEFORE ESTIMATOR** — state the causal quantity, ideal experiment, identifying variation, and unit of inference. No identifying variation → descriptive claim. - **EXOGENOUS-VARIATION SWEEP** — actively search thresholds, timing accidents, protocol/software changes, rollouts, natural discontinuities, independent transitions, and other quasi-experiments. - **DESIGNED CONTRAST** — remove confounding by design when possible: matched pairs, within-unit changes, independent origins, decoupled subsets, change points. - **BOUNDS FIRST** — weak identification → report what the data permit under minimal assumptions, then show what additional assumptions narrow. - **ONTOLOGY / TARGET POPULATION** — state which unit and population the estimate actually describes; do not silently generalize beyond the identifying contrast. - **PLACEBO / PRE-TREND BATTERY** — test outcomes, periods, populations, or thresholds the proposed mechanism should not affect. - **CONFOUNDER-STRENGTH SENSITIVITY** — quantify how strong an unmeasured confounder must be to overturn the result. - **SELECTION TRAJECTORY** — ask why units entered the exposed/selected group and whether they were already changing before selection. - **NUISANCE-MODEL DISAGREEMENT** — result depends on adjustment choice → identify and test the assumption causing the disagreement. - **COMPARISON-WEIGHT AUDIT** — pooled estimate → inspect which elementary comparisons actually contribute and with what weights. --- ## E · COMPETING EXPLANATIONS **Trigger:** a lead may deserve concentration of resources. - **ACH / NONE-OF-THE-ABOVE** — compare evidence across competing hypotheses; discard evidence compatible with all; retain an unconceived-alternative row. - **DIAGNOSTICITY GATE** — if possible test outcomes do not separate hypotheses, choose another test. - **DIFFERENTIAL MEASUREMENT** — find the measurement where the leading explanations predict different values. - **LOCARD TRACE** — each mechanism/artifact should leave collateral traces elsewhere; search for them. - **PAIRED ABLATIONS** — `destroy mechanism, preserve cue` + `destroy cue, preserve mechanism`. - **PERTURBATION / SPECIFICATION STABILITY** — vary defensible subsets, transformations, nuisance models, thresholds, seeds, and analysis choices; distinguish robustness distribution from knob attribution. - **ERROR BUDGET** — express plausible systematic errors in the effect's units. - **SOURCE-INDEPENDENCE CHECK** — apparently independent evidence sharing collection, ontology, preprocessing, or labels counts as less than independent. - **ELIMINATION LEDGER** — track `UNTOUCHED / ALIVE / WOUNDED / DEAD` and the observation that changed state. - **ALTERNATIVE / COLD DERIVATION** — reproduce representative results by a structurally different route, or with artifacts stripped of the original narrative. --- ## F · OBSERVATION / SELECTION PROCESS **Trigger:** filtering, missingness, uneven effort, curation, historical data, protocol drift, or provenance may shape the dataset. - **OBSERVATION-PROCESS AUDIT** — map `generation → measurement → collection → inclusion → QC → preprocessing → loaded data`; record what each stage removes and what removal correlates with. - **ABSENT / UNDETECTED / REMOVED** — distinguish these explicitly; recover upstream QC/filter information when needed. - **CHAIN OF CUSTODY** — trace important derived variables/results back to raw measurements; broken provenance is a leakage/contamination alarm. - **COVERAGE-STANDARDIZED COMPARISON** — unequal measurement/observation effort → compare equivalent coverage/information, not merely equal row count. - **ACTUALISTIC CALIBRATION** — pass known/synthetic material through the real inclusion/QC/join pipeline and measure what survives. - **PALIMPSEST / ERA DETECTION** — detect hidden protocol, schema, curator, or collection-era strata from missingness/provenance patterns. - **PRESERVATION-PROXY CHECK** — test whether completeness, observation effort, metadata richness, collection date, or similar archive variables explain the result. - **SAMPLING-INDUCED GRADIENT CHECK** — apparent gradual onset/decay or endpoints may arise from incomplete sampling; simulate abrupt truth through the observed sampling process. --- ## G · PIPELINE / ARTIFACT DIAGNOSIS **Trigger:** spectacular result, unexplained change, replication failure, or suspicious software/data behavior. - **CHECK THE PLUG** — files, row alignment, labels, units, joins, versions, executed code, and outputs. - **DETERMINISTIC MINIMAL REPRODUCER** — fix randomness and reduce to the smallest input that reproduces the effect. - **PIPELINE BISECTION** — inject known-good signal and bisect where it is lost/corrupted. - **INVARIANTS + LOUD ASSERTIONS** — specify what each transformation must preserve and fail loudly when violated. - **COUNT TWO WAYS** — derive headline quantities through another implementation/path. - **FAULT TREE + FAULT INJECTION** — enumerate plausible failures, deliberately inject them, and identify silent holes. - **REINTRODUCE / SAME-HOLE SWEEP** — restored bug should restore artifact; then inspect all prior results exposed to the same failure mode. - **BISECT HISTORY** — binary-search changes when behavior shifted. - **INHERITED-CONSTANT / VERSION AUDIT** — unexpected yield → inspect mappings, annotations, external constants, resource versions, and producer semantics. - **TIME-REVERSE THE ANALYST** — trace backward from claim through every decision capable of changing it. --- ## H · ALLOCATION / DEPTH **Trigger:** deciding breadth vs depth or where to spend scarce resources. - **BINDING-RESOURCE COST** — price actions by what actually limits progress: attention, samples, experimental latency, compute, or wall time. - **CAPABILITY-CALIBRATED COST** — do not import human labor estimates; prototype when uncertain and measure actual agent/tool cost. - **OUTSIDE VIEW + EXPECTED YIELD** — use previous run/base-rate performance before detailed inside-view forecasting. - **INFORMATION-GAIN ORDERING** — prefer tests that most efficiently partition the live hypothesis space. - **MARGINAL-YIELD SWITCH** — continue an arm while its expected yield beats a fresh start. - **DEPTH SWITCH** — a few leads dominate on `upside × evidence × discriminability` → stop widening and invest heavily. - **ADVENTUROUS TAIL** — preserve capacity for coherent high-upside/high-variance research. - **MARGINAL VALUE / REDUNDANCY** — value additions relative to capabilities already present, not in isolation. --- ## I · CONFIRMATION / HYGIENE **Trigger:** a lead may become a scientific claim, or infrastructure is being reused long-term. - **INDEPENDENT CONFIRMATION** — prefer a genuinely independent data-generating process; freeze the claim/analysis first. - **CONFIRMATION DIVERSITY** — spend limited confirmation resources across different mechanisms/explanations, not redundant top ranks. - **INVARIANCE / ORTHOGONAL CONSEQUENCE** — test properties predicted by the mechanism that leading artifacts have little reason to reproduce. - **TRANSFER TEST** — check structure in a genuinely different regime, cohort, clade, source, or measurement system. - **WINNER'S-CURSE AWARENESS** — discovery effect size is not the expected confirmation effect. - **SUCCESS POST-MORTEM** — ask whether diagnostics would have caught an artifact version of a successful result. - **ASSUMPTION REGISTER** — track consequential `chosen/inherited × tested/untested` assumptions. - **OMITTED / FILTERED / EXCLUDED** — important removals must remain recoverable and auditable. - **PROGRESSIVE vs DEGENERATING** — prefer modifications that predict something new over those that only rescue the existing claim. --- ## ROUTER ``` visual structure → A calibration / weak signal / information / filters → B search stagnation / reframing → C causal interpretation → D competing explanations → E selection / missingness / provenance → F pipeline suspicion → G allocation / depth → H confirmation / assumptions → I ``` **Default: activate 1–3 operators.** More only when independently useful. The toolbox exists to expose **non-obvious moves** the agent might otherwise miss. Standard methods need not be restated unless a specific construction changes the reasoning.