Discovery Toolbox

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: dekan-aleksandr
• Source: dekan-aleksandr/biodiscovery-skills
• License: MIT

Install and usage instructions live in the source repository linked above.