Correctness and legibility checklist for data/results figures in any field: charts, parameter scans, spectra, distributions, fit comparisons, constraint contours, heatmaps, and low-dimensional projection scatters. Use when an agent plots computed or measured data, revises a results figure for a manuscript, or audits whether a data figure is publication-ready. Covers data fidelity (excluded rows never enter plotted summaries, connected-series evaluator fingerprints are homogeneous, a rendered continuous field is continuity-scanned before delivery, claim-titles true against every axis category, one canonical value per claim), label economy floor and ceiling, colour threading with a CVD-safe palette, role-mapped typography, chart choice by data shape, a render-then-verify QA loop (bbox overlap check plus per-panel perceptual crops), a figure-reproduction provenance bundle, and a fail-closed display-acceptance gate that refuses durable or outward-facing use until every plotted quantity is bound to a verification-
Use when writing Julia numerical or scientific code, to apply always-on performance guardrails, gate an accelerated implementation as an identity against its verbatim reference over the full domain of intended use, and escalate to reproducible benchmark gating for speedup or regression claims. Works standalone by default and can optionally emit ecosystem artifacts.
Convergence and reliability gate for NUMERICAL results in any field, including fits, optimizations, integrals, eigenvalues, roots, poles, zeros, ODE/PDE solutions, Monte-Carlo estimates, and downstream feature extraction. Use before trusting, comparing, publishing, or folding a computed number into durable research artifacts. Requires resolution convergence, independent-method checks, regression anchors, post-correction downstream recomputation, method-precondition checks, configuration-threading audits, gate-discrimination (negative-control) audits of purpose-built validation chains, accelerated/heuristic fast-path scoping (no false guarantee, unconditional escape hatch, production-validated precondition), and honest uncertainty/reporting. Emits an auditable reliability matrix. Sibling to derivation-verify for symbolic claims and julia-perf for speed claims.
AI failure-mode checklist (M1-M8) for research agents. Generic across domains. Walk M1-M7 at the moments work becomes durable — you just finished a derivation or computation and are about to write conclusions, fold a number or claim into the plan or contract, check off a task-board item, make a milestone closeout commit, hand off, assemble submission material, or claim a result is final — and, in projects using the engine's approval flow, before requesting an A1-A5 approval gate. Walk M8 before starting each heavy or production computation or hand-building a reusable computational method.
Use when a research milestone (theory or computation) needs executed, reproducible work whose results are independently reviewed before being trusted. Milestone-based research-team workflow for theory+computation projects with reproducible artifacts, independent parallel workstreams (default: host-native subagents; configurable), and a strict convergence gate.
Use when a result, manuscript, derivation, or diff needs independent review. Runs clean-room multi-agent review loops across Claude/Gemini/Codex/OpenCode/Kimi with strict review-contract checks, fallback policy, and convergence gates.
Turn a research idea into an auditable posterior: run an admission gate before any graph is built, decompose the idea into five source-grounded sub-criteria, encode them as a Gaia argument graph (gaia-lang, pinned 0.5.0a4), run exact inference, and write the posterior back to the idea store. Enforces a parameter honesty discipline — three fixed likelihood grades on the Jeffreys evidence scale, a mandatory anchor note on every number, MaxEnt fallback when an anchor fails review — plus pairwise expansion for mutual exclusivity, tournament-result absorption, revival by appending evidence, and review that audits anchors rather than scores. Use when admitting an idea into a tracked portfolio, when new evidence (literature, trial computation, tournament result) should move an idea's posterior, or when auditing why a posterior is what it is.
Turn a shallow, metadata-only literature pull into a DEEP review — multi-hop discovery via the existing literature-workflows recipes, per-paper deep-read notes that fill the research-team KB note template from the actual source (with verbatim quotes + locators), an optional double-reader mode for load-bearing papers (two independent readers from different model families plus a moderator that keeps disagreements visible), cross-paper synthesis (consensus / tensions / gaps), correct Markdown math rendering, and a checkable literature_survey_v1 artifact. Run when a survey feels thin, before promoting an idea, or before writing a related-work / introduction section.