AI Agent Workflow Collection

Use when writing recommendation letters, reference letters, or award nominations for students, postdocs, or colleagues. Invoke when user mentions recommendation letter, reference, nomination, letter of support, endorsement, or needs help with strong advocacy, comparative statements, or evidence-based character assessment.

Use when documenting significant technical or architectural decisions that need context, rationale, and consequences recorded. Invoke when choosing between technology options, making infrastructure decisions, establishing standards, migrating systems, or when team needs to understand why a decision was made. Use when user mentions ADR, architecture decision, technical decision record, or decision documentation.

Applies Bayesian reasoning to systematically update probability estimates with new evidence, helping make better forecasts and avoid overconfidence. Use when making predictions or judgments under uncertainty, forecasting outcomes, evaluating probabilities, testing hypotheses, calibrating confidence, assessing risks with uncertain data, or when user mentions priors, likelihoods, Bayes theorem, probability updates, forecasting, calibration, or belief revision.

Use when you need to generate many creative options before systematically narrowing to the best choices. Invoke when exploring product ideas, solving open-ended problems, generating strategic alternatives, developing research questions, designing experiments, or when you need both breadth (many ideas) and rigor (principled selection). Use when user mentions brainstorming, ideation, divergent thinking, generating options, or evaluating alternatives.

Use when writing or reviewing career documents including research statements, teaching statements, diversity statements, CVs, or biosketches. Invoke when user mentions research statement, teaching philosophy, diversity statement, biosketch, academic CV, faculty application, or needs help with career narrative, positioning, or professional documents for academic advancement.

Systematically investigates causal relationships to identify true root causes rather than correlations or symptoms. Distinguishes genuine causation from spurious associations, tests competing explanations, and designs interventions addressing underlying drivers. Use when investigating why something happened, debugging systems, analyzing failures, evaluating policy impacts, or when user mentions root cause, causal chain, confounding, spurious correlation, or asks "why did this really happen?

Use when making high-stakes decisions under uncertainty that require stakeholder buy-in. Invoke when evaluating strategic options (build vs buy, market entry, resource allocation), quantifying tradeoffs with uncertain outcomes, justifying investments with expected value analysis, pitching recommendations to decision-makers, or creating business cases with cost-benefit estimates. Use when user mentions "should we", "ROI analysis", "make a case for", "evaluate options", "expected value", "justify decision", or needs to combine estimation, decision analysis, and persuasive communication.

Use when facing decisions with multiple legitimate perspectives and inherent tensions. Invoke when stakeholders have competing priorities (growth vs. sustainability, speed vs. quality, innovation vs. risk), need to pressure-test ideas from different angles before committing, exploring tradeoffs between incompatible values, synthesizing conflicting expert opinions into coherent strategy, or surfacing assumptions that single-viewpoint analysis would miss.

Use when planning high-stakes initiatives (migrations, launches, strategic changes) that require clear specifications, proactive risk identification (premortem/register), and measurable success criteria. Invoke when user mentions "plan this migration", "launch strategy", "implementation roadmap", "what could go wrong", "how do we measure success", or when high-impact decisions need comprehensive planning with risk mitigation and instrumentation.

Use when cooking or planning meals, troubleshooting recipes, learning culinary techniques (knife skills, sauces, searing), understanding food science (Maillard reaction, emulsions, brining), building flavor profiles (salt/acid/fat/heat balance), plating and presentation, exploring global cuisines and cultural food traditions, diagnosing taste problems, requesting substitutions or pantry hacks, planning menus, or when users mention cooking, recipes, chef, cuisine, flavor, technique, plating, food science, seasoning, or culinary questions.

Extracts atomic technical claims from a substacker essay draft, converting flowing intuition-first prose into a numbered list where each item is a statement that could in principle be verified or falsified. Skips non-technical sections (personal anecdote, motivation, call-to-action). Use when the Technical Reviewer starts a per-draft review. Trigger keywords: extract claims, atomic claims, technical claim list, fact-check prep.

Assigns each extracted claim to one of five buckets — simplified-correct, simplified-boundary, wrong, contested, overclaim — with low/medium/high confidence and one-sentence rationale. Classification happens before primary-source verification (which confirms, not invents). Use for every claim from claim-extractor. Trigger keywords: classify, bucket, simplified vs wrong, claim type, technical classification.

Use when starting technical work requiring structured approach - writing tests before code (TDD), planning data exploration (EDA), designing statistical analysis, clarifying modeling objectives (causal vs predictive), or validating results. Invoke when user mentions "write tests for", "explore this dataset", "analyze", "model", "validate", or when technical work needs systematic scaffolding before execution.

Use when designing visual interfaces, data visualizations, educational content, or presentations and need to ensure they align with how humans naturally perceive, process, and remember information. Invoke when user mentions cognitive load, visual hierarchy, dashboard design, form design, e-learning, infographics, or wants to improve clarity and reduce user confusion. Also applies when evaluating existing designs for cognitive alignment or choosing between design alternatives.

Detects and prevents visual misleads, cognitive biases, and data integrity violations in visualizations, dashboards, reports, and presentations. Audits charts for honesty, diagnoses misinterpretation causes, and provides specific fixes. Invoke when user mentions chartjunk, misleading chart, truncated axis, data integrity, visual deception, 3D chart problems, cherry-picking data, or needs to audit visualizations for accuracy. For general design evaluation, use design-evaluation-audit. For cognitive foundations, use cognitive-design.

Use when transforming analysis/data into persuasive narratives—presenting to executives, explaining technical concepts to non-technical audiences, creating customer-facing communications, writing investor updates, announcing changes, turning research into insights, or when user mentions "write this for", "explain to", "present findings", "make this compelling", "audience is". Invoke when information needs to become a story tailored to specific stakeholders.

Build a personalized preference profile from a small number of well-chosen, cluster-grounded questions instead of a long survey. Represents the person's interests as an uncertainty region over the theme map, picks the single highest-information-gain choice-based question (contrasting real talks from different clusters), balances exploiting known interests against exploring uncertain ones, deliberately injects outlier probes to fight selection bias, and stops as soon as the schedule would be stable. Also elicits the user-owned objective weights and hard constraints. Interactive — runs where it can actually ask the person. Conference-agnostic. Use to turn a theme map into a preference profile, to decide what to ask a conference attendee, or to elicit scheduling priorities. Trigger keywords - preference elicitation, ask few questions, information gain, choice-based questions, selection bias probe, objective weights, attendee preferences.

Turns limitations into creative fuel by strategically imposing constraints that force novel thinking, break habitual patterns, and reveal unexpected solutions. Covers resource constraints (budget/time/tools), format constraints (length/medium), rule-based constraints, and perspective constraints. Use when brainstorming feels stuck or generates obvious ideas, working with limited resources, designing with specific limitations, or when user mentions "think outside the box", "we're stuck", "same old ideas", "tight constraints", or "limited budget/time".

Finds and cites a primary source for each substacker draft claim — original arXiv paper, official documentation, RFC, canonical textbook. Source hierarchy enforced: primary > secondary > tertiary > not-a-source. Records URL, title, passage/result that settles the claim. Use after classify-claim; runs once per claim unless classification is simplified-correct with high confidence on standard undergrad material. Trigger keywords: cross-reference, primary source, citation, arXiv, RFC, paper lookup, source hierarchy.

Use when designing database schemas, need to model domain entities and relationships clearly, building knowledge graphs or ontologies, creating API data models, defining system boundaries and invariants, migrating between data models, establishing taxonomies or hierarchies, user mentions "schema", "data model", "entities", "relationships", "ontology", "knowledge graph", or when scattered/inconsistent data structures need formalization.

Use when dealing with complex systems that need simplification, identifying bottlenecks or critical failure points, redesigning architecture or processes for better performance, breaking down problems that feel overwhelming, analyzing dependencies to understand ripple effects, user mentions "this is too complex", "where's the bottleneck", "how do we redesign this", "what are the key components", or when optimization requires understanding how parts interact.

Use when testing plans or decisions for blind spots, need adversarial review before launch, validating strategy against worst-case scenarios, building consensus through structured debate, identifying attack vectors or vulnerabilities, user mentions "play devil's advocate", "what could go wrong", "challenge our assumptions", "stress test this", "red team", or when groupthink or confirmation bias may be hiding risks.

Generates structured experimental designs (factorial, response surface, Taguchi) to systematically discover how multiple factors affect outcomes while minimizing experimental runs. Use when optimizing multi-factor systems with limited experimental budget, screening many variables to find the vital few, discovering interactions between parameters, mapping response surfaces for peak performance, validating robustness to noise factors, or when users mention factorial designs, A/B/n testing, parameter tuning, or process optimization.

Applies thesis-antithesis-synthesis reasoning to escape false binary choices by steelmanning opposing positions, mapping their underlying principles and tradeoffs, and synthesizing principled third-way resolutions. Use when debates are trapped in false dichotomies, polarized positions need charitable interpretation, tradeoffs are obscured by binary framing, synthesis beyond "pick one side" is needed, or when users mention steelman arguments, Hegelian dialectic, or resolving seemingly opposed principles.