# Teammately Docs

Public developer documentation with first-class context surfaces for coding agents.

## Pages
- [Admin Console](https://teammately.ai/docs/admin-console.md): Understand the organization-level administration surfaces available at admin.teammately.ai.
- [Agent context index](https://teammately.ai/docs/agent-context.md): Use Teammately docs safely from AI agents, retrieval tools, and coding assistants.
- [Agent instructions](https://teammately.ai/docs/agent-instructions.md): Rules coding agents should follow when using Teammately public docs as source context.
- [Agent Setup](https://teammately.ai/docs/agent-setup.md): Configure Project Context and Reference Materials so Teammately agents have reusable project understanding before contribution work.
- [Project Context](https://teammately.ai/docs/agent-setup/project-context.md): Maintain the Project Agent Brief that gives Teammately agents stable, project-wide understanding.
- [Reference Materials](https://teammately.ai/docs/agent-setup/reference-materials.md): Connect project knowledge, inspect indexing state, and verify the blocks available to Teammately agents.
- [Assets](https://teammately.ai/docs/assets.md): Manage reusable project cases, worlds, project tools, harnesses, weights, comparison directions, and review screens before selecting them for benchmark work.
- [Cases](https://teammately.ai/docs/assets/cases.md): Understand canonical project cases, their input and materials, and how they become members of benchmark datasets.
- [Comparison Directions](https://teammately.ai/docs/assets/comparison-directions.md): Create reusable guidance for meaningful candidate-output differences in comparative expert work.
- [Harnesses](https://teammately.ai/docs/assets/harnesses.md): Build versioned Python Harness bundles, validate and debug Drafts, publish exact Versions, and activate them for benchmark evaluations.
- [Project Tools](https://teammately.ai/docs/assets/project-tools.md): Understand the current Project Tools asset surface and its deliberately limited public behavior.
- [Review Screens](https://teammately.ai/docs/assets/review-screens.md): Configure reusable project templates for the context and presentation experts see during Contribution work.
- [Weights](https://teammately.ai/docs/assets/weights.md): Understand the current Weights asset surface and the absence of a public model-weight lifecycle.
- [Worlds](https://teammately.ai/docs/assets/worlds.md): Understand the current Worlds asset surface without inferring an environment lifecycle that the product does not expose.
- [Benchmark Datasets](https://teammately.ai/docs/benchmark-datasets.md): Select benchmark Cases, inspect representation, and freeze immutable Snapshots for reproducible evidence.
- [Benchmark Dataset Cases](https://teammately.ai/docs/benchmark-datasets/cases.md): Inspect benchmark Case membership, coverage traces, references, and scoped bulk actions.
- [Dataset Representation](https://teammately.ai/docs/benchmark-datasets/representation.md): Analyze how distinct benchmark Cases are distributed across facets, evaluator rules, and provenance.
- [Dataset Snapshots](https://teammately.ai/docs/benchmark-datasets/snapshots.md): Freeze Cases, evaluator links, and representation facts as an immutable benchmark evidence boundary.
- [Benchmark Evaluations](https://teammately.ai/docs/benchmark-evaluations.md): Run and inspect exact Harness Versions against an immutable Benchmark Version through Dashboard, List, Arena, and Compare.
- [Arena and Rankings](https://teammately.ai/docs/benchmark-evaluations/arena-and-rankings.md): Interpret pairwise candidate disagreement, governed metric families, repeated-sampling ranks, and uncertainty.
- [Compare Harness Versions](https://teammately.ai/docs/benchmark-evaluations/compare.md): Compare two or more saved Harness Versions in a symmetric evidence matrix across Cases, evaluators, and Coverage Facets.
- [Evaluation Execution Settings](https://teammately.ai/docs/benchmark-evaluations/execution-settings.md): Configure machines and choose saved Harness Versions and Run counts at launch.
- [Inspect Evaluation Results](https://teammately.ai/docs/benchmark-evaluations/inspect-results.md): Trace Dashboard and List signals to Run, Case, Policy, Rubric, completeness, and telemetry evidence.
- [Map External Evaluation Outputs](https://teammately.ai/docs/benchmark-evaluations/output-mapping.md): Import reference outputs, map them to immutable benchmark Cases and attempts, and inspect the resulting output-only Run.
- [Run a Benchmark Evaluation](https://teammately.ai/docs/benchmark-evaluations/run-evaluation.md): Launch exact active Harness Versions against an immutable Benchmark Version as standard or repeated Run Groups.
- [Benchmark Run Metadata](https://teammately.ai/docs/benchmark-evaluations/run-metadata.md): Interpret benchmark-level descriptive fields without confusing them with project settings or version identity.
- [Benchmarks and versions](https://teammately.ai/docs/concepts/benchmarks-and-versions.md): Understand benchmarks, benchmark versions, snapshots, and why versioned measurement matters.
- [Coevolve](https://teammately.ai/docs/concepts/coevolve.md): Explore multiple evidence-backed candidate directions while keeping goals, benchmark receipts, trajectories, and the current frontier connected.
- [Correctness Elicitation](https://teammately.ai/docs/concepts/correctness-elicitation.md): Turn tacit specialist judgment into attributable contributions, governed policies, applicability conditions, and binary rubrics.
- [Dimensions and ontology](https://teammately.ai/docs/concepts/dimensions-and-ontology.md): Learn how dimensions and ontology values define the coverage space for a Teammately project.
- [Policies and rubrics](https://teammately.ai/docs/concepts/policies-and-rubrics.md): Learn how Teammately turns product judgment into reusable policies and scoring rubrics.
- [Trialground](https://teammately.ai/docs/concepts/trialground.md): Evaluate exact Harness and Benchmark Versions in a managed proving ground with inspectable responses and Rubric evidence.
- [Weave](https://teammately.ai/docs/concepts/weave.md): Construct deliberate challenge sets from coverage structure, canonical cases, variants, multimodal materials, and supported worlds.
- [Workspaces and projects](https://teammately.ai/docs/concepts/workspaces-projects.md): Learn how Teammately organizes teams, projects, product goals, and access boundaries.
- [Correctness Governance](https://teammately.ai/docs/correctness-governance.md): Govern project policies and rubrics, their applicability, linked cases, approval state, and contribution provenance.
- [Write Binary Rubrics](https://teammately.ai/docs/correctness-governance/binary-rubrics.md): Write atomic pass-or-fail criteria grounded in governed policies, applicable cases, and observable candidate behavior.
- [Policies and Rubrics](https://teammately.ai/docs/correctness-governance/policies-and-rubrics.md): Understand the governed relationship between behavior policies, applicability, binary rubrics, linked cases, and expert provenance.
- [Coverage Engineering](https://teammately.ai/docs/coverage-engineering.md): Design the behavior space a benchmark must represent and connect reusable project facets to benchmark coverage work.
- [Benchmark snapshots](https://teammately.ai/docs/coverage-engineering/benchmark-snapshots.md): Freeze a benchmark into a version so every run measures the same cases and judgment rules.
- [Benchmarks](https://teammately.ai/docs/coverage-engineering/benchmarks.md): Create and manage benchmark sets that measure important AI product behavior.
- [Boundary Cases](https://teammately.ai/docs/coverage-engineering/boundary-cases.md): Use edge and ambiguous cases to sharpen policies, applicability logic, and rubrics.
- [Candidate and In-Use Cases](https://teammately.ai/docs/coverage-engineering/candidate-and-in-use-cases.md): Distinguish examples under consideration from cases that actively support benchmark evidence.
- [Case Construction Patterns](https://teammately.ai/docs/coverage-engineering/case-construction-patterns.md): Define reusable mechanisms for constructing cases and steer how benchmarks use or avoid them.
- [Case Pool](https://teammately.ai/docs/coverage-engineering/case-pool.md): Use the Case Pool to collect, triage, enrich, and promote candidate cases.
- [Case Segmentation](https://teammately.ai/docs/coverage-engineering/case-segmentation.md): Segment cases into meaningful behavior groups so benchmark coverage is explainable.
- [Coverage Gaps](https://teammately.ai/docs/coverage-engineering/coverage-gaps.md): Find missing or underrepresented behavior areas before benchmark evidence becomes misleading.
- [Refresh coverage after product change](https://teammately.ai/docs/coverage-engineering/coverage-refresh.md): Reconcile coverage facets, Cases, benchmark membership, and Snapshots after the target system or its evidence changes.
- [Create a benchmark](https://teammately.ai/docs/coverage-engineering/create-a-benchmark.md): Create the durable Benchmark workspace in which you will define coverage, select Cases, and create reproducible Snapshots.
- [Dimensions and Ontology](https://teammately.ai/docs/coverage-engineering/dimensions-ontology.md): Define reusable behavior axes and their allowed values, then inspect how cases and benchmarks cover them.
- [Generate a dimension schema](https://teammately.ai/docs/coverage-engineering/generate-dimension-schema.md): Ask Teammately for coverage-dimension proposals, then accept only the dimensions and ontology values that describe meaningful behavior.
- [Plan Benchmark Coverage](https://teammately.ai/docs/coverage-engineering/plan-benchmark-coverage.md): Apply project Coverage Facets to one benchmark, inspect representation, and turn important gaps into concrete case or contribution work.
- [Project Topics](https://teammately.ai/docs/coverage-engineering/project-topics.md): Maintain source-grounded subject areas, editable groups, and Atlas relationships used to organize project and benchmark coverage.
- [Synthesize cases](https://teammately.ai/docs/coverage-engineering/synthesize-cases.md): Generate candidate cases to fill coverage gaps before adding them to benchmarks or curated datasets.
- [Coverage Management](https://teammately.ai/docs/coverage-management.md): Manage benchmark coverage from setup through representation, Coverage Stories, case review, Case Foundry, and contribution requests.
- [Case Foundry](https://teammately.ai/docs/coverage-management/case-foundry.md): Generate or update bounded case candidates from the saved coverage setup and Story map.
- [Case Review](https://teammately.ai/docs/coverage-management/case-review.md): Prepare, inspect, refine, and admit Case candidates and generated materials into the benchmark dataset.
- [Coverage Stories](https://teammately.ai/docs/coverage-management/coverage-stories.md): Organize benchmark coverage intent into governed Stories and testable facet tuples.
- [Set Up Benchmark Coverage](https://teammately.ai/docs/coverage-management/get-started.md): Define benchmark intent, facet handling, artifact preferences, and evidence requirements before generating coverage work.
- [Expert Contributions](https://teammately.ai/docs/expert-contributions.md): Coordinate benchmark-scoped expert work, attributable judgment, governed artifacts, and the decisions that move correctness forward.
- [Complete an Expert Contribution](https://teammately.ai/docs/expert-contributions/complete-contribution.md): Work through form, chat, interview, case-review, and checkpoint tasks while keeping specialist judgment attributable.
- [Contributed Artifacts](https://teammately.ai/docs/expert-contributions/contributed-artifacts.md): Inspect policies, rubrics, cases, and coverage observations produced through attributable expert contribution work.
- [Contribution Lifecycle and Status](https://teammately.ai/docs/expert-contributions/lifecycle-and-status.md): Interpret Contribution, activity, task, expert-runtime, and checkpoint states without inferring completion.
- [Logs & Status](https://teammately.ai/docs/expert-contributions/logs-and-status.md): Inspect contribution reviews, sessions, interviews, engagement, and operational records within the benchmark scope.
- [Request an Expert Contribution](https://teammately.ai/docs/expert-contributions/request-contribution.md): Create a focused benchmark contribution with an accountable expert, clear objectives, selected cases, attachments, and appropriate task components.
- [Key objects and relationships](https://teammately.ai/docs/getting-oriented/key-objects-and-relationships.md): Understand how project foundations, contributions, datasets, evaluations, and improvement artifacts connect.
- [Operating Teammately end to end](https://teammately.ai/docs/getting-oriented/operating-teammately-end-to-end.md): Operate the current product from project foundations through benchmark coverage, expert contribution, evaluation, and improvement.
- [Product map](https://teammately.ai/docs/getting-oriented/product-map.md): Navigate Teammately across workspace entry points, project foundations, benchmark workspaces, expert contribution UI, and administration.
- [User roles](https://teammately.ai/docs/getting-oriented/user-roles.md): Understand the responsibilities of project operators, domain experts, AI engineers, accountable owners, and organization administrators.
- [Governance Overview](https://teammately.ai/docs/governance.md): Govern correctness standards, expert decisions, versions, and evidence without overstating unsupported enterprise claims.
- [Approval History and Reviewer Activity](https://teammately.ai/docs/governance/approval-history-and-reviewer-activity.md): Understand how approvals and reviewer actions support explainable correctness decisions.
- [Benchmark Versioning](https://teammately.ai/docs/governance/benchmark-versioning.md): Preserve benchmark snapshots so evidence can be compared across target and standard changes.
- [Case Versioning](https://teammately.ai/docs/governance/case-versioning.md): Track meaningful changes to cases, metadata, context, and attached outputs.
- [Resolve conflicting correctness evidence](https://teammately.ai/docs/governance/conflict-resolution.md): Reconcile disagreement without hiding the Cases, expert judgments, sources, or versions that produced it.
- [Human Approval Boundaries](https://teammately.ai/docs/governance/human-approval-boundaries.md): Define which AI-assisted suggestions require accountable human review before becoming standards.
- [Policy Versioning](https://teammately.ai/docs/governance/policy-versioning.md): Preserve policy changes so decisions can be interpreted against the standard used at the time.
- [Reproducibility](https://teammately.ai/docs/governance/reproducibility.md): Preserve enough source context to explain and repeat correctness decisions.
- [Reviewer and Project Access](https://teammately.ai/docs/governance/reviewer-and-project-access.md): Route reviewers and project participants to the work they are qualified to inspect or approve.
- [Roles and Permissions](https://teammately.ai/docs/governance/roles-and-permissions.md): Use roles and permissions to route ownership, review, and approval work clearly.
- [Rubric Versioning](https://teammately.ai/docs/governance/rubric-versioning.md): Track changes to pass/fail criteria and the evidence they produce.
- [Detect and route stale correctness evidence](https://teammately.ai/docs/governance/staleness-detection.md): Identify which current claims need review after Cases, standards, coverage, sources, or target behavior change.
- [Versioning and Staleness](https://teammately.ai/docs/governance/versioning-and-staleness.md): Know when correctness objects changed and when old evidence may need review.
- [What AI Features Can and Cannot Do](https://teammately.ai/docs/governance/what-ai-features-can-and-cannot-do.md): Explain the difference between AI-assisted suggestions and approved correctness infrastructure.
- [Workspace Administration](https://teammately.ai/docs/governance/workspace-administration.md): Administer workspace and project boundaries so correctness work has clear ownership and scope.
- [Improve](https://teammately.ai/docs/improve.md): Coordinate durable Improvement Sessions from pinned benchmark evidence through Goal Contracts, candidates, evaluations, trajectories, and frontiers.
- [Candidates and the Current Frontier](https://teammately.ai/docs/improve/candidates-and-frontier.md): Interpret candidate stages, canonical receipts, constraints, retained Candidate Systems, and the current frontier.
- [Chronology, Trajectories, and Receipts](https://teammately.ai/docs/improve/chronology-and-trajectories.md): Read durable session events, safe narrated work segments, evaluation receipts, external handoffs, and usage evidence.
- [Goal Contracts](https://teammately.ai/docs/improve/goal-contracts.md): Bind an Improvement Session to exact targets, measurable objectives, constraints, and permitted intervention scope.
- [Start an Improvement Session](https://teammately.ai/docs/improve/start-improvement-session.md): Start from benchmark evidence, prepare a measurable Goal Contract, and choose bounded Work or Evolve behavior.
- [Work and Evolve](https://teammately.ai/docs/improve/work-and-evolve.md): Choose bounded implementation work or multi-branch evolutionary search with explicit epoch and provider authorization.
- [Integrations](https://teammately.ai/docs/integrations.md): Move Cases, source materials, external outputs, and benchmark evidence across Teammately's supported product boundaries.
- [Check candidate correctness](https://teammately.ai/docs/integrations/check-candidate-correctness.md): Use Teammately benchmark evidence to compare a candidate behavior change against a baseline before internal human review.
- [Connect Model Outputs](https://teammately.ai/docs/integrations/connect-model-outputs.md): Map externally produced outputs to immutable Benchmark Cases and create an output-only reference Run.
- [Import Case Examples](https://teammately.ai/docs/integrations/import-case-examples.md): Bring real product behavior examples into a Project and reconcile them against Project Input Schema before benchmark use.
- [Expert UI](https://teammately.ai/docs/integrations/reviewer-workspace.md): Understand how experts receive assigned work and how expert-facing tasks, interviews, and checkpoints fit into Teammately.
- [What is correctness infrastructure?](https://teammately.ai/docs/introduction/correctness-infrastructure.md): Learn how five connected capabilities turn specialist judgment into cases, executable standards, evaluation evidence, and improvement.
- [The Teammately correctness lifecycle](https://teammately.ai/docs/introduction/correctness-lifecycle.md): Follow specialist AI work from project foundations through coverage, elicitation, construction, evaluation, and improvement.
- [Product boundaries](https://teammately.ai/docs/introduction/product-boundaries.md): Understand what Teammately owns across correctness specification, benchmark development, evaluation, and improvement.
- [What is Teammately?](https://teammately.ai/docs/introduction/what-is-teammately.md): Understand Teammately as correctness infrastructure for building trustworthy specialist AI with expert judgment and AI agents.
- [Object model](https://teammately.ai/docs/object-model.md): Understand how project foundations, benchmark artifacts, contributions, evaluations, and improvement sessions connect.
- [Applicability logic](https://teammately.ai/docs/object-model/applicability-logic.md): Explain when a policy or rubric should be used for a case, output, or coverage segment.
- [Benchmarks](https://teammately.ai/docs/object-model/benchmarks.md): Understand a Benchmark as the durable program that owns benchmark-scoped coverage, evidence boundaries, evaluations, and improvement work.
- [Case Pool surface](https://teammately.ai/docs/object-model/case-pool.md): Understand Case Pool as the Assets view for reusable project Cases, candidates, and explicit Benchmark selection.
- [Cases](https://teammately.ai/docs/object-model/cases.md): Understand cases as the situations Teammately uses to represent important AI behavior.
- [Coverage dimensions](https://teammately.ai/docs/object-model/coverage-dimensions.md): Organize cases by the behavior axes that matter to product correctness.
- [Ontology](https://teammately.ai/docs/object-model/ontology.md): Use ontology values to classify cases consistently within each coverage dimension.
- [Outputs](https://teammately.ai/docs/object-model/outputs.md): Distinguish managed Run responses, imported output-only Runs, and Case-scoped reference outputs.
- [Policies](https://teammately.ai/docs/object-model/policies.md): Define policies as reusable statements of what correct AI behavior requires.
- [Reference and golden outputs](https://teammately.ai/docs/object-model/reference-and-golden-outputs.md): Understand Case-scoped example responses without treating them as generic approval state or candidate behavior.
- [Represent conversations in Cases](https://teammately.ai/docs/object-model/represent-conversations-in-cases.md): Preserve multi-message context inside canonical Case input without inventing a separate Conversation Case object.
- [Rubrics](https://teammately.ai/docs/object-model/rubrics.md): Turn policies into binary, reviewable checks that explain pass and fail evidence.
- [Versions, staleness, and resolution](https://teammately.ai/docs/object-model/versions-staleness-and-resolution.md): Track how correctness objects evolve and how teams resolve conflicting evidence.
- [Workspaces, projects, and target systems](https://teammately.ai/docs/object-model/workspaces-projects-and-target-systems.md): Model organizational boundaries, product boundaries, and the AI system being governed.
- [Build policies and rubrics](https://teammately.ai/docs/operating-manual/build-policies-and-rubrics.md): Materialize expert-grounded behavior rules, applicability, and binary criteria in Correctness Governance.
- [First correctness loop](https://teammately.ai/docs/operating-manual/first-correctness-loop.md): Complete one traceable path from project context and benchmark coverage to expert judgment, evaluation evidence, and improvement.
- [Import and prepare cases](https://teammately.ai/docs/operating-manual/import-and-prepare-cases.md): Bring cases into the project, conform them to Project Input Schema, inspect materials, and prepare benchmark selection.
- [Prepare human review context](https://teammately.ai/docs/operating-manual/prepare-review-packet.md): Assemble customer-owned review context from exact evaluation, contribution, coverage, and improvement evidence.
- [Task index](https://teammately.ai/docs/operating-manual/task-index.md): Route correctness work to the current project foundation, benchmark workspace, evaluation, or improvement surface.
- [Enterprise Playbooks](https://teammately.ai/docs/playbooks.md): Apply Teammately’s correctness lifecycle to common AI product operating scenarios.
- [Building a Correctness Benchmark for a RAG System](https://teammately.ai/docs/playbooks/building-correctness-benchmark-rag.md): Represent retrieval-grounded behavior through cases, context, policies, rubrics, and benchmark evidence.
- [Using Teammately for Customer Support AI](https://teammately.ai/docs/playbooks/customer-support-ai.md): Govern assistant behavior where correctness depends on policy, escalation, tone, and account context.
- [Using Teammately for Enterprise Search](https://teammately.ai/docs/playbooks/enterprise-search.md): Build correctness standards for search and answer systems that must handle context, intent, and authority.
- [Finding Coverage Gaps Before Review](https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review.md): Use coverage dimensions, failures, and expert signals to decide where evidence is incomplete.
- [Handling Conflicting Expert Opinions](https://teammately.ai/docs/playbooks/handling-conflicting-expert-opinions.md): Turn expert disagreement into sharper standards instead of unresolved review noise.
- [Using Teammately for Policy-Heavy AI Systems](https://teammately.ai/docs/playbooks/policy-heavy-ai-systems.md): Govern AI behavior where correctness depends on many explicit rules and boundary cases.
- [Refreshing a Benchmark from New Signals](https://teammately.ai/docs/playbooks/refreshing-a-benchmark-from-new-signals.md): Update benchmark coverage and standards when new cases, review findings, or product changes appear.
- [Running Expert Contributions for an Enterprise Assistant](https://teammately.ai/docs/playbooks/running-expert-contributions-enterprise-assistant.md): Collect expert judgment for assistants that must follow product, domain, and policy expectations.
- [Turning Expert Judgment into Policies and Rubrics](https://teammately.ai/docs/playbooks/turning-expert-judgment-into-policies-and-rubrics.md): Convert specialist judgment into explicit, versioned, and testable correctness standards.
- [Using Teammately Alongside Existing Evaluation Infrastructure](https://teammately.ai/docs/playbooks/using-teammately-alongside-existing-evaluation-infrastructure.md): Position Teammately as correctness infrastructure that can complement existing tests and metrics.
- [The Teammately correctness loop](https://teammately.ai/docs/product-loop.md): See how coverage, elicitation, case construction, evaluation, and improvement reinforce one another.
- [Project Settings](https://teammately.ai/docs/project-settings.md): Configure the project identity, evaluation Regime, case input contract, and project members.
- [General Project Settings](https://teammately.ai/docs/project-settings/general.md): Manage the project name and Project Memo without confusing descriptive metadata with agent context.
- [Project Input Schema](https://teammately.ai/docs/project-settings/input-schema.md): Define the canonical input architecture, case-material fields, and accepted artifact formats for project cases.
- [Project Members](https://teammately.ai/docs/project-settings/project-members.md): Grant or remove user and group access to a project while keeping task assignment and artifact governance separate.
- [Regime Settings](https://teammately.ai/docs/project-settings/regime.md): Inspect and publish the project scoring Regime that governs future Benchmark Versions.
- [Product quickstart](https://teammately.ai/docs/quickstart.md): Configure one project foundation, one benchmark slice, one expert contribution, one evaluation, and one evidence-backed improvement.
- [Reference library](https://teammately.ai/docs/reference.md): Look up Teammately objects, states, permissions, metadata, schemas, and source-confidence boundaries.
- [Glossary](https://teammately.ai/docs/reference/glossary.md): Definitions for current Teammately capabilities, product surfaces, artifacts, and lifecycle states.
- [IDs and keys](https://teammately.ai/docs/reference/ids.md): Understand the identifiers used across Teammately projects, records, benchmarks, runs, policies, and rubrics.
- [Metadata and context](https://teammately.ai/docs/reference/metadata-and-context.md): Use record context and metadata fields to make Teammately cases easier to filter, review, and analyze.
- [Permissions](https://teammately.ai/docs/reference/permissions.md): Understand the user-facing permission boundaries for projects, reviewers, settings, and expert UI access.
- [Troubleshooting](https://teammately.ai/docs/troubleshooting.md): Diagnose common Teammately setup, upload, review, classification, and benchmark issues.
- [IP access restriction](https://teammately.ai/docs/troubleshooting/authentication.md): Resolve the current Access Restricted page when the detected IP is not on the Workspace allowlist.
- [Benchmark Results Changed Unexpectedly](https://teammately.ai/docs/troubleshooting/benchmark-results-changed-unexpectedly.md): Diagnose result changes across target behavior, benchmark cases, standards, and versions.
- [Benchmark run troubleshooting](https://teammately.ai/docs/troubleshooting/benchmark-runs.md): Diagnose a Run that cannot start, has no usable outputs, or produces results that cannot be compared safely.
- [Case upload troubleshooting](https://teammately.ai/docs/troubleshooting/dataset-upload.md): Repair uploads with rejected rows, missing inputs, incorrect column mapping, or Cases that arrive without usable context.
- [Dimension classification troubleshooting](https://teammately.ai/docs/troubleshooting/dimension-classification.md): Resolve missing, ambiguous, or inconsistent Case classifications before they distort coverage analysis.
- [Expert Contribution problems](https://teammately.ai/docs/troubleshooting/expert-contributions.md): Diagnose contribution access, task routing, checkpoint, synchronization, completion, and artifact-reconciliation problems.
- [Low expert agreement](https://teammately.ai/docs/troubleshooting/low-expert-agreement.md): Investigate disagreement as evidence about context, applicability, source authority, or unresolved product policy.
- [Missing Outputs](https://teammately.ai/docs/troubleshooting/missing-outputs.md): Separate a missing managed Run response from an unmatched row in an imported output-only Run.
- [Noisy AI Suggestions](https://teammately.ai/docs/troubleshooting/noisy-ai-suggestions.md): Triage generated suggestions that are fluent but not ready for the artifact they affect.
- [Output mapping troubleshooting](https://teammately.ai/docs/troubleshooting/output-mapping.md): Fix external output rows that do not join cleanly to immutable Case IDs in an output-only Run.
- [Overlapping Rubrics](https://teammately.ai/docs/troubleshooting/overlapping-rubrics.md): Resolve rubrics that ask the same question or create contradictory evidence.
- [Overly Broad Policies](https://teammately.ai/docs/troubleshooting/overly-broad-policies.md): Narrow policies that are too vague to guide review, rubrics, or human reviews.
- [Permissions troubleshooting](https://teammately.ai/docs/troubleshooting/permissions.md): Separate Project membership from Expert Contribution assignment and readiness when an expert has no tasks available.
- [Stale Dimensions](https://teammately.ai/docs/troubleshooting/stale-dimensions.md): Refresh Dimensions and ontology values that no longer explain the current behavior space without rewriting historical evidence.
- [Unbalanced Coverage](https://teammately.ai/docs/troubleshooting/unbalanced-coverage.md): Fix benchmarks that overrepresent easy or common cases while missing important behavior.
- [Unclear Cases](https://teammately.ai/docs/troubleshooting/unclear-cases.md): Repair Cases that informed reviewers cannot interpret consistently from the supplied input, context, and output identity.
- [Synthetic Cases That Feel Unrealistic](https://teammately.ai/docs/troubleshooting/unrealistic-synthetic-cases.md): Improve generated candidates whose surface details or behavior assumptions do not represent plausible target-system use.
- [Weak Applicability Logic](https://teammately.ai/docs/troubleshooting/weak-applicability-logic.md): Fix standards that are applied to the wrong cases or skipped where they matter.