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Dépôt GitHub

RewardHarness

RewardHarness contient 76 skills collectées depuis TIGER-AI-Lab, avec une couverture métier par dépôt et des pages de détail sur le site.

skills collectés
76
Stars
14
mis à jour
2026-05-16
Forks
1
Couverture métier
15 catégories métier · 100% classifié
explorateur de dépôts

Skills dans ce dépôt

instruction-following-completeness
Scientifiques des données

Score completeness of instruction execution. Partial edits get partial credit; missed sub-steps cost points.

2026-05-16
realism-and-artifact-penalties
Scientifiques des données

Guidance on penalizing artifacts while allowing conceptual unrealism if requested by the prompt.

2026-05-16
text-and-ocr-analyzer
Scientifiques des données

Extracts and analyzes text within images to verify spelling, placement, and clarity.

2026-05-16
evaluating-background-changes
Scientifiques des données

Guidelines for evaluating background replacements and scene changes.

2026-05-14
evaluating-multi-step-edits
Scientifiques des données

Guidelines for evaluating prompts with multiple distinct instructions.

2026-05-14
evaluating-object-replacements
Acheteurs en gros et au détail (sauf produits agricoles)

Guidelines for evaluating object replacements and modifications.

2026-05-14
evaluating-style-transfers
Scientifiques des données

Guidelines for evaluating artistic style transfers.

2026-05-14
evaluating-text-rendering
Graphistes

Guidelines for evaluating text additions and modifications in images.

2026-05-14
compare-images
Scientifiques des données

Compare a source image and edited images to identify differences, check if instructions were followed, and assess visual quality.

2026-05-14
artifact-and-structure-preservation
Scientifiques des données

Rules for prioritizing image integrity and penalizing severe artifacts or loss of original structure.

2026-05-14
artistic-and-cultural-references
Directeurs artistiques

Guidelines for evaluating prompts that reference specific artists, art styles, or cultural phenomena.

2026-05-14
embrace-intended-drastic-changes
Scientifiques des données

Rules for evaluating setting and style changes, strictly forbidding penalizing an image for losing original context when a new setting is requested.

2026-05-14
enforce-logical-consistency
Scientifiques des données

Ensures the reasoning chain does not contain internal contradictions between stated facts and image evaluations.

2026-05-14
entity-definition-activation
Scientifiques des données

Requires explicitly defining specific artists, styles, or entities and their visual hallmarks before evaluating.

2026-05-14
environmental-blending-and-lighting-adaptation
Scientifiques des données

Rules for evaluating background changes, rewarding necessary adaptations to the subject's lighting, shadows, and ground contact.

2026-05-14
evaluate-insertions-and-anatomical-integrity
Artistes en effets spéciaux et animateurs

Guidelines for evaluating inserted humans, animals, or objects, prioritizing anatomical correctness over superficial integration.

2026-05-14
evaluate-physical-plausibility
Scientifiques des données

Guidelines for evaluating inserted or replaced objects for physical plausibility, penalizing floating objects or gravity-defying placements.

2026-05-14
evaluate-text-additions
Scientifiques des données

Guidelines for evaluating text additions, strictly prioritizing exact string matches and legibility over natural integration.

2026-05-14
holistic-evaluation-vs-tunnel-vision
Analystes en assurance qualité des logiciels et testeurs

Prevents tunnel vision on specific requested edits by enforcing holistic evaluation, penalizing collateral damage, anatomical distortion, and loss of original context.

2026-05-14
instruction-over-naturalness
Scientifiques des données

Prioritizes instruction fulfillment over naturalness, warning against the 'subtlety trap' and 'preservation bias'.

2026-05-14
mitigate-positional-bias
Scientifiques des données

Strategies to prevent defaulting to Image B and ensure fair evaluation of both images.

2026-05-14
object-replacement-proportions-and-exaggeration
Concepteurs web et d'interfaces numériques

Guidelines for evaluating object replacements where the new object is inherently bulkier or different in proportion (e.g., snow goggles vs glasses).

2026-05-14
overcoming-default-to-a-bias
Scientifiques des données

Strategies to combat the strong bias of defaulting to Image A by hallucinating details or overvaluing naturalness.

2026-05-14
penalize-gibberish-text
Scientifiques des données

Guidelines for penalizing gibberish text, establishing that instruction fulfillment strictly outweighs text artifacts.

2026-05-14
penalize-unintended-style-changes
Directeurs artistiques

Guidelines for penalizing images that alter the original art style when not requested, with exceptions for intended drastic changes.

2026-05-14
preserve-subject-interaction-and-pose
Autres psychologues

Guidelines for preserving realistic interaction and strict anatomical integrity when an object is replaced.

2026-05-14
prevent-image-feature-swapping
Scientifiques des données

Mandates strict verification of which image contains which features to prevent attributing Image A's details to Image B.

2026-05-14
reasoning-preference-alignment
Scientifiques des données

Ensures the final preference logically matches the comparative analysis in the reasoning chain.

2026-05-14
strict-semantic-and-conceptual-alignment
Scientifiques des données

Ensures strict adherence to requested concepts, rejecting semantic near-misses (e.g., classroom vs cafeteria).

2026-05-14
subject-vs-setting-modifications
Artistes en effets spéciaux et animateursMonteurs de films et de vidéos

Guidelines for preserving the main subject during setting changes, penalizing subject destruction.

2026-05-14
verify-extreme-claims-and-hallucinations
Scientifiques des données

Guidelines to prevent hallucinating extreme failures (e.g., 'blank black screen') and ensure logical consistency.

2026-05-14
verify-instruction-target-and-spatial-relations
Analystes en assurance qualité des logiciels et testeurs

Ensures edits are applied to the exact target, strictly enforcing spatial prepositions (under, behind) and prioritizing correct placement over natural integration.

2026-05-14
object-locator-and-counter
Scientifiques des données

Detects, counts, and locates specific objects in the image, useful for instructions targeting a specific instance (e.g., 'the 4th surfboard').

2026-05-14
side-by-side-image-comparator
Scientifiques des données

Compares two images side-by-side to definitively determine which image contains specific objects, text, or features, preventing feature-swapping.

2026-05-14
text-and-spelling-checker
Rédacteurs en chef

Extracts and verifies text in the image, checking for exact string matches, spelling errors, and legibility.

2026-05-14
visual-detail-and-setting-analyzer
Scientifiques des données

Analyzes images to verify exact settings (e.g., cafeteria vs classroom), fine-grained details, and extreme artifacts.

2026-05-14
visual-question-answering
Scientifiques des données

Answers specific visual questions about the images to verify the presence of objects, text, spatial relations, or specific attributes.

2026-05-14
evaluating-multiple-constraints
Scientifiques des données

How to systematically evaluate prompts with more than one instruction.

2026-05-14
evaluating-style-transfers
Scientifiques des données

Guidelines for evaluating artistic style transfers and thematic changes.

2026-05-14
evaluating-text-additions
Scientifiques des données

Guidelines for evaluating the addition or modification of text in images.

2026-05-14
Affichage des 40 principaux skills collectés sur 76 dans ce dépôt.
RewardHarness Agent Skills sur GitHub | SkillsMP