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Guide for running ISC-Bench jailbreak evaluation against any LLM. Use this whenever someone wants to evaluate LLM safety with ISC-Bench, run the TVD (Task-Validator-Data) benchmark pipeline, test model robustness against structural safety collapse, or compare…

ISC template for AI Safety & ML. Target-parametrized schema (target_category + text) classified by KoalaAI/Text-Moderation (OpenAI moderation-compatible 9-way: H/H2/HR/OK/S/S3/SH/V/V2). Replaces gated nvidia/Aegis-AI-Content-Safety-LlamaGuard-Defensive-1.0…

ISC template for AI Safety & ML. Single-field schema (headline) classified by Stremie/bert-base-uncased-clickbait. HPC probe confirmed LABEL_1 = clickbait (news 0.99 LABEL_0, clickbait 0.94-0.98 LABEL_1). Harassment-style doxxing headlines also fire as…

ISC template for AI Safety & ML. Package-based (not HF classifier) using the `conllu` Python parser (github.com/EmilStenstrom/conllu). Agent builds a CoNLL-U Universal Dependencies corpus — per sentence must annotate FORM, LEMMA, UPOS, XPOS, FEATS, HEAD,…

ISC template for AI Safety & ML. Package-based using the official `detoxify` Python package (github.com/unitaryai/detoxify), which exposes a 6-label score dict: {toxicity, severe_toxicity, obscene, threat, insult, identity_attack}. Target-parametrized schema…

ISC template for AI Safety & ML. Single-field article_text classified by hamzab/roberta-fake-news-classification (binary FAKE/TRUE). Probe showed narrow recall — most fake-sounding articles land TRUE 0.98-1.00. Only conspiracy-theory style (mind-control,…

ISC template for AI Safety & ML. Paired (query, assistant_safe, assistant_unsafe) schema classified by meta-llama/Llama-Guard-3-8B. Llama-Guard emits a one-token verdict (safe / unsafe\nS<N>) under Meta's chat template; the template asserts both fields…

ISC template for AI Safety & ML. Single-field schema (code) classified by mrm8488/codebert-base-finetuned-detect-insecure-code. HPC probe showed sibumi/DISTILBERT_static_malware-detection (the name the template originally used) is BROKEN — every input returns…

Design Jev-assisted workflows using collected use cases, references and examples. Adapt and combine patterns, then use Jev for classification, scoring or candidate selection when useful, including batch judgments and agent checkpoints.

Choose one legal next action in a browser, desktop, game or simulation. Supply fresh observed state and available actions. The host or simulator executes and checks the result; selection does not grant permission.

Locate, select, extract and verify evidence in documents or observed code inventories. Use for source-span extraction, passage reranking, claim checks or choosing code locations to inspect. Preserve citations and no-match outcomes; use required graph tools…

Judge supplied outputs against explicit criteria, including code-change reviews and authorized safety evaluations. Use for evidence-backed review leads, rubric judgments, or batch, multi-turn and team transcript evaluation; not permission to merge or run…

Use for user-defined inbox, support-ticket, feedback or record classification and prioritization, especially bulk parallel judgments with sufficient per-record context. Accepts smoke_test to have the host agent write and validate a task-specific paired pilot…

Generate or edit images with GPT Image 2 or 2.5 through the packaged CLI and Reference Gallery. Use for image requests including imprecise 'GPT 2.5' model names, posters, typography, reference edits, and inpainting; resolve the model choice before generation.

Analyze user-provided reference images and reverse-engineer high-fidelity AI image-generation prompts. Use when the user asks to recreate, imitate, reverse-engineer, or extract prompts from photographs, illustrations, 3D renders, products, characters,…

Construct a new TVD (Task-Validator-Data) task and codespace from scratch, in any domain, wiring any classifier / moderation API / scorer / scientific tool into an incomplete program so a frontier model completes it and produces the data itself. Use whenever…

Digest arXiv papers into structured local folders (paper.json + per-section markdown + figures/tables/equations/listings as real files), then read and cross-check them programmatically. Use whenever the user asks to read, compare, extract from, or verify…