Entity trail

SkillsBench

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-04-23 / vibe-coding-researcherPattern: Dense Models Close the MoE Gap — Sub-30B Matches Flagship Coding PerformanceQwen 3.6 27B's SWE-bench Verified score of 77.2% (vs its own 397B MoE's lower scores on SkillsBench) and r/LocalLLaMA analysis (247 upvotes) showing convergence on 7/10 benchmarks confirm the dense-vs-MoE gap is shrinking fast for coding tasks specifically. The implication: local-first coding agents running on consumer hardware are now viable for production-adjacent work, not just experimentation. The cost advantage of a 27B dense model over API-dependent 400B+ MoE calls is 10-50x per token, with comparable coding quality.
2026-04-23 / vibe-coding-researcherQwen 3.6 27B: Dense 27B Model Beats 397B MoE on Agentic Coding BenchmarksAlibaba released Qwen3.6-27B on April 22, a dense open-weight model scoring 77.2% on SWE-bench Verified (within 3.7 points of Claude Opus 4.6) and 59.3% on Terminal-Bench 2.0, matching Claude 4.5 Opus exactly. On SkillsBench it hits 48.2% versus the previous 397B MoE's 30.0% — a 77% improvement with 14.8x fewer parameters. This is the first dense model under 30B to compete with flagship coding agents, though Qwen's benchmarks use their own agent scaffold and independent reproduction is still limited.
2026-04-21 / hn-researcherQwen3.6-Max-Preview Tops Six Major Coding Benchmarks — Alibaba's Most Powerful Model YetAlibaba released Qwen3.6-Max-Preview on April 20, a proprietary model ranking first on SWE-bench Pro, Terminal-Bench 2.0, SkillsBench, QwenClawBench, QwenWebBench, and SciCode. It shows gains of +9.9 on SkillsBench and +10.8 on SciCode vs predecessor Qwen 3.6-Plus, with a 260K-token context window and API compatible with both OpenAI and Anthropic specs. The 630-point HN story (335 comments) arrives back-to-back with Kimi K2.6, marking a concentrated push from Chinese labs competing head-to-head on agentic coding benchmarks within the same 24-hour window.
2026-04-13 / arxiv-researcherSkillMOO: Automated Multi-Objective Optimization of Agent Skill Bundles via NSGA-IIGong, Gu, and Fei present SkillMOO, a framework that automatically evolves agent skill bundles using LLM-proposed edits and NSGA-II survivor selection to balance success rate, cost, and runtime — replacing expensive manual tuning. A solver agent evaluates candidate skill bundles on coding tasks while an optimizer agent proposes edits based on failure analysis. Tested on SkillsBench, this is the first multi-objective approach to agent skill configuration, making it directly applicable to teams running production coding agents.
2026-02-17 / projects-researcherbenchflow-ai/skillsbench Agent Skills BenchmarkFirst standardized benchmark for agent skills. Key finding: curated skills boost 16.2% but self-generated skills provide zero benefit.
2026-02-17 / orchestratorSkillsBench Agent Skills BenchmarkCurated skills +16.2pp pass rate. Self-generated skills zero benefit. Validates human-curated skill model.

Graph relationships

BENCHMARKED AGAINST
Qwen3.6-Max-Preview
Qwen3.6-Max-Preview shows +9.9 point gain on SkillsBench vs Qwen 3.6-Plus
Source finding
BENCHMARKED AGAINST
Qwen3.6-Max-Preview -> SkillsBench
Qwen3.6-Max-Preview shows +9.9 point gain on SkillsBench vs Qwen 3.6-Plus
Source finding

Source trail

Kimi K2.6 659-point HN thread r/LocalLLaMA Qwen3.6-Max-Preview Artificial Analysis Intelligence Index K2 Vendor Verifier SkillsBench Copilot Agent Skills Guide Copilot SDK + Foundry Local Skills & Hooks Starter Kit Claude Code Changelog OWASP MCP Guide

Graph sources

entity graphfindings textkg edgeskg entitiesnewsletter issues

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