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Downey6689Downey6689/skills/advanced-evaluation/SKILL.md

advanced-evaluation

Skill1 starsChanged 9 months agoNo licenceView on GitHub

Advanced LLM-as-judge evaluation. Invoke when you need rubrics, pairwise comparisons, or bias mitigation.

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  • Other versions10 projects use a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 10

    1. muratcankoylan/Agent-Skills-for-Context-Engineering★ 18ksame name
    2. brake71/1c-ssl-skills★ 85same name
    3. docxology/template★ 20same name
    4. xfstudio/skills★ 9same name
    5. rohunvora/my-claude-skills★ 6same name
    6. nek1987/auto-agent-harness★ 4same name
    7. Ghosteken/agent-harness★ 2same name
    8. corticalstack/flow★ 1same name
    9. AbdelrhmanAlquran/claude-skills★ 0same name
    10. DVwebBiz11/claude-skills★ 0same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • Used alongsideProjects with it also use self-improvement-loops, long-horizon-prompting and 2 more.

    Other files people use alongside this one.

    Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
    • DVwebBiz11Skill

      self-improvement-loops

      This skill should be used when the harness, scaffold, workflow, or optimizer itself is the optimization target: recursive self-improvement (RSI) loops, meta-harnesses, self-improving harnesses that mine their own failures and propose bounded edits, evolutionary or population-based search over agent scaffolds, acceptance gates for self-modifying systems, and agentic context evolution where the mechanism that produces context is versioned and evolved. Route governance of a single autonomous loop (locked surfaces, durable logs, rollback, novelty gates, approval boundaries) to harness-engineering, measurement and quality-gate design to evaluation, judge design to advanced-evaluation, and remote sandbox infrastructure to hosted-agents.

      Also in 4 projects that use this

    • DVwebBiz11Skill

      long-horizon-prompting

      This skill should be used when writing, enhancing, or evaluating the launch prompt for a long-running autonomous agent or a parallel multi-agent orchestration attacking a hard problem: pseudo-formal task briefs that define terms and an exact success predicate linguistically, enumerate non-counting outcomes, set persistence rules with explicit stop and return conditions and effort floors, manage a diverse portfolio of parallel approaches with an approach registry and blocked-route bookkeeping, and gate the return on adversarial audit. Route agent topology and coordination protocols to multi-agent-patterns, runtime control surfaces and loop governance to harness-engineering, evaluator and quality-gate construction to evaluation, judge design to advanced-evaluation, and compaction or memory mechanics to context-compression and memory-systems.

      Also in 4 projects that use this

    • AbdelrhmanAlquranSkill

      latent-briefing

      This skill should be used when the user asks to "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context", "latent briefing", "reduce worker tokens", "cross-agent memory without summarization", or discusses Attention Matching compaction, recursive language models with workers, or token explosion in hierarchical agents.

      Also in 4 projects that use this

    • GitHubSkill

      harness-engineering

      Adopt repository-level harness engineering for coding agents. Use when a user wants to prevent repeated AI coding-agent mistakes by turning failures into durable instructions, drift checks, regression tests, failure memory, and adoption reports tailored to the target repository.

      Also in 4 projects that use this

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