Eval Harness Updater

Refresh eval harnesses to keep live + fallback modes actionable under unstable environments.

Focus Areas

• Prompt and parser drift
• Timeout/partial-stream handling
• SLO and regression gates
• Dual-run fallback consistency

Workflow

1. Resolve harness path.
2. Research test/eval best practices (Exa + arXiv — see Research Gate below).
3. Add RED regressions for parsing and timeout edge cases.
4. Patch minimal harness logic.
5. Validate eval outputs and CI gates.
6. Resolve companion artifact gaps (see Cross-Reference table below).

Research Gate (Exa + arXiv — BOTH MANDATORY)

Before proposing harness changes, gather current best practices:

1. Use Exa for implementation and ecosystem patterns:
   • mcp__Exa__web_search_exa({ query: 'LLM eval harness 2025 best practices' })
   • mcp__Exa__get_code_context_exa({ query: 'eval harness parser reliability timeout handling' })
2. Search arXiv for academic research on evaluation methodology (mandatory):
   • Via Exa: mcp__Exa__web_search_exa({ query: 'site:arxiv.org LLM evaluation harness 2024 2025' })
   • Direct API: WebFetch({ url: 'https://arxiv.org/search/?query=LLM+evaluation+harness&searchtype=all&start=0' })
3. Record decisions, constraints, and non-goals in memory learnings.

arXiv is mandatory (not fallback) when topic involves: LLM evaluation, agent evaluation, SLO gates, regression testing methodology, or parser reliability.

Cross-Reference: Creator Ecosystem

This skill is part of the Creator Ecosystem. When research uncovers gaps, trigger the appropriate companion creator:

Gap Discovered	Required Artifact	Creator to Invoke	When
Domain knowledge needs a reusable skill	skill	Skill({ skill: 'skill-creator' })	Gap is a full skill domain
Existing skill has incomplete coverage	skill update	Skill({ skill: 'skill-updater' })	Close skill exists but incomplete
Capability needs a dedicated agent	agent	Skill({ skill: 'agent-creator' })	Agent to own the capability
Existing agent needs capability update	agent update	Skill({ skill: 'agent-updater' })	Close agent exists but incomplete
Domain needs code/project scaffolding	template	Skill({ skill: 'template-creator' })	Reusable code patterns needed
Behavior needs pre/post execution guards	hook	Skill({ skill: 'hook-creator' })	Enforcement behavior required
Process needs multi-phase orchestration	workflow	Skill({ skill: 'workflow-creator' })	Multi-step coordination needed
Artifact needs structured I/O validation	schema	Skill({ skill: 'schema-creator' })	JSON schema for artifact I/O
User interaction needs a slash command	command	Skill({ skill: 'command-creator' })	User-facing shortcut needed
Repeated logic needs a reusable CLI tool	tool	Skill({ skill: 'tool-creator' })	CLI utility needed
Narrow/single-artifact capability only	inline	Document within this artifact only	Too specific to generalize

Iron Laws

1. ALWAYS run the Exa + arXiv research gate before updating any eval harness — updating without current external knowledge produces stale evaluation criteria.
2. NEVER remove existing evaluation criteria without replacing them with equivalent or better ones — reducing test coverage in an eval harness is a regression.
3. ALWAYS cross-reference the creator ecosystem for gaps before declaring the harness complete — missing companion artifacts (skills, agents, schemas) leave the harness unable to test new capabilities.
4. NEVER update eval harness in isolation from the skill/agent it evaluates — harness and artifact must stay synchronized or the harness tests the wrong behavior.
5. ALWAYS preserve backward compatibility in eval scoring — changing scoring semantics without migrating historical baselines makes trend analysis impossible.

Anti-Patterns

Anti-Pattern	Why It Fails	Correct Approach
Updating eval harness without research gate	Criteria based on outdated knowledge; misses recent evaluation methodology advances	Always run Exa + arXiv research before updating any eval criteria
Removing test cases to simplify the harness	Silently reduces coverage; regressions pass undetected	Only remove test cases when the behavior they tested has been deliberately removed
Harness and artifact in separate PRs	Harness tests wrong behavior the moment artifact changes; immediate test drift	Always update harness and artifact in the same commit
Changing scoring scale mid-project	Historical baselines become incomparable; trend analysis breaks	Define scoring scale once; create a migration if it must change
Declaring harness complete without companion check	Missing skills or schemas leave evaluation gaps	Always run companion artifact check before marking harness update complete

Memory Protocol (MANDATORY)

Before starting: Read .claude/context/memory/learnings.md

After completing:

• New evaluation pattern → .claude/context/memory/learnings.md
• Evaluation gap found → .claude/context/memory/issues.md
• Scoring decision made → .claude/context/memory/decisions.md

ASSUME INTERRUPTION: If it's not in memory, it didn't happen.