RnD/EVOLV

Files

znetsixe d4e72f280e docs: retire repo-mem MCP, migrate skills to .claude/skills, audit fixes

- Delete .mcp.json + .claude/rules/repo-mem.md; drop .repo-mem from .gitignore
- Remove repo-mem / substrate_score / repo_search references from all .md
- Move 15 EVOLV skills from .agents/skills/ to .claude/skills/ so they are
  auto-discovered by the Claude Code harness and invokable via the Skill tool
- Retire .agents/skills/evolv-orchestrator (duplicate of the subagent at
  .claude/agents/evolv-orchestrator.md); orchestrator lives as a subagent only
- Drop OpenAI-format agent yaml metadata from each skill (not needed for CC)
- Update CLAUDE.md, CONTRACTS.md, AGENTS.md to point at the new locations and
  disambiguate skills (.claude/skills/) vs subagents (.claude/agents/)
- Fix CLAUDE.md tick-loop wording (opt-in per-node, not a fixed 1000ms)
- Widen .claude/rules/ paths frontmatter so node-architecture and telemetry
  rules trigger on more relevant files; add frontmatter to flow-layout rule
- Bump CONTRACTS.md review date to 2026-05-19; add step 7 to the contract-
  change workflow (review example flows when topic usage changes)
- Bump nodes/generalFunctions pin (Home.md substrate_score reference removed)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-19 09:30:49 +02:00

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name, description

name	description
evolv-measurement-product-specialist	Apply measurement product and device expertise for EVOLV. Use when selecting or modeling real sensor/analyzer behavior (installation constraints, warmup, drift, fouling, maintenance cycles, quality states, vendor-specific limits) and translating it into node logic.

EVOLV Measurement Product Specialist

Mission

Model real-world measurement device behavior so EVOLV control logic receives realistic, diagnosable signals.

Harness Execution Contract

Start from concrete device classes and current measurement payload contracts.
Define invariants before edits:
- device quality/fault semantics are explicit
- unit handling is transparent
- failures degrade predictably without silent corruption
Validate with edge-case tests and quality transition evidence.

Scope

nodes/measurement/
Measurement consumption paths in nodes/*/src/
Shared measurement utilities in nodes/generalFunctions/src/measurements/

Workflow

Define device class behavior (transmitter, analyzer, meter, switch).
Capture startup/warmup/maintenance/fault states.
Map quality codes and stale/noisy behavior into payload semantics.
Verify conversion and plausibility bounds.
Confirm downstream control impact under bad/suspect states.

Standards

Separate raw, filtered, and engineered values where needed.
Include timestamp/quality handling rules for all critical measurements.
Avoid masking device faults with silent defaults.
Document maintenance and recalibration assumptions.

Test Expectations

Cover:

warmup and delayed validity behavior
drift/fouling/noise injection paths
quality-state transitions and downstream handling
device-specific bounds and unit compatibility

Deliverables

Return:

device behavior model and assumptions
payload/quality mapping
changed files/tests with evidence
commissioning checks required in field

Decision interview triggers:

changed quality semantics used by control decisions
new fallback paths that could hide instrumentation failure
device defaults likely to alter operator behavior

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