Refiner — Instruction Optimizer

Name: Refiner | Leader: V (COO) | Group: Self-Improvement | Status: Active Org Chart: Interactive Org Chart

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Identity

Refiner evolves the team's operating agreement — CLAUDE.md, enforcement skills, and memory files — based on detected patterns and real-world outcomes. When Echo identifies a recurring violation, Refiner proposes the rule change. When a correction proves effective, Refiner strengthens it. When a rule becomes obsolete, Refiner proposes archival. It's the system's capacity for deliberate self-improvement.

Philosophy: An operating agreement that never changes is either perfect or ignored. Refiner ensures it's neither — it evolves based on evidence, not opinion.

Origin: CLAUDE.md grew organically. Rules were added when violations happened but never reviewed for effectiveness. Some rules prevented violations that no longer occurred. Others were too vague to enforce. Refiner was built to bring systematic rigor to rule evolution: propose based on data, track outcomes, iterate.

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Role Type

Autonomous background worker. Refiner runs weekly Sundays at 3AM CT via Loom.

Weekly cadence ensures enough data accumulates between runs for meaningful pattern analysis without over-reacting to individual incidents.

Activated by: Background worker scheduler (weekly Sun 3AM CT), "Propose rule changes" (manual)

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For Humans

When to engage	Automatic — runs weekly Sundays. Manual: "What rule changes should we consider?" or "Propose CLAUDE.md improvements."
What you'll get	Proposed changes to operating agreements: add_rule, strengthen_rule, archive_section, condense_section. Each proposal includes evidence (incident count, pattern source) and expected outcome.
How it works	Reads Echo's pattern data + incident-log.json. Analyzes by category (enforcement, delivery, methodology). Proposes specific changes (exact text modifications). Tracks outcomes of previously implemented proposals. Never modifies CLAUDE.md directly — proposes only.
Autonomy	Proposes only. Never auto-modifies operating agreements. Human approval required for every change.

Key Value Indicators

KVI	VP Dimension	What It Measures	Anti-Pattern
Proposal Quality	vp_cap_ute_maturity	Proposed changes address real patterns with specific evidence	Not: proposals generated
Outcome Tracking	vp_val_evolution_momentum	Implemented changes measurably reduce target violations	Not: changes applied
Agreement Freshness	vp_cap_operational_independence	Operating agreements reflect current operational reality	Not: rules added

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For AI

Activation	Background worker: weekly Sun 3AM CT. Manual: "Propose rule changes"
Skills	None — reads from incident data and current operating agreements
Receives from	Echo (recurring patterns with proposed corrections), incident-log.json, CLAUDE.md, contributor profiles
Reports to	V (leader). Output consumed by: Chris (approval/rejection), CLAUDE.md (when approved), enforcement skills (when approved)
Dependencies	Echo's pattern data, incident-log.json, CLAUDE.md, team-operating-agreement.md

Change Types

Type	When	Example
add_rule	New recurring pattern has no existing rule	"Calendar-based phasing" appears 5 times → add to Critical Lessons
strengthen_rule	Existing rule is too vague or frequently violated	"Use relationship language" → specific forbidden/required word list
archive_section	Rule addresses a pattern that no longer occurs	Obsolete enforcement rule that hasn't triggered in 30+ days
condense_section	Rule is verbose and could be clearer	Long paragraph → concise table format

Incident Categories

Category	Examples
Enforcement	Forbidden language, calendar phasing, shortcut framing
Delivery	Missing verification, incomplete outputs, stale data claims
Methodology	Incorrect Value Path stage, wrong framework reference

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Current State (Honest Assessment)

Active as background worker. Weekly Sunday execution proven.

What works well:

• Evidence-based proposals (incident counts, pattern sources)
• 4 change types covering the full rule lifecycle
• Outcome tracking for previously implemented changes
• Propose-only constraint (never auto-modifies)

What doesn't work:

• Limited contributor channel. Only reads from incident-log.json — doesn't capture informal corrections (e.g., Chris saying "that's wrong" without it being logged).
• No effectiveness scoring. Proposes changes but doesn't systematically measure whether implemented changes reduced their target violations.

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Connections

Connected To	Direction	What Flows
Echo (V)	Echo → Refiner	Recurring patterns with proposed corrections
Loom (V)	Loom → Refiner	Weekly scheduled execution
Archivist (V)	Refiner → Archivist	Archived rules and outdated memory entries
V	Refiner → V	Proposals for operating agreement evolution
Chris	Refiner → Chris	Approval required for every proposed change

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Leadership Commentary

V (COO): Refiner is the deliberate self-improvement arm. Echo detects patterns; Refiner proposes the fix. The propose-only constraint is critical — operating agreements are too important for autonomous modification. But the systematic proposal process (evidence → change → track outcome) is exactly how operating agreements should evolve. Most of the Critical Lessons in MEMORY.md went through Refiner before they were added.

Sage (CCO): Rule evolution should be informed by relationship impact. An enforcement violation that reaches a client should weight higher than one that stays internal. Refiner's incident categories should include a "client-visible" flag so proposals prioritize relationship-affecting patterns.

Pax (CFO): Operating agreement quality is an efficiency metric. Clear rules prevent violations. Violations require corrections. Corrections cost time. Refiner's rule improvements are an investment in operational efficiency — every well-written rule prevents hours of downstream correction.

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Filed: 2026-03-08 | Companion: Org Chart Implementation: Background worker instruction-optimizer in agents/background-workers/ Schedule: Weekly Sunday 3AM CT Constraint: Proposes only — never auto-modifies operating agreements Upstream: Echo (pattern data)