jordan a776744889 Initial project setup with Claude Code monorepo structure

- Rust workspace with stemedb-core crate
- Full .claude/ configuration (agents, skills, commands, guides)
- ai-lookup/ for token-efficient fact storage
- Quality gates: clippy, fmt, jscpd duplication detection
- Pre-commit hook with 5-phase quality checks
- CLAUDE.md router and CODING_GUIDELINES.md standards

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

2026-01-31 10:56:26 -07:00

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TrustRank (Reputation)

Last Updated: 2025-01-31 Confidence: Medium (planned feature)

Summary

TrustRank is a recursive PageRank-style algorithm for agent credibility. Agents who make accurate claims early gain reputation; agents who contradict settled truth lose reputation.

Key Facts:

Stored in A:{AgentId} key in KV store
Background "Gardener" process calculates scores
Used by Authority lens to weight assertions
Reputation decays for low-confidence agents

File Pointer: crates/stemedb-gardener/src/trustrank.rs (planned, Phase 4)

How It Works

Validation Loop (Gardener)

Every N hours:
1. Find "Settled Facts" (>99% consensus over T time)
2. For each settled fact:
   - Reward agents who claimed it early (+R)
   - Punish agents who claimed opposite (-R)
3. Update A:{AgentId} scores
4. Back-propagate: high-R agents boost sources they cite

Usage in Authority Lens

impl Lens for AuthorityLens {
    fn resolve(&self, candidates: &[Assertion], ctx: &QueryContext) -> LensResult {
        // Weight by agent reputation
        let weighted: Vec<_> = candidates.iter()
            .map(|a| {
                let rep = ctx.get_reputation(&a.agent);
                (a, rep)
            })
            .collect();

        // Return highest weighted assertion
        weighted.into_iter()
            .max_by_key(|(_, rep)| *rep)
            .map(|(a, _)| a.clone())
    }
}

Reputation Score Range

Score	Meaning
0-300	Unreliable (spam, noise)
300-600	Neutral (unproven)
600-900	Reliable (track record)
900-1000	Authoritative (expert)

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