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sea

SEA (Sparse Evidence Attention) learning superpower. Evidence-augmented completion with task-aware scoring and adaptive modulation.

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donaldfilimon/abi19 stars(GitHub 19 + cccskills 0)

Run score Not tested

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  • SKILL.md1.7 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

SEA Superpower Plugin

Claim-honest routing to SEA through the real ABI CLI. This skill does not add a /abi-superpower-sea binary or standalone adaptive/evidence commands.

Capabilities

  • SEA subsystem integration
  • Plugin framework registration
  • Runtime lifecycle management
  • Configuration and settings management
  • Status monitoring and reporting

Integration Points

  • ABI's SEA subsystem integration
  • OpenCode plugin framework integration
  • Runtime lifecycle management
  • Configuration and settings management

Real CLI path

SEA_STORE=$(mktemp -d)
ABI_WDBX_PATH="$SEA_STORE" ./target/debug/abi complete --learn "explain bias"

Task class is inferred. The public CLI does not expose --task or --evidence-limit; MCP ai_learn accepts a capped evidence_limit up to 100.

SEA Architecture

  • 8 signals: semantic, keyword, metadata, recency, authority, graph, contradiction, and task fit
  • Code repair, project recall, and benchmark review adjust baseline weights; all inferred task classes affect task-fit scoring
  • Deterministic record/token/cluster/prompt budgets and a 100-candidate cap

Implementation

Maps to:

  • crates/abi-sea/src/learn_loop.rs
  • crates/abi-sea/src/evidence.rs
  • crates/abi-ai/src/constitution.rs
  • ../wdbx/crates/abi-wdbx/src/store.rs

Runtime Boundary

SEA is linked into the Rust workspace and selected by --learn. Tests and smokes must use a scratch store; ABI_WDBX_PATH=:memory: selects the no-store fallback and does not prove evidence recall.

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