Agent skill

sparta-intent

DEPRECATED — Use /memory intent, /memory recall, /memory clarify instead. All capabilities absorbed into graph_memory.

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/sparta-intent

SKILL.md

Sparta Intent Mapper (DEPRECATED 2026-02-19)

This skill has been absorbed into /memory. All capabilities now live in graph_memory.*:

Old (sparta-intent) New (graph_memory)
sparta_intent.inference graph_memory.intent.IntentMapper
sparta_intent.query_spec graph_memory.intent.QuerySpec
classifiers/predictor.py graph_memory.classifiers.AmbiguityPredictor / IntentPredictor
ambiguity_oracle.py graph_memory.ambiguity.AmbiguityOracle
result_stats.py graph_memory.ambiguity.ResultStats
clarifier.py graph_memory.ambiguity.Clarifier
aql_compiler.py + arango_exec.py graph_memory.hybrid_search.hybrid_search_sparta_qra()

Why deprecated: This skill was a silo that bypassed /memory's hybrid search (BM25+vector+graph), taxonomy extraction, multihop traversal, and RecallSource infrastructure. The stress test hit a B-grade ceiling (avg 0.784) because it retrieved individual QRAs without graph context.

Migration: Replace from sparta_intent.inference import IntentMapper with from graph_memory.intent import IntentMapper. Replace raw AQL queries with hybrid_search_sparta_qra().

Classifier models still used: The trained DistilBERT models at /mnt/storage12tb/media/agents/shared/sparta-intent/classifiers/models/ are still loaded by graph_memory.classifiers.

Original Architecture (for reference)

Query → IntentMapper → QuerySpec → AQLLinter → AQLCompiler → PlanGate → ArangoExecutor → ResultStats → AmbiguityOracle → Clarifier

Usage

Query (rule-based, <5ms)

bash
./run.sh query "How does firmware verification prevent attacks?"
./run.sh query "What controls mitigate T1071?" --json
./run.sh query "How do I detect RF jamming?" --execute  # runs against ArangoDB

Query with LLM (scillm via Chutes.ai)

bash
./run.sh query "How do I detect RF jamming?" --llm

Diagnose ambiguity

bash
./run.sh diagnose "spacecraft security"

Training (delegates to create-intent-map)

bash
./run.sh train-sft        # prints delegation instructions
./run.sh train-grpo       # prints delegation instructions

Composability

Integration How
Memory recall() before inference for cached QuerySpecs, learn() after successful queries
scillm LLM-based inference via quick_completion() when --llm flag is set
Taxonomy Canonical BRIDGE_KEYWORDS imported from taxonomy/taxonomy.py for Tier0 bridge classification
Embedding graph_memory.embeddings.encode_texts() for vector lane AQL queries
Telemetry track_skill("sparta-intent") wraps query execution for outcome logging

Safety

  • 7-template AQL allowlist — no dynamic query construction
  • Path traversal prevention — os.path.realpath() check on template paths
  • Bind parameters only — no string interpolation in AQL
  • Depth cap — max 2 hops in graph traversal
  • k cap — max 25 results (1000 for diagnostics)
  • Plan gate — rejects full collection scans before execution
  • AQL linter — validates QuerySpec constraints pre-compilation

Oracle Signals

Signal Threshold Meaning
Margin < 0.08 Top results too close in score
Entropy > 1.35 Score distribution too flat
Tag disagreement > 0.45 Result set spans multiple tactics
Entity disagreement > 0.55 Result set spans multiple controls

Decision: >= 2 triggers = ambiguous → generate clarification question.

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