Hippoagent Entity Recall

When the user asks "cosa ricordi di X?", "dammi tutto su Y", "chi è Z?", "cosa abbiamo detto di W?", "ricostruisci ciò che sai di [Capitalized name or known alias]", call hippo_entity_get(name) BEFORE answering. Triggers also on "what do you know about X", "tell me about Y", "facts about Z", "history of W". The tool returns entity+aliases+facts from a persistent SQLite KG (P2.a entity-centric KG, schema v4, Unicode-safe). Lookup is case-insensitive Unicode (Müller/MÜLLER/müller same match) and resolves aliases ("S. Tonegawa" → canonical "Tonegawa"), returning the list of fact_id linked to the entity. If entity is None, declare honestly "non ho memoria di X" — do NOT confabulate from training data. If entity exists but facts == [], declare "ho il nodo entity ma nessun fatto collegato" and suggest hippo_facts_search or hippo_recall. Zero LLM cost, sub-50ms on ~500 entity corpus, free in hosted mode. Backend SQLite WAL + UNIQUE INDEX on name_norm Python-normalized (NFC + str.lower, NOT SQLite ASCII-only LOWER). Entity-first recall — complements hippo_facts_search (topic-based) and hippo_recall (semantic episode) with a third modality, graph node lookup. ANTI-PATTERN: do NOT use for factual claim validation ("Tonegawa won Nobel in 2014") — that's hippo_validate_claim. Do NOT use for topic-based episode/dialog search — that's hippo_search or hippo_recall.

mkdir -p ~/.claude/skills/hippoagent-entity-recall
curl -L https://claudskills.com/skills/hippoagent-entity-recall/SKILL.md \
  -o ~/.claude/skills/hippoagent-entity-recall/SKILL.md

@misc{hippoagent-entity-recall-2026,
  author    = {aureliocpr-ctrl},
  title     = {Hippoagent Entity Recall [Claude Code skill]},
  year      = {2026},
  publisher = {ClaudSkills},
  url       = {https://claudskills.com/skills/hippoagent-entity-recall/}
}