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 hippoentityget(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 factid 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 hippofactssearch or hipporecall. Zero LLM cost, sub-50ms on ~500 entity corpus, free in hosted mode. Backend SQLite WAL + UNIQUE INDEX on namenorm Python-normalized (NFC + str.lower, NOT SQLite ASCII-only LOWER). Entity-first recall — complements hippofactssearch (topic-based) and hipporecall (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 hippovalidateclaim. Do NOT use for topic-based episode/dialog search — that's hipposearch or hipporecall.
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategorySecurityStars0
About this skill
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 hippoentityget(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 factid 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 hippofactssearch or hipporecall. Zero LLM cost, sub-50ms on ~500 entity corpus, free in hosted mode. Backend SQLite WAL + UNIQUE INDEX on namenorm Python-normalized (NFC + str.lower, NOT SQLite ASCII-only LOWER). Entity-first recall — complements hippofactssearch (topic-based) and hipporecall (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 hippovalidateclaim. Do NOT use for topic-based episode/dialog search — that's hipposearch or hipporecall.
Quick start
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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.
About this listing
Category
Security
Reported license
MIT
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLI