القائمة
Quick factual Q&A with citations. Use for simple lookups and single facts.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | perplexity-ask |
| description | Quick factual Q&A with citations. Use for simple lookups and single facts. |
| user-invocable | false |
Reference patterns for .claude/agents/perplexity-ask.md.
Use only:
python3 $CLAUDE_PROJECT_DIR/.claude/skills/earnings-orchestrator/scripts/pit_fetch.py --source perplexity --op ask ...
Return the wrapper JSON envelope directly.
python3 $CLAUDE_PROJECT_DIR/.claude/skills/earnings-orchestrator/scripts/pit_fetch.py \
--source perplexity --op ask \
--query "What was AAPL's Q1 2025 EPS vs consensus?" \
--pit 2025-02-01T00:00:00-05:00 \
--search-context-size medium
In PIT mode, the synthesis item is excluded from data[] (its available_at = now > PIT). Agent works from PIT-filtered search_results only.
python3 $CLAUDE_PROJECT_DIR/.claude/skills/earnings-orchestrator/scripts/pit_fetch.py \
--source perplexity --op ask \
--query "What was AAPL's Q1 2025 EPS vs consensus?" \
--search-context-size medium
In open mode, data[] includes search_results plus a synthesis item (record_type: "synthesis") with answer and citations.
python3 $CLAUDE_PROJECT_DIR/.claude/skills/earnings-orchestrator/scripts/pit_fetch.py \
--source perplexity --op ask \
--query "AAPL revenue guidance FY2025" \
--search-recency month \
--search-context-size high
sonar-pro model via POST /chat/completions.pit_fetch.py normalizes each search_result with:
available_at (date-only -> start-of-day NY tz)available_at_source: "provider_metadata"record_type: "synthesis", answer, citations, available_at, available_at_source.pit_fetch.py via .env (PERPLEXITY_API_KEY).data[], gaps[]) for deterministic hook validation.Predict stock direction post 8-K earnings & refine using 10-Q/10-K outcomes
Predict stock direction after an 8-K earnings release from a prebuilt earnings context bundle
Post-event causal attribution — explains why a stock moved after 8-K earnings, compares against prediction, writes reusable lessons for future predictions. Production invocation via SDK embed (main session), not fork.
Core Neo4j schema reference with all labels, relationships, data types, and indexes. Use when exploring database structure, checking field types, or understanding the financial knowledge graph schema.
V1105_LONGDESC_START_MARKER. This skill tests v2.1.105's skill-description cap raise from 250 to 1536 characters. If this description appears truncated in the skill listing system-reminder at around character 250, the cap was not raised; if it appears truncated at around 1536, the cap was raised correctly per changelog. The test sentinel markers are placed at specific character offsets: V1105_OFFSET_100 is at char 100 approximately, V1105_OFFSET_300 is at char 300 approximately (old cap would truncate this), V1105_OFFSET_700 is at char 700 approximately, V1105_OFFSET_1000 is at char 1000, V1105_OFFSET_1200 is at char 1200, V1105_OFFSET_1400 is at char 1400, and V1105_END_MARKER is the last visible sentinel. This description also contains deliberately redundant filler to reach the target length without substantive content since the purpose is only to verify truncation behavior rather than to provide meaningful guidance. filler filler filler filler filler filler filler filler filler filler filler filler filler
Child skill for v2.1.107 nesting retest — writes marker and returns