Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Search conversation history for research context - papers discussed, citations analyzed, discoveries made, or questions explored.
Research Messages Search
Find past research conversations, paper discussions, and analysis from your conversation history.
Quick Start: The Research Needle
Most common use: Finding when you discussed a specific paper, topic, or research question.
Standard Opening
User: "When did we discuss transformer architectures?"
Agent: "Let me search our conversation history for transformer discussions.
I'll use hybrid search (semantic + keyword) to find the most relevant messages."
Agent: "Found 8 conversations about transformers:
Most relevant:
- Dec 15, 2025: Analyzed 'Attention Is All You Need' paper
- Dec 18, 2025: Discussed transformer memory mechanisms
- Jan 3, 2026: Compared transformer vs Mamba architectures
Want me to expand around any of these to see the full context?"
Diagnosis: What Are They Looking For?
Ask 2 questions to understand the search:
Question 1: What type?
Agent: "Are you looking for:
A) A specific paper we discussed?
B) Analysis of a research topic?
C) Discovery results from a query?
D) Citation network we built?
E) Something else?"
Question 2: Time range?
Agent: "Time frame:
A) Recent (last week)
B) This month
C) Specific date/period
D) Any time (full history)"
Route based on answers:
Type A (paper) + Recent → Paper title search, last 7 days
Type B (topic) + Any time → Semantic search, all history
Type C (discovery) + Month → Keyword "discovery", current month
Type D (citation) + Specific → Date-bounded search
Conversation Patterns
Pattern 1: Finding Paper Discussion
Trigger: User asks about a specific paper
User: "What did we say about the BERT paper?"
Agent: "Let me search for BERT discussions."
[Run search with "BERT paper", focus on title/authors]
Agent: "Found 3 conversations:
- Nov 20: Initial BERT paper analysis
- Nov 22: Compared BERT vs RoBERTa
- Dec 1: Used BERT for sentiment analysis
The Nov 20 conversation has the main analysis. Want to see it?"
[If yes, use needle+expand strategy]
Pattern 2: Finding Topic Discussions
Trigger: User asks about research topic/concept
User: "When did we talk about citation networks?"
Agent: "Searching for citation network discussions..."
[Run semantic search, --mode vector for concept matching]
Agent: "Found 5 conversations about citation networks:
Most relevant:
- Jan 5: Built citation graph for ML papers
- Jan 8: Analyzed influential papers using PageRank
- Jan 10: Discussed loop closure in citations
These span multiple conversations. Want the full thread?"
[If yes, expand around first occurrence and follow chronologically]
Pattern 3: Finding Discovery Results
Trigger: User asks about papers found from discovery
User: "What papers did we find about state space models?"
Agent: "Searching for state space model discoveries..."
[Search with "discovery state space models" + keyword mode]
Agent: "Found 2 discovery sessions:
- Dec 28: Discovered 15 SSM papers from ArXiv
- Jan 4: Found Mamba paper and variants
The Dec 28 session has the full paper list. Show that?"
Pattern 4: Needle + Expand Strategy
Trigger: User wants full context around a specific finding
Agent: "Found the needle! Message from Dec 15 at 2:30 PM.
Getting context around that message..."
[Run get-messages with --before and --after]
Agent: "Here's the full conversation (10 messages before + 10 after):
[Show conversation thread]
That's the complete context. Need more?"
Quick Reference Cards
Card 1: Search Modes
When to use each mode:
Mode
Use When
Example
hybrid (default)
General search
"transformer discussions"
vector
Concept/semantic
"papers about memory"
fts
Exact phrase
"Attention Is All You Need"
Default to hybrid unless user needs exact matches or pure semantic.
Card 2: Time Filters
Common time patterns:
# Last week
--start-date "2026-01-06" --end-date "2026-01-13"# Specific month
--start-date "2025-12-01" --end-date "2025-12-31"# After specific date
--start-date "2025-11-01"# Before specific date
--end-date "2025-10-31"
Pro tip: Use ISO format YYYY-MM-DD for dates.
Card 3: Research-Specific Queries
Effective search terms for research:
Paper titles: Use quotes for exact match
Authors: Include "et al" or full names
Topics: Use technical terms, not casual language
Citations: Include "citation" or "references"
Discovery: Include "discovered" or "found papers"
Analysis: Include "analyzed" or "compared"
Examples:
"Attention Is All You Need" → Exact paper
transformer memory mechanisms → Topic discussion
Vaswani et al → Author search
citation network analysis → Analysis type
discovered mamba papers → Discovery results
Agent: "That search returned too many results. Let's narrow it:
Option A: Add time filter (when roughly was this?)
Option B: Add specific terms (any unique words?)
Option C: Use exact phrase (put in quotes)
Which helps?"
Refinement 2: No Results Found
Symptom: Search returns 0 results
Fix: Broaden and diagnose
Agent: "No results for that search. Let's troubleshoot:
1. Try semantic search (concepts vs exact words):
--mode vector
2. Try broader terms:
'transformer' instead of 'transformer architecture'
3. Search all agents (if multi-agent setup):
--all-agents
Which should we try?"
Refinement 3: Wrong Context
Symptom: Found messages but not the right conversation
Fix: Expand correctly
Agent: "That's not the conversation you wanted. Let me:
1. Check the other search results (we have 4 more)
2. Expand more context (20 messages instead of 10)
3. Refine search terms (what's unique about that conversation?)
Which approach?"
Refinement 4: Need Cross-Session Context
Symptom: Topic discussed across multiple sessions
Fix: Search with sort, present chronologically
Agent: "This topic spans multiple conversations. Here's the timeline:
Session 1 (Dec 5): Initial discussion
Session 2 (Dec 8): Follow-up analysis
Session 3 (Dec 12): Final conclusions
Want to see all three in order, or just the most relevant?"
Troubleshooting
Problem
Quick Fix
Search too slow
Reduce --limit to 5
No results found
Use --mode vector for semantic search
Too many results
Add date range with --start-date
Wrong messages
Use exact phrase in quotes
Need more context
Use get-messages.ts with --before/--after
Want full thread
Expand before+after the needle
Multi-agent search
Add --all-agents flag
Need specific date
Use --start-date and --end-date (same day)
Results not relevant
Switch to --mode fts for exact keyword
Advanced: Cross-Agent Research Search
Use case: Finding which agent discussed a topic
Agent: "Searching across all agents for transformer discussions..."
[Run with --all-agents flag]
Agent: "Found discussions in 3 agents:
- Agent A (Lead Engineer): Technical analysis
- Agent B (Literature Review): Paper summaries
- Agent C (Research Assistant): Discovery results
Which agent's context do you want to explore?"
Results include agent_id - use with finding-agents skill to get agent details.
Summary: The Agent's Mental Model
Core workflow:
Understand what user is looking for (paper, topic, discovery, etc.)
Choose search mode (hybrid default, vector for concepts, fts for exact)
Run search with appropriate filters
Present top results with context
If needed, expand around the needle
Refine if results aren't quite right
Key principles:
Default to hybrid search (best balance)
Use semantic (vector) for concept searches
Use exact (fts) for specific phrases/titles
Always offer to expand for full context
Present results with timestamps and message types
Help user narrow down if too many results
Help user broaden if no results
Success: User finds the research conversation they were looking for and gets the full context they need.
