context7-openclaw-research

name	context7-openclaw-research
version	2.0.0
description	Library research via web search and long-term memory via OpenClaw LTM. OpenClaw may enrich stored content using Context7/Perplexity.
metadata	{"short-description":"ライブラリ調査（web検索 + OpenClaw委譲）と長期記憶（OpenClaw LTM）。"}
allowed-tools	["WebSearch","WebFetch","Grep","Grep","Grep","Grep","Bash"]
dependencies	["lorairo-mem"]

Complex Analysis (Library Research + Long-Term Memory)

Complex analysis using web research and OpenClaw LTM for design pattern memory and strategic decisions.

Note: ライブラリ調査は WebSearch/WebFetch で実施し、LTM 保存時に OpenClaw が Context7/Perplexity を使って内容をブラッシュアップして保存します。

When to Use

Use this skill when:

• Design pattern search: Researching past similar designs (OpenClaw LTM)
• Library research: WebSearch で公式ドキュメント/仕様を確認し、保存時に OpenClaw が補強
• Long-term memory: Storing design decisions and rationale (OpenClaw LTM)
• Dependency analysis: Understanding architectural relationships
• Strategic decisions: Evaluating approaches and trade-offs

Core Patterns

1. Design Knowledge Search (OpenClaw LTM)

ltm_search.py - Past design patterns

• Searches design decisions, implementation patterns, lessons learned
• Usage:

python3 .agents/skills/lorairo-mem/scripts/ltm_search.py <<'JSON'
{"limit": 10, "filters": {"type": ["decision", "howto"], "tags": ["repository-pattern"]}}
JSON

ltm_latest.py - Recent entries

python3 .agents/skills/lorairo-mem/scripts/ltm_latest.py <<'JSON'
{"limit": 5}
JSON

2. Long-term Memory Storage (OpenClaw LTM)

POST /hooks/lorairo-memory - Store knowledge

• Stores design knowledge with proper metadata
• Use: After implementation, after major decisions
• Content: Design approach, rationale, results, lessons learned

HOOK_TOKEN=$(jq -r '.hooks.token' ~/.clawdbot/clawdbot.json)

curl -sS -X POST http://host.docker.internal:18789/hooks/lorairo-memory \
  -H "Authorization: Bearer $HOOK_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "title": "LoRAIro [Feature] Design Decision",
    "summary": "Brief summary of the decision",
    "body": "# Design Details\n\n## Background\n...\n\n## Decision\n...\n\n## Rationale\n...",
    "type": "decision",
    "importance": "High",
    "tags": ["architecture", "pattern-name"],
    "source": "Container"
  }'

3. Library Research (Web + OpenClaw)

ライブラリ調査は WebSearch で公式ドキュメントを確認し、要点をまとめて LTM 保存時に OpenClaw が補強します。

Example (web search):

WebSearch: "PySide6 Signal Slot QThread official docs"

4. Web Research

WebSearch - Latest information

• Searches official docs, blogs, case studies, recent updates
• Use: When you need up-to-date or external sources

Workflow Guidelines

Design Phase

1. LTM search (ltm_search.py) - Past designs
2. Library research (WebSearch) - Technical details
3. Store decision (POST /hooks/lorairo-memory) - For future

Implementation Phase

1. LTM search - Implementation patterns
2. Web docs - API details
3. Code integration (Serena tools)
4. Store knowledge - After completion

Code Search vs OpenClaw LTM

Code Search (fast) - Use for:

• Symbol search (Grep/Glob)
• File structure exploration
• Current implementation details

OpenClaw LTM (1-3s) - Use for:

• Design pattern search
• Long-term memory
• Strategic decisions
• Cross-project knowledge

Combined Workflow

1. docs/decisions/: Check past design decisions
2. OpenClaw: Search past designs (ltm_search.py)
3. Web: Research library (WebSearch)
4. Code: Implement with Grep/Glob/Read/Edit
5. docs/lessons-learned.md: Record lessons
6. OpenClaw: Store knowledge (POST /hooks/lorairo-memory)

LoRAIro-Specific Usage

Design Decisions to Store

• Architecture patterns (Repository, Service Layer, Direct Widget Communication)
• Technical choices (SQLAlchemy, PySide6, pytest rationale)
• Performance improvements (caching, async decisions)
• Refactoring (intent and effects)

Libraries to Research

• PySide6: Signal/Slot, QThread, Qt Designer
• SQLAlchemy: ORM, transactions, migrations
• pytest: Fixtures, mocks, parametrization
• Pillow: Image processing, metadata

Query Examples (OpenClaw LTM)

# Widget patterns
python3 .agents/skills/lorairo-mem/scripts/ltm_search.py <<'JSON'
{"limit": 5, "filters": {"tags": ["widget", "signal-slot", "direct-communication"]}}
JSON

# Repository patterns
python3 .agents/skills/lorairo-mem/scripts/ltm_search.py <<'JSON'
{"limit": 5, "filters": {"tags": ["repository-pattern", "sqlalchemy"]}}
JSON

# Testing patterns
python3 .agents/skills/lorairo-mem/scripts/ltm_search.py <<'JSON'
{"limit": 5, "filters": {"tags": ["pytest", "testing"]}}
JSON

Performance Characteristics

Operation	Tool	Time
LTM search	ltm_search.py	2-5s
LTM write	POST /hooks/lorairo-memory	1-3s
Web search	WebSearch	2-5s
Code search	Grep/Glob/Read	0.1-0.3s

Examples

See examples.md for detailed scenarios.

Reference

See reference.md for OpenClaw LTM + WebSearch reference.