paddleocr-doc-parsing
Use this skill to extract structured Markdown/JSON from PDFs and document images—tables with cell-level precision, formulas as…
You MUST invoke this skill before saying "I don't know," guessing, or treating any topic as new, no matter how trivial the question seems. It supplements other memory systems, which only hold partial records. Searching past conversations is the only way to recover what was
Claude Code
$ npx -y skills add obra/episodic-memory --skill remembering-conversations --agent claude-code
Codex
$ npx skills add obra/episodic-memory --skill remembering-conversations -a codex -y
Cursor
$ npx skills add obra/episodic-memory --skill remembering-conversations -a cursor -y
Gemini CLI
$ npx skills add obra/episodic-memory --skill remembering-conversations -a gemini-cli -y
GitHub Copilot
$ npx skills add obra/episodic-memory --skill remembering-conversations -a github-copilot -y
OpenCode
$ npx skills add obra/episodic-memory --skill remembering-conversations -a opencode -y
Windsurf
$ npx skills add obra/episodic-memory --skill remembering-conversations -a windsurf -y
name: remembering-conversations description: You MUST invoke this skill before saying "I don't know," guessing, or treating any topic as new, no matter how trivial the question seems. It supplements other memory systems, which only hold partial records. Searching past conversations is the only way to recover what was actually said.
**Core principle:** Search before reinventing. Searching costs nothing; reinventing or repeating mistakes costs everything.
**YOU MUST search historical memory for any historical search.**
Announce: "Searching past conversations for [topic]."
Use the Task tool with `subagent_type: "search-conversations"`:
Task tool: description: "Search past conversations for [topic]" prompt: "Search for [specific query or topic]. Focus on [what you're looking for - e.g., decisions, patterns, gotchas, code examples]." subagent_type: "search-conversations"
If a `search-conversations` agent is available, dispatch it with the same prompt. If not, use the MCP tools directly:
1. Search with the episodic-memory `search` tool 2. Read the top 2-5 results with the episodic-memory `read` tool 3. Synthesize findings in your response 4. Include source pointers so the user can inspect the original conversations
The search workflow will: 1. Search with the `search` tool 2. Read top 2-5 results with the `read` tool 3. Synthesize findings (200-1000 words) 4. Return actionable insights + sources
**Saves 50-100x context vs. loading raw conversations.**
Use the MCP tools directly unless a local search agent is available:
1. Search with the episodic-memory `search` tool 2. Read the top 2-5 results with the episodic-memory `read` tool 3. Synthesize findings in your response 4. Include source pointers so the user can inspect the original conversations
Use this whenever the current task would benefit from information you may have learned before, even if the user did not explicitly ask you to search.
**When past experience may help:**
**When you're stuck:**
**When historical signals are present:**
**Before answering from uncertainty:**
**Don't search first:**
Use these directly when a search agent is unavailable or the current harness does not support agent dispatch:
When using MCP tools directly, keep context small: search first, then read only the top 2-5 relevant conversations or line ranges.
See MCP-TOOLS.md for complete API reference if needed for advanced usage.
Semantic search for Claude Code, Codex, Cursor, opencode, and Oh My Pi (OMP) conversations. Remember past discussions, decisions, and patterns.
Use this skill to extract structured Markdown/JSON from PDFs and document images—tables with cell-level precision, formulas as…
Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps,…