Recall Skill for Claude
Query the memory system for relevant learnings from past sessions
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/parcadei/Continuous-Claude-v3 --skill recallThe instruction itself
7 sections, as written by the authorRecall - Semantic Memory Retrieval
Query the memory system for relevant learnings from past sessions.
Usage
/recall <query>
Examples
/recall hook development patterns
/recall wizard installation
/recall TypeScript errors
What It Does
- Runs semantic search against stored learnings (PostgreSQL + BGE embeddings)
- Returns top 5 results with full content
- Shows learning type, confidence, and session context
Execution
When this skill is invoked, run:
cd $CLAUDE_OPC_DIR && PYTHONPATH=. uv run python scripts/core/recall_learnings.py --query "<ARGS>" --k 5
Where <ARGS> is the query provided by the user.
Output Format
Present results as:
## Memory Recall: "<query>"
### 1. [TYPE] (confidence: high, id: abc123)
<full content>
### 2. [TYPE] (confidence: medium, id: def456)
<full content>
Options
The user can specify options after the query:
--k N- Return N results (default: 5)--vector-only- Use pure vector search (higher precision)--text-only- Use text search only (faster)
Example: /recall hook patterns --k 10 --vector-only
Other skills for the same job
different authors, same section of the catalogue
Build agent-facing web experiences with ATXP-based authentication, following the ClawDirect pattern. Use this skill when building websites that AI agents interact with via MCP tools, implementing cookie-based agent auth, or creating agent skills for web apps. Provides templates using @longrun/turtle, Express, SQLite, and ATXP.
Search claude-mem's persistent cross-session memory database. Use when user asks "did we already solve this?", "how did we do X last time?", or needs work from previous sessions.
Inspect and explain conversations in the local Deep Agents Code SQLite session store. Use as a fallback when LangSmith trace tooling is unavailable, for offline or untraced sessions, or when asked to identify or summarize a local dcode thread, inspect checkpoint metadata, list recent local threads, or parse ~/.deepagents/.state/sessions.db and a thread UUID or prefix.
Use when you need state across calls — building env vars, navigating with cd, driving REPLs (python -i, mysql, psql, node), or responding to interactive prompts (sudo password, ssh host-key confirmation, mysql connection). Teaches the prompt-sentinel exec pattern (default mode), raw I/O for REPLs (raw_send=True then read_only=True), the one-in-flight-per-session rule, and the close-or-leak-against-the-cap discipline. Bash on macOS — never zsh; explicit shell=/bin/zsh is rejected. Read before calling terminal_pty_open.
Help developers build third-party tools that import, inspect, migrate, or analyze DeepChat data. Use when Codex needs to work with DeepChat provider configuration, model configuration, MCP/app settings, sessions, messages, legacy chat data, `agent.db`, `chat.db`, SQLCipher encrypted SQLite, Electron safeStorage wrapped passwords, Tauri importers, or native macOS/Windows/Linux data access.
统一管理多智能体角色的团队协作框架，支持智能体动态组合、灵活协作和扩展新角色。智能体本质上是"角色定义"，可以根据任务需求灵活组建团队，实现从会议决策到系统构建的完整能力。智能体角色明确分工：有干活的、有指挥的、有挑毛病的，能实时看到沟通过程，共享数据库记忆，确保上下文一致。
>- Corrects speech-to-text transcription errors using dictionary rules and Claude's built-in AI (no external API key required — Native AI Correction is the DEFAULT). Stage 3 API is a backup for automation without Claude Code. Builds personalized correction databases that learn from each fix, auto-loads person-name ASR variants from your people roster, and reads per-domain context files that prime the AI pass for context-dependent homophones. Triggers when working with ASR/STT output containing recognition errors, homophones, garbled technical terms, person-name errors, or Chinese/English mixed content. Also triggers on requests to clean up meeting notes, lecture transcripts, interview recordings, or any text produced by speech recognition. Use this skill even when the user just says "fix this transcript", "clean up these meeting notes", or mentions garbled names without invoking ASR specifically.
>- recon (find unsafe SQL construction sites), batched verify (trace user input to those sites in parallel subagents, 3 sites each), and merge (consolidate batch results). Covers string concat, f-strings, unsafe ORM methods, and dynamic identifiers. Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/sqli-results.md. Use when asked to find SQLi or database injection bugs.
How to use it
Take parcadei/recall from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.