About this skill
The History skill provides AI agents with the capability to access and interact with a user's archive of past analytical work. Its primary purpose is to enable users to quickly recall previous analyses, find specific findings, and leverage prior work as a foundation for new tasks. An agent can list recent analyses, display full details for a specific analysis by ID, or search across various fields like title, question, key findings, and tags. This helps prevent redundant work and ensures continuity in analytical projects. Users can invoke this skill at the start of a session for context, when framing a new question to check for similar existing analyses, or simply to review their analytical journey. The skill guides the agent to load data from a `.knowledge/analyses/index.yaml` file and then execute commands based on user input, filtering by dataset or displaying all analyses. It also offers contextual suggestions after displaying history, such as re-running analyses or building on specific findings, enhancing the interactive experience. The skill also handles edge cases such as an empty archive or no matching search results, providing appropriate feedback to the user.
Best use case
The primary use case is for data analysts, researchers, or anyone frequently performing analyses with an AI agent who needs to manage and leverage their past work effectively. It helps users maintain a coherent history of their investigations, quickly retrieve insights, and avoid starting from scratch, thereby improving productivity and ensuring consistency across projects.
## Purpose
The user should expect to receive a structured list of recent or matching past analyses, or detailed information about a specific archived analysis, facilitating recall and continuity of work.
Practical example
Example input
/history search=customer churn analysis
Example output
Showing 3 of 50 analyses matching 'customer churn analysis': Date Title Level Findings Dataset ---------- -------------------------- ------- --------- ------------ 2023-11-15 Q4 Customer Churn Drivers High 5 Sales_Data_Q4 2023-09-01 Customer Churn Prediction Medium 3 Sales_Data_Q3 2023-07-20 Churn Rate Reduction Ideas Low 2 Marketing_Data
When to use this skill
- When the user explicitly asks for their analysis history or prior work.
- At the beginning of an AI agent session to provide context on previous tasks.
- Before starting a new analysis to check if similar investigations have already been conducted.
- When needing to recall specific findings or outputs from a past project.
When not to use this skill
- When the user needs to perform a completely new analysis without any historical context.
- When the user is looking for real-time data or information not contained in the archive.
- When the user has no existing analysis archive, as the skill would return no results.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/history/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Skill: History Compares
| Feature / Agent | Skill: History | Standard Approach |
|---|---|---|
| Platform Support | Claude | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | easy | N/A |
Frequently Asked Questions
What does this skill do?
## Purpose
Which AI agents support this skill?
This skill is designed for Claude.
How difficult is it to install?
The installation complexity is rated as easy. You can find the installation instructions above.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
Best AI Skills for Claude
Explore the best AI skills for Claude and Claude Code across coding, research, workflow automation, documentation, and agent operations.
AI Agent for Product Research
Browse AI agent skills for product research, competitive analysis, customer discovery, and structured product decision support.
ChatGPT vs Claude for Agent Skills
Compare ChatGPT and Claude for AI agent skills across coding, writing, research, and reusable workflow execution.
SKILL.md Source
# Skill: History
## Purpose
Browse and search past analyses from the analysis archive. Helps users
recall what they've analyzed before, find prior findings, and build on
previous work.
## When to Use
- User says `/history` or "what have I analyzed before?"
- At session start, to provide context on prior work
- When framing a new question, to check if similar analysis exists
## Invocation
`/history` — list recent analyses (last 10)
`/history {id}` — show full details for a specific analysis
`/history search={term}` — search by title, question, or tags
`/history --all` — list all analyses across all datasets
`/history dataset={id}` — filter to a specific dataset
## Instructions
### Step 1: Load Archive
1. Read `.knowledge/analyses/index.yaml`
2. If empty: "No analyses archived yet. Complete an analysis and it will appear here."
### Step 2: Execute Command
**List recent (`/history`):**
- Filter to active dataset (unless `--all` flag)
- Sort by date descending
- Show last 10 as a table: date, title, level, key finding count, dataset
- Show total count: "Showing 10 of {total} analyses."
**Show specific (`/history {id}`):**
- Find entry by ID in index
- Display: title, date, question, level, all key findings, metrics used,
agents used, output files, tags, confidence, recommendations
- If output files exist, offer: "Want to review the full analysis?"
**Search (`/history search={term}`):**
- Search across: title, question, key_findings, tags (case-insensitive)
- Display matching entries as a table
- If no matches: "No analyses match '{term}'. Try broader terms."
**All datasets (`/history --all`):**
- Include dataset_id column in output
- Sort by date descending across all datasets
### Step 3: Contextual Suggestions
After displaying history:
- "Want to re-run this analysis with fresh data?"
- "Want to build on finding #{n}?"
- If recent analysis was partial: "This analysis was incomplete. Resume with `/resume-pipeline`."
## Edge Cases
- **No active dataset:** Show all analyses or prompt to connect
- **Archive file missing:** Create empty index
- **Analysis output files deleted:** Note "output files no longer available"
- **Very long history (>100):** Paginate, show 20 at a timeRelated Skills
Skill: Explore Data
## Purpose
dcf
Discounted cash flow valuation with sensitivity analysis
notebooklm-research
Full-autopilot AI research agent powered by Google NotebookLM (notebooklm-py v0.3.4). Ingests sources (URL, text, PDF, DOCX, YouTube, Google Drive), runs deep web research, asks cited questions, and generates 10 native artifact types (audio podcast, video, cinematic video, slide deck, report, quiz, flashcards, mind map, infographic, data table, study guide). Produces original content drafts via Claude, with optional publishing to social platforms via threads-viral-agent integration. Use this skill when the user mentions: NotebookLM, research with sources, create notebook, generate podcast from articles, turn research into content, trending topic research, research pipeline, source-based analysis, cited research answers, generate slides, generate quiz, make flashcards, deep web research, create infographic, compare sources, research report, study guide, source analysis, or knowledge synthesis.
RLM (Recursive Language Model) Skill
The RLM (Recursive Language Model) Skill enables AI agents to process extremely large contexts (10M+ tokens) by recursively chunking, processing, and aggregating results, effectively overcoming context window limitations.
q
Fast SQLite-based vault search using FTS5 full-text search index
nblm
This skill allows AI agents, particularly Claude Code, to directly query and manage your Google NotebookLM notebooks, providing source-grounded and citation-backed answers from Gemini.
lastXdays
Researches any given topic across Reddit, X (Twitter), and the broader web within a custom, configurable time window, synthesizing findings and generating expert-level prompts.
tavily-search
Use Tavily API for real-time web search and content extraction. Use when: user needs real-time web search results, research, or current information from the web. Requires Tavily API key.
baidu-search
Search the web using Baidu AI Search Engine (BDSE). Use for live information, documentation, or research topics.
notebooklm
Google NotebookLM 非官方 Python API 的 OpenClaw Skill。支持内容生成（播客、视频、幻灯片、测验、思维导图等）、文档管理和研究自动化。当用户需要使用 NotebookLM 生成音频概述、视频、学习材料或管理知识库时触发。
openclaw-search
Intelligent search for agents. Multi-source retrieval with confidence scoring - web, academic, and Tavily in one unified API.
aisa-tavily
AI-optimized web search via AIsa's Tavily API proxy. Returns concise, relevant results for AI agents through AIsa's unified API gateway.