How do I install the Recall Conversations skill?

Run npx skillmds add tools-only/recall-conversations in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Recall Conversations safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Recall Conversations?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Recall Conversations free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Recall Conversations?

tools-only (@tools-only) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Recall Conversations by tools-only — Agent Skill

Name: Recall Conversations
Rating: 4.2 (5 reviews)
Author: tools-only

Recall Conversations

This skill should be used when the user asks to recall, search, or continue past conversations. Triggers on "what did we discuss", "continue where we left off", "remember when", "as I mentioned", "you suggested", "we decided", "search my conversations", "find the conversation where", "what did we work on". Also triggers on implicit signals like past-tense references ("the bug we fixed"), possessives without context ("my project"), or assumptive questions ("do you remember").

tools-only 77f6ce0 3 files · 8.0 KB Updated Sep 9, 2026 7 repo stars

File contents

Tools

Two scripts retrieve data. For full option catalogs, load references/tool-reference.md.

recent_chats.py — retrieve recent sessions:

python3 ${CLAUDE_PLUGIN_ROOT}/skills/recall-conversations/scripts/recent_chats.py --n 3

search_conversations.py — keyword search across all sessions:

python3 ${CLAUDE_PLUGIN_ROOT}/skills/recall-conversations/scripts/search_conversations.py --query "keyword"

Workflow

Identify the lens from user intent:

User Says	Lens
"where were we", "recap"	restore-context
"what I learned", "reflect"	extract-learnings
"gaps", "struggling"	find-gaps
"mentor", "review process"	review-process
"retro", "project review"	run-retro
"decisions", "CLAUDE.md"	extract-decisions
"bad habits", "antipatterns"	find-antipatterns

Load references/lenses.md for per-lens parameters, core questions, and supplementary search patterns.

Gather context using lens-appropriate tools:
- For recent context: recent_chats.py --n N
- For keyword search: search_conversations.py --query "keywords"
Apply lens questions to analyze the retrieved conversations.
Deepen the search if initial results are insufficient:
- Retrieve more sessions: --n 20
- Search for specific terms that surfaced
- Filter by project: --project projectname
- If 2 rounds of deepening yield no new relevant sessions, synthesize from available data.

Query Construction

Search terms should be content-bearing words that discriminate between sessions — high information value words that are rare enough to rank relevant sessions above irrelevant ones. BM25 ranking (when FTS5 is available) weights rare terms higher automatically.

Include: specific nouns, technologies, concepts, project names, domain terms, unique phrases. More terms improve ranking precision.

Exclude: generic verbs ("discuss", "talk"), time markers ("yesterday"), vague nouns ("thing", "stuff"), meta-conversation words ("conversation", "chat") — these appear in nearly every session and add noise rather than signal.

Algorithm:

Extract substantive keywords from user request
If 0 keywords, ask for clarification ("Which project specifically?")
If 1+ specific terms, search with those terms; use --project to narrow scope

Synthesis

Principles

Prioritize significance — 3-5 key findings, not exhaustive lists
Be specific — file paths, dates, project names
Make it actionable — every finding suggests a response
Show evidence — quotes or references
Keep it scannable — clear structure, no walls of text

Structure

## [Analysis Type]: [Scope]

### Summary
[2-3 sentences]

### Findings
[Organized by whatever fits: categories, timeline, severity]

### Patterns
[Cross-cutting observations]

### Recommendations
[Actionable next steps]

Length

Default: 300-500 words. Expand only when data warrants it.

---name: recall-conversationsdescription: This skill should be used when the user asks to recall, search, or continue past conversations. Triggers on "what did we discuss", "continue where we left off", "remember when", "as I mentioned", "you suggested", "we decided", "search my conversations", "find the conversation where", "what did we work on". Also triggers on implicit signals like past-tense references ("the bug we fixed"), possessives without context ("my project"), or assumptive questions ("do you remember").--- ## Tools Two scripts retrieve data. For full option catalogs, load `references/tool-reference.md`. **recent_chats.py** — retrieve recent sessions: ```bash python3 ${CLAUDE_PLUGIN_ROOT}/skills/recall-conversations/scripts/recent_chats.py --n 3 ``` **search_conversations.py** — keyword search across all sessions: ```bash python3 ${CLAUDE_PLUGIN_ROOT}/skills/recall-conversations/scripts/search_conversations.py --query "keyword" ``` --- ## Workflow 1. **Identify the lens** from user intent: | User Says | Lens | |-----------|------| | "where were we", "recap" | restore-context | | "what I learned", "reflect" | extract-learnings | | "gaps", "struggling" | find-gaps | | "mentor", "review process" | review-process | | "retro", "project review" | run-retro | | "decisions", "CLAUDE.md" | extract-decisions | | "bad habits", "antipatterns" | find-antipatterns | Load `references/lenses.md` for per-lens parameters, core questions, and supplementary search patterns. 2. **Gather context** using lens-appropriate tools: - For recent context: `recent_chats.py --n N` - For keyword search: `search_conversations.py --query "keywords"` 3. **Apply lens questions** to analyze the retrieved conversations. 4. **Deepen the search** if initial results are insufficient: - Retrieve more sessions: `--n 20` - Search for specific terms that surfaced - Filter by project: `--project projectname` - If 2 rounds of deepening yield no new relevant sessions, synthesize from available data. --- ## Query Construction Search terms should be content-bearing words that discriminate between sessions — high information value words that are rare enough to rank relevant sessions above irrelevant ones. BM25 ranking (when FTS5 is available) weights rare terms higher automatically. **Include:** specific nouns, technologies, concepts, project names, domain terms, unique phrases. More terms improve ranking precision. **Exclude:** generic verbs ("discuss", "talk"), time markers ("yesterday"), vague nouns ("thing", "stuff"), meta-conversation words ("conversation", "chat") — these appear in nearly every session and add noise rather than signal. **Algorithm:** 1. Extract substantive keywords from user request 2. If 0 keywords, ask for clarification ("Which project specifically?") 3. If 1+ specific terms, search with those terms; use `--project` to narrow scope --- ## Synthesis ### Principles 1. **Prioritize significance** — 3-5 key findings, not exhaustive lists 2. **Be specific** — file paths, dates, project names 3. **Make it actionable** — every finding suggests a response 4. **Show evidence** — quotes or references 5. **Keep it scannable** — clear structure, no walls of text ### Structure ```markdown ## [Analysis Type]: [Scope] ### Summary [2-3 sentences] ### Findings [Organized by whatever fits: categories, timeline, severity] ### Patterns [Cross-cutting observations] ### Recommendations [Actionable next steps] ``` ### Length Default: 300-500 words. Expand only when data warrants it.

Frequently asked questions