remember-conversation
Skill49 starsChanged 3 months agoMITView on GitHub
Review the current conversation and save key facts to long-term memory. Use when the user asks you to remember things from this chat, save important info, or "remember this for next time".
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 4 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 29
Use namespaced keys: identity.name, prefs.tone, projects.starter-kit.goal
- Distinctive: line 30
Keep values concise — one fact per save
- Distinctive: line 49
Don't save things the user explicitly told you not to remember
- Distinctive: line 50
Don't save passwords, API keys, or sensitive data ever
- Very specific: line 51
Don't assume — if unsure, ask first via ask_questions
- Distinctive: line 52
Don't dump the whole conversation into memory — be selective
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Remember Conversation
When to use
- User says "remember this", "save that", "for next time"
- End of a productive session where context should persist
- User shares personal info that will be relevant later (preferences, identity, projects)
Steps
- Identify save-worthy facts — review the conversation for:
- Identity: name, role, location, timezone, language preference
- Preferences: tone, formats, tools they like/dislike
- Projects: things they're working on, with dates and goals
- Relationships: people they mention (clients, colleagues)
- Decisions: choices made that should inform future advice
- Constraints: deadlines, budgets, requirements
- Skip ephemeral content — don't save:
- Specific questions that were answered (that's task context, not memory)
- Passing remarks that won't matter later
- Things you can re-derive from session messages
- Structure each fact — call
rememberfor each one:- Use namespaced keys:
identity.name,prefs.tone,projects.starter-kit.goal - Keep values concise — one fact per save
- Include a
descriptionso future searches make sense
- Use namespaced keys:
- Avoid duplicates — call
recallfirst to check if a similar key exists. Update viaremember(it upserts) rather than creating a duplicate key. - Confirm what was saved — use
show_data_tablelisting the keys you stored, with columns for key, value, and description. - Offer next steps via
offer_choices:- "Save more"
- "Review what's saved"
- "Forget specific items"
- "Done"
Style
- Neutral and factual. Don't editorialise on what the user said.
- One fact per save. Don't bundle multiple into a single value.
- Lowercase namespaced keys with dots (
prefs.theme.colour).
What not to do
- Don't save things the user explicitly told you not to remember
- Don't save passwords, API keys, or sensitive data ever
- Don't assume — if unsure, ask first via
ask_questions - Don't dump the whole conversation into memory — be selective
In this repository32 other files it gives its agents.
- AGENTS.mdAGENTS.md
- docs/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- agent-asks-tasksSkill
- caretakerSkill
- code-reviewSkill
- compare-optionsSkill
- create-reportSkill
- csv-analyseSkill
- document-qaSkill
- draft-emailSkill
- enrich-errorSkill
- extract-structured-dataSkill
- fact-checkSkill
- git-diff-summariserSkill
- librarian-curateSkill
- meeting-notesSkill
- morning-briefSkill
- plan-taskSkill
- reflectSkill
- reverieSkill
- review-code-securitySkill
- review-email-toneSkill
- review-outputSkill
- review-summary-faithfulnessSkill
- rewrite-for-audienceSkill
- route-findingSkill
- routine-health-checkSkill
- save-research-docSkill
- score-importanceSkill
- summarise-urlSkill
- web-researchSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

