memory-wrap
Skill8 starsChanged 41 days agoMITView on GitHub
At the end of a working session, find the decisions, facts and lessons worth keeping and save the ones the user approves to their imi knowledge base. Use when the user says "wrap up", "anything worth saving", "memory check", "let's close out", "save anything important", "before we go", or when a substantive session winds down after real decisions were made or new facts surfaced. Works with any imi domain.
In this repository7 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- domain-config-advisorSkill
- imi-onboardingSkill
- briefSkill
- constitution-reviewSkill
- what-changedSkill
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 3 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
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 17
All tools below are on the imi MCP server (possibly prefixed, e.g.
- Distinctive: line 18
If no imi tools are available, say so and stop.
- Very specific: line 22
Run 2–3 memory_recall queries on the main subjects, people and projects of the session, with record_kinds ["capture", "agent_memory"].
- Distinctive: line 50
Wait for the answer. Save nothing without it.
- Distinctive: line 68
Never try to mark memories confirmed yourself.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 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
Memory Wrap
Turn the durable part of this conversation into memory. The bar: would knowing this in six months change how the user works or decides? If yes, propose it. If it is marginal, skip it. This is not a log of the conversation.
All tools below are on the imi MCP server (possibly prefixed, e.g. mcp__imi__capture_thought).
If no imi tools are available, say so and stop.
1. Check what imi already knows
Run 2–3 memory_recall queries on the main subjects, people and projects of the session,
with record_kinds ["capture", "agent_memory"]. Do not read everything — just enough to avoid
proposing what is already stored. Keep each request_id.
2. Pick candidates (be skeptical)
- Decision — a concrete choice about the work, its direction or process.
- Fact — specific, non-obvious, new information about people, organizations, systems, commitments.
- Lesson — an insight, a named concept, a way of framing something that clicked.
Skip anything already stored, process chatter without a conclusion, things that only matter inside this conversation, and anything vague. Never propose secrets, credentials, access tokens, or large pasted code or transcripts.
Aim for 3–7. If you have 10+, tighten the filter. If there is nothing, say so — not every session produces durable knowledge.
3. Ask before saving
Found 4 things worth saving — "all", "none", or numbers (e.g. "1, 3"):
1. [DECISION] <one self-contained sentence> · tags: a, b, c
2. [FACT] ...
3. [LESSON] ...Wait for the answer. Save nothing without it.
4. Save what was approved
Choose the tool by shape:
- A few standalone items — one
capture_thoughtper item:content= the item, prefixed with its type ([DECISION], [FACT], [LESSON]) and expanded enough to stand alone months from now;tags= the proposed tags plus the type;source= "manual".deduped: truemeans imi already had it — nothing more to do. - An end-of-task batch (the session finished a defined piece of work) — one
memory_writebackwithmemory_payloadholdingdecisions,lessons,next_steps,unresolved_questionsas lists of strings, and anidempotency_keythat is stable for this session (e.g. the date plus a short slug), so a retry does not duplicate.
What you save is evidence, pending review (ADR-002): searchable immediately, but it only becomes instruction-grade when the user confirms it in imi's review queue. The user's "yes" here decides what is written, not what is trusted — say so in one line when reporting. Never try to mark memories confirmed yourself.
5. Report and close the loop
One line: how many saved, how many already present. Then call record_memory_usage for each
recall request_id, listing the record ids that changed what you proposed or skipped.
Tone
Housekeeping, not ceremony. The list should take ten seconds to review.
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.

