/arsave
AgentRecall full save — journal + palace + awareness + insights in one shot.
$ npx -y skills add Goldentrii/AgentRecall-X --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/arsave
Context preview
What this command does when you run it.
AgentRecall full save — journal + palace + awareness + insights in one shot.
Command definition
arsave.mddescription: "AgentRecall full save — journal + palace + awareness + insights in one shot."
/arsave — AgentRecall Full Save
One command to save everything. No long prompts needed.
When to Use
**Default: USE IT.** Most projects are long-term. Memory compounds — insights saved today prevent repeated mistakes and rebuild costs across future sessions.
**Skip /arsave only when** the session was truly throwaway:
- Pure Q&A with no decisions made
- Trivial one-off task that won't be revisited
- Nothing non-obvious happened worth recalling
What This Does
Runs the complete AgentRecall end-of-session flow:
1. **Gather** — review what happened this session 2. **Save** — one `session_end` call writes journal + awareness + consolidation 3. **Verify** — check that key content was promoted 4. **Git** — push to GitHub if user has configured it
Process
Step 1: Gather session context
**Start with machine-captured facts, not memory.** At long context windows your memory of early decisions is compressed and unreliable. Ground truth comes first:
1. **Read today's capture log** — `~/.agent-recall/projects/<slug>/journal/YYYY-MM-DD-log.md` (if it exists). This file contains incremental Q&A captures logged during the session. Pull out the key facts from it.
2. **Check git diff** — if in a git repo, run `git diff --stat HEAD` or `git log --oneline -5` to see what files actually changed.
3. **Supplement with memory** — now recall what happened that isn't in the log: decisions made in conversation, things we discussed but didn't act on, blockers identified, next steps.
Combine all three into a 2-3 sentence summary. The log anchors you; memory fills the gaps.
Step 1b: Capture intention on first save
Check whether this project already has an intention recorded:
grep -l "Intention:" ~/.agent-recall/projects/<slug>/palace/identity.md 2>/dev/null
**If NOT found** (this is the first save for this project, or intention was never captured):
Look at the earliest user messages in this conversation — where the user explained what they're trying to do, why they're starting this project, what problem they're solving, or what their goal is. Extract one clear sentence that captures the core WHY.
Write it to the identity file:
mkdir -p ~/.agent-recall/projects/<slug>/palace
# prepend to identity.md (or create it)
echo "**Intention:** <extracted intention sentence>" | cat - ~/.agent-recall/projects/<slug>/palace/identity.md 2>/dev/null > /tmp/identity-tmp.md && mv /tmp/identity-tmp.md ~/.agent-recall/projects/<slug>/palace/identity.md
Rules for extraction:
- One sentence, max ~20 words
- Capture the WHY and WHAT, not the HOW (e.g. "Build a pixel-perfect replica of novada.com as a deployable Next.js site")
- If no clear intention is detectable from the conversation (e.g. pure Q&A, no project context), skip — don't fabricate
**If already found**: skip this step entirely.
Step 2: Record corrections
If the human corrected your understanding during this session — "no not that", "I meant X not Y", "wrong priority" — record each significant correction:
check({
goal: "<what you originally understood>",
confidence: "high",
human_correction: "<what the human actually wanted>",
delta: "<the gap — e.g. 'assumed technical priority, human meant business priority'>"
})This feeds the predictive warning system. Future agents on this project will get `watch_for` warnings like: "You tend to misinterpret X — corrected N times."
If no corrections happened this session, skip this step.
Step 3: Save everything in one call
Call `session_end` with:
session_end({
summary: "<2-3 sentence session summary>",
insights: [
{
title: "<one-line insight>",
evidence: "<what happened that confirmed this>",
applies_when: ["keyword1", "keyword2"],
severity: "critical" | "important" | "minor"
}
// 1-3 insights max
],
trajectory: "<where is the work heading — one line>"
})This single call:
- Writes the daily journal entry
- Updates awareness with new insights (merge or add)
- Consolidates decisions/goals/blockers into palace rooms
- Archives demoted insights (not deleted — moved to awareness-archive.json)
Step 4: Verify promotion
After `session_end`, verify that content actually made it to the right places:
1. Call `recall(query="<key decision from today>")` — confirm it appears in palace results 2. Check the session_end response: `insights_processed` should match what you sent
If gaps found, use `remember` to manually save the missing content:
remember({
content: "<the missing decision/insight>",
context: "architecture decision" // hints the router
})Step 5: Output the save card
Render the following card. Replace all `<placeholders>` with real values from the session_end response and the actual project slug. Count the project's journal files to get the session number (`ls ~/.agent-recall/projects/<slug>/journal/*.md 2>/dev/null | wc -l`).
──────────────────────────────────────────────────────────────
AgentRecall ✓ Saved <project-slug> <YYYY-MM-DD> #<N>
──────────────────────────────────────────────────────────────
Intention palace/identity.md
└─ "<captured intention>" [captured] ← only if written this save
Journal ~/.agent-recall/projects/<slug>/journal/
└─ <YYYY-MM-DD>.md [written]
Awareness ~/.agent-recall/awareness.md
└─ <N> insights added (<M> total)
Palace ~/.agent-recall/projects/<slug>/palace/
├─ rooms/<room1>.md [updated]
├─ rooms/<room2>.md [updated]
└─ palace-index.json [reindexed]
Insights ~/.agent-recall/insights-index.json
└─ cross-project index updated
──────────────────────────────────────────────────────────────Omit
Read more
description: "AgentRecall full save — journal + palace + awareness + insights in one shot."
/arsave — AgentRecall Full Save
One command to save everything. No long prompts needed.
When to Use
**Default: USE IT.** Most projects are long-term. Memory compounds — insights saved today prevent repeated mistakes and rebuild costs across future sessions.
**Skip /arsave only when** the session was truly throwaway:
- Pure Q&A with no decisions made
- Trivial one-off task that won't be revisited
- Nothing non-obvious happened worth recalling
What This Does
Runs the complete AgentRecall end-of-session flow:
1. **Gather** — review what happened this session 2. **Save** — one `session_end` call writes journal + awareness + consolidation 3. **Verify** — check that key content was promoted 4. **Git** — push to GitHub if user has configured it
Process
Step 1: Gather session context
**Start with machine-captured facts, not memory.** At long context windows your memory of early decisions is compressed and unreliable. Ground truth comes first:
1. **Read today's capture log** — `~/.agent-recall/projects/<slug>/journal/YYYY-MM-DD-log.md` (if it exists). This file contains incremental Q&A captures logged during the session. Pull out the key facts from it.
2. **Check git diff** — if in a git repo, run `git diff --stat HEAD` or `git log --oneline -5` to see what files actually changed.
3. **Supplement with memory** — now recall what happened that isn't in the log: decisions made in conversation, things we discussed but didn't act on, blockers identified, next steps.
Combine all three into a 2-3 sentence summary. The log anchors you; memory fills the gaps.
Step 1b: Capture intention on first save
Check whether this project already has an intention recorded:
grep -l "Intention:" ~/.agent-recall/projects/<slug>/palace/identity.md 2>/dev/null
**If NOT found** (this is the first save for this project, or intention was never captured):
Look at the earliest user messages in this conversation — where the user explained what they're trying to do, why they're starting this project, what problem they're solving, or what their goal is. Extract one clear sentence that captures the core WHY.
Write it to the identity file:
mkdir -p ~/.agent-recall/projects/<slug>/palace # prepend to identity.md (or create it) echo "**Intention:** <extracted intention sentence>" | cat - ~/.agent-recall/projects/<slug>/palace/identity.md 2>/dev/null > /tmp/identity-tmp.md && mv /tmp/identity-tmp.md ~/.agent-recall/projects/<slug>/palace/identity.md
Rules for extraction:
- One sentence, max ~20 words
- Capture the WHY and WHAT, not the HOW (e.g. "Build a pixel-perfect replica of novada.com as a deployable Next.js site")
- If no clear intention is detectable from the conversation (e.g. pure Q&A, no project context), skip — don't fabricate
**If already found**: skip this step entirely.
Step 2: Record corrections
If the human corrected your understanding during this session — "no not that", "I meant X not Y", "wrong priority" — record each significant correction:
check({
goal: "<what you originally understood>",
confidence: "high",
human_correction: "<what the human actually wanted>",
delta: "<the gap — e.g. 'assumed technical priority, human meant business priority'>"
})This feeds the predictive warning system. Future agents on this project will get `watch_for` warnings like: "You tend to misinterpret X — corrected N times."
If no corrections happened this session, skip this step.
Step 3: Save everything in one call
Call `session_end` with:
session_end({
summary: "<2-3 sentence session summary>",
insights: [
{
title: "<one-line insight>",
evidence: "<what happened that confirmed this>",
applies_when: ["keyword1", "keyword2"],
severity: "critical" | "important" | "minor"
}
// 1-3 insights max
],
trajectory: "<where is the work heading — one line>"
})This single call:
- Writes the daily journal entry
- Updates awareness with new insights (merge or add)
- Consolidates decisions/goals/blockers into palace rooms
- Archives demoted insights (not deleted — moved to awareness-archive.json)
Step 4: Verify promotion
After `session_end`, verify that content actually made it to the right places:
1. Call `recall(query="<key decision from today>")` — confirm it appears in palace results 2. Check the session_end response: `insights_processed` should match what you sent
If gaps found, use `remember` to manually save the missing content:
remember({
content: "<the missing decision/insight>",
context: "architecture decision" // hints the router
})Step 5: Output the save card
Render the following card. Replace all `<placeholders>` with real values from the session_end response and the actual project slug. Count the project's journal files to get the session number (`ls ~/.agent-recall/projects/<slug>/journal/*.md 2>/dev/null | wc -l`).
──────────────────────────────────────────────────────────────
AgentRecall ✓ Saved <project-slug> <YYYY-MM-DD> #<N>
──────────────────────────────────────────────────────────────
Intention palace/identity.md
└─ "<captured intention>" [captured] ← only if written this save
Journal ~/.agent-recall/projects/<slug>/journal/
└─ <YYYY-MM-DD>.md [written]
Awareness ~/.agent-recall/awareness.md
└─ <N> insights added (<M> total)
Palace ~/.agent-recall/projects/<slug>/palace/
├─ rooms/<room1>.md [updated]
├─ rooms/<room2>.md [updated]
└─ palace-index.json [reindexed]
Insights ~/.agent-recall/insights-index.json
└─ cross-project index updated
──────────────────────────────────────────────────────────────Omit
Correction-first persistent memory for AI agents. MCP server + SDK + CLI. Compounds across sessions.
Repo: Goldentrii/AgentRecall-X
Other commands on agentrecall-x.
- /arrecall
AgentRecall on-demand recall — surface past fixes, decisions, and patterns mid-session without leaving your flow.
Open command - /arreflect
AgentRecall consolidation & reflection — periodic triage of recurring corrections; proposes rule changes, never applies them without the owner.
Open command - /arstart
AgentRecall session opener — status board (no args), project loader (<slug>), or cold-start bootstrap. Use first every session.
Open command

