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Operate agent memory

Skill hiteshbandhu/skills-i-use/skills/ai-engineer-talks/operate-agent-memory

Runs checklists and workflows for agent context engineering — separating context from memory, smart truncation, sub-agent offload, and long-session evals. Use when the user hits context limits on observability or trace-heavy agents, designs multi-turn memory, or says "context management", "escape context window", "agent forgets follow-ups".From its SKILL.md

Install

npx -y skills add hiteshbandhu/skills-i-use --skill operate-agent-memory

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
runs commandsInstructs the agent to run 3 commands, including `cp -r skills/operate-agent-memory ~/.claude/skills/` and 2 more.

SKILL.md

2.2 KB, 495 tokens by cl100k_base, as published. Nobody here has run it

Operate agent memory

Action playbook from Sally-Ann Delucia (Arize Alex). Do not summarize the talk — pick a workflow and execute it.

Supporting files:

workflows.md — workflows A–E
source-index.md — talk → learnings

Optional deliverables: {SKILL_OUTPUT_DIR}/operate-agent-memory/

Step 0 — Pick workflow

What is the user trying to do?
├─ Diagnose context death spiral (retry adds data)     → A
├─ Design in-window vs memory split                    → B
├─ Implement truncation + retrievable store              → C
├─ Add long-session regression evals                     → D
└─ Split main vs sub-agent context budgets               → E

Stop summarizing once a workflow is identified — run its checklist.

Install

cp -r skills/operate-agent-memory ~/.claude/skills/
cp -r skills/operate-agent-memory ~/.cursor/skills/
cp -r skills/operate-agent-memory ~/.codex/skills/

Source: ingest-into-skills playlists/memory-ai-engineer/.

Cross-cutting rules

Rule	Source
Context = what the model sees; memory = what survives outside	[src-001 @ 7:48]
Agents fail on context, not prompts	[src-001 @ 14:14]
Naive head truncation breaks follow-ups	[src-001 @ 5:18]
Uncontrolled summarization is unreliable	[src-001 @ 6:15]
Long sessions fail late — eval turn N+1	[src-001 @ 8:44]

Output to user

Name the workflow (A–E) and deliverable
Save artifacts under ./skill-outputs/operate-agent-memory/ when useful
Do not auto-commit

Invocation examples

@operate-agent-memory our agent loses thread on turn 12
design sub-agents for heavy trace search
long-session eval harness for support copilot

What ships with it: 3 files

3.9 KB alongside SKILL.md

Gives 0 of the 12 instructions most memory context skills give in 495 tokens

Counted across 754 of the 1,056 authors here whose files we hold, read 2026-09-06

Preserve existing content structurein 15 of 754, across 9 files
Front-load the leading wordin 14 of 754, across 10 files
Update existing entries instead of duplicatingin 14 of 754, across 7 files
Keep CLAUDE.md under one hundred linesin 14 of 754, across 12 files
Read CLAUDE.md at the project rootin 14 of 754
Keep each meaning in a single source of truthin 12 of 754, across 8 files
Redact sensitive information before committingin 11 of 754, across 4 files
Scan for all CLAUDE.md filesin 11 of 754, across 7 files
Use frontmatter for metadata on filesin 10 of 754, across 3 files
Repeat user interactions 10 timesin 10 of 754, across 4 files
Write the CLAUDE.md file into the target folderin 10 of 754, across 8 files
Use memlab to process snapshotsin 9 of 754, across 3 files

Said here and by no other author read

Pick a workflow from the list
Run the selected workflow checklist
Save artifacts under the output directory

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.