Neat-Freak Knowledge Closeout
KKKKhazix/khazix-skills
Brings project docs, agent rule files, authorized memory and leftover workspace files back in line with what the code and runtime actually do at the end of a work session.
A skill your agent uses when designing, reviewing, or paying for an agent memory system — adding memory to an agent, choosing between long-context / RAG / graph / agentic memory, auditing what a…
Install Memory Engineering
$ npx skills add alirezarezvani/claude-skills --skill memory-engineering -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install alirezarezvani/claude-skills memory-engineering --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering/memory-engineering/skills/memory-engineering .claude/skills/memory-engineering && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "memory-engineering" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/memory-engineering/skills/memory-engineering into .claude/skills/memory-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-engineering", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/alirezarezvani/claude-skills/tree/main/engineering/memory-engineering/skills/memory-engineeringType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add alirezarezvani/claude-skills --skill memory-engineering -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install alirezarezvani/claude-skills memory-engineering --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/engineering/memory-engineering/skills/memory-engineering .agents/skills/memory-engineering && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "memory-engineering" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/memory-engineering/skills/memory-engineering into .agents/skills/memory-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-engineering", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alirezarezvani/claude-skills --skill memory-engineering -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install alirezarezvani/claude-skills memory-engineering --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/engineering/memory-engineering/skills/memory-engineering .cursor/skills/memory-engineering && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "memory-engineering" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/memory-engineering/skills/memory-engineering into .cursor/skills/memory-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-engineering", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/alirezarezvani/claude-skills.git --path engineering/memory-engineering/skills/memory-engineering--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add alirezarezvani/claude-skills --skill memory-engineering -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install alirezarezvani/claude-skills memory-engineering --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/engineering/memory-engineering/skills/memory-engineering .gemini/skills/memory-engineering && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "memory-engineering" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/memory-engineering/skills/memory-engineering into .gemini/skills/memory-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-engineering", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install alirezarezvani/claude-skills memory-engineeringInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add alirezarezvani/claude-skills --skill memory-engineering -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/engineering/memory-engineering/skills/memory-engineering .github/skills/memory-engineering && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "memory-engineering" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/memory-engineering/skills/memory-engineering into .github/skills/memory-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-engineering", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alirezarezvani/claude-skills --skill memory-engineering -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install alirezarezvani/claude-skills memory-engineering --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/engineering/memory-engineering/skills/memory-engineering .opencode/skills/memory-engineering && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "memory-engineering" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/memory-engineering/skills/memory-engineering into .opencode/skills/memory-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-engineering", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
memory-engineering- GitHub stars
- 28k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 617 words
- Files
- 12 (incl. scripts, references, assets)
- Skills in repo
- 342
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when designing, reviewing, or paying for an agent memory system — adding memory to an agent, choosing between long-context / RAG / graph / agentic memory, auditing what a…
- Works in 7 steps: Never quote accuracy without cost per… → Never return a "best" memory system —… → Never auto-merge contradictions. The… → …
- Paying for an agent memory system — adding memory to an agent
- SKILL.md covers What this does, The four lenses, Workflow and Hard rules, plus 3 more sections
- Runs Python scripts from its folder; calls python
What it does
Memory Engineering is an agent skill from alirezarezvani/claude-skills. Use when designing, reviewing, or paying for an agent memory system — adding memory to an agent, choosing between long-context / RAG / graph / agentic memory, auditing what a CLAUDE.md or memory directory actually holds, deciding what to keep and what to expire, or when a memory store keeps growing and nobody has said what leaves it. Prices the write path, picks which cost to pay, classifies records as facts / skills / logs, and refuses a design that has no forgetting policy.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including scripts, reference files and assets (for example `assets/forgetting_policy_template.md`, `assets/memory_design_spec.example.json` and `assets/memory_engineer_worksheet.md`).
It sits in Agent Workflows, covering Agent memory and Agent instruction files. The repository describes itself as: 380 Claude Code skills & agent skills & plugins (30+ Agents, 70+ custom commands, 380+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8… The licence is MIT.
When your agent uses it
- Paying for an agent memory system — adding memory to an agent
- Choosing between long-context / RAG / graph / agentic memory
- Auditing what a CLAUDE.md
- Memory directory actually holds
Example prompts
- “/memory-engineering”
Requirements
- Python 3
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- Never quote accuracy without cost per correct answer.
- Never return a "best" memory system — name the cost the choice makes you pay.
- Never auto-merge contradictions. The system surfaces; the human decides.
- Never call a design done without a forgetting rule. No evaluated system provides one by default.
- Never schedule a pass not yet run by hand.
- Report findings as findings. A non-zero exit is a result to surface, not an error to swallow.
- Attribute every number with its confidence level. Vendor customer figures are testimonials, not benchmarks.
What it can do on your machine
Read from SKILL.md and the folder at commit 19392f7. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 4 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
x.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Memory Engineering loads about 1.7k tokens when it runs, and up to ~9.5k if it reads all its reference files. Until then it costs about 125 tokens; SKILL.md has 617 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 617 words, ~1,669 tokens.
Download SKILL.mdSave it as
.claude/skills/memory-engineering/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.- name
- memory-engineering
- description
- Use when designing, reviewing, or paying for an agent memory system — adding memory to an agent, choosing between long-context / RAG / graph / agentic memory, auditing what a CLAUDE.md or memory directory actually holds, deciding what to keep and what to expire, or when a memory store keeps growing and nobody has said what leaves it. Prices the write path, picks which cost to pay, classifies records as facts / skills / logs, and refuses a design that has no forgetting policy.
- argument-hint
- [optional: path to a memory dir, design spec JSON, or a question]
- license
- MIT
- metadata.version
- 1.0.0
- metadata.build_pattern
- Four-lens synthesis (Stanford / Microsoft / Anthropic / Nvidia) + 4 deterministic stdlib scripts, with a blocking forgetting gate
- metadata.distinct_from
- llm-wiki (maintains one specific markdown vault; this audits and prices any memory system); skillopt-sleep (runs a nightly consolidation loop; this decides…
Memory Engineering — engineer the forgetting, not just the remembering
Portability: 4 stdlib scripts, no APIs/LLM calls/network. They measure and gate; you decide.
What this does
Anyone can give an agent memory: vector store, pipe in the history, retrieve top-k. That works until the history outgrows the context window, the write path costs more than every query it serves, and the store fills with stale state nobody removes. Memory is not a bucket — it is a system with a metabolism.
The shift: a storer optimizes what a system remembers; a memory engineer optimizes what it forgets. The problem was never that an agent forgets — it is that it never forgets on purpose.
The four lenses
| Lens | Question | The finding that hurts |
|---|---|---|
| Stanford | What does remembering cost? | Construction energy exceeds total query energy across 300 queries. The tuned half is the smaller half. |
| Microsoft | What is worth keeping? | More raw memory can make an agent worse. Keep facts and skills; drop the events. |
| Anthropic | Who controls what it keeps? | A wrong memory does not fail once — it persists into every future session that reads it. |
| Nvidia | Where does it hit hardware? | It is all KV cache in HBM. Construction is prefill-heavy and stalls the query a user is waiting on. |
Workflow
# 1 - Price it first. Never quote a quality number without a cost number.
python scripts/memory_cost_profiler.py --print-sample-spec > workload.json
python scripts/memory_cost_profiler.py --spec workload.json
# 2 - Pick which cost to pay. No "best" verdict; on a tie it asks, exit 2.
python scripts/memory_architecture_picker.py --constraints workload.json
# 3 - Audit what the store actually holds (skip if greenfield).
python scripts/memory_density_auditor.py --dir ~/.claude/memory
# 4 - Gate on forgetting. Exit 4 is a stop, not a suggestion.
python scripts/forgetting_policy_linter.py --policy design.json
# 5 - No command. Prove each pass by hand before scheduling it.Step 1 reports the construction/query split, cost per correct answer, and
amortization — if construction dominates, cut construction tokens before
touching retrieval. Step 2 names the cost the winning family makes you pay.
Step 3 classifies records FACT / SKILL / LOG / PROSE (LOG-HEAVY = archiving
events; PROSE-HEAVY = docs, not memory).
Step 4 is the gate: F1 (explicit forgetting rule) and F4 (contradictions surfaced, never auto-merged) are blocking. Retrofitting forgetting onto two years of records is a migration nobody does; auto-merging disagreeing memories destroys the evidence the conflict existed.
Step 5 has no script — prove each pass by hand, then automate. Run it once
against real history and ask whether it changed a decision. If not, scheduling
it only makes noise. Ship order: forgetting_policy_design.md §7.
Show full SKILL.md (287 more words)Show less
Hard rules
- Never quote accuracy without cost per correct answer.
- Never return a "best" memory system — name the cost the choice makes you pay.
- Never auto-merge contradictions. The system surfaces; the human decides.
- Never call a design done without a forgetting rule. No evaluated system provides one by default.
- Never schedule a pass not yet run by hand.
- Report findings as findings. A non-zero exit is a result to surface, not an error to swallow.
- Attribute every number with its confidence level. Vendor customer figures are testimonials, not benchmarks.
Scripts
| Script | Role | Exit codes |
|---|---|---|
scripts/memory_cost_profiler.py | Construction vs query split, cost per correct answer, amortization, co-location warning | 0 · 2 finding · 3 bad input |
scripts/memory_architecture_picker.py | Scores 4 families, disqualifies, names the cost, refuses to pick on a tie | 0 · 2 ambiguous · 3 bad input · 4 none viable |
scripts/memory_density_auditor.py | FACT/SKILL/LOG/PROSE, duplicates, staleness, density (--dir or --jsonl) | 0 dense · 2 finding · 3 bad input |
scripts/forgetting_policy_linter.py | The gate: 8 checks, F1 and F4 blocking | 0 PASS · 2 CONDITIONAL · 4 FAIL |
All support --output json and --sample (no input file needed).
References and assets
references/memory_cost_canon.md— construction dominance, energy per correct answer, the four families, ten recommendations (7 sources)references/what_to_keep.md— PlugMem and MEMENTO: facts over logs, density over volume (7 sources)references/memory_control_and_governance.md— memory as files, scope/audit/rollback, poisoning, reading vendor numbers (7 sources)references/forgetting_policy_design.md— forgetting mechanisms, contradiction discipline, KV cache, ship order (7 sources)assets/memory_engineer_worksheet.md— seven forcing questions with recommended answers + citations; walk one at a timeassets/memory_design_spec.example.json— one file covering every script's inputassets/forgetting_policy_template.md— fillable policy covering F1–F8
Provenance
Framing from "How to be a Memory Engineer" by @N01ennn; every
number is cited to a primary source instead, and two paraphrases are corrected — memory_cost_canon.md §2, memory_control_and_governance.md §4.
© alirezarezvani, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 11 other files (scripts, references, assets) in engineering/memory-engineering/skills/memory-engineering of alirezarezvani/claude-skills.
- SKILL.md
- assets/forgetting_policy_template.md
- assets/memory_design_spec.example.json
- assets/memory_engineer_worksheet.md
- references/forgetting_policy_design.md
- references/memory_control_and_governance.md
- references/memory_cost_canon.md
- references/what_to_keep.md
- scripts/forgetting_policy_linter.py
- scripts/memory_architecture_picker.py
- scripts/memory_cost_profiler.py
- scripts/memory_density_auditor.py
Open the folder on GitHubat commit 19392f7
Compare with similar skills
Memory Engineering next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Memory Engineering this skillalirezarezvani/claude-skills | 28k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Neat-Freak Knowledge CloseoutKKKKhazix/khazix-skills | 21k | — | ~1.9k | Automated safety check: Pass | MIT | |
| SkillOpt Sleep Cyclemicrosoft/SkillOpt | 18k | — | ~2.3k | Automated safety check: Pass | MIT | |
| CLAUDE.md Improveranthropics/claude-plugins-official | 38k | 5 repos | ~1.5k | Automated safety check: Pass | Apache-2.0 | |
| Harness Engineering10xChengTu/harness-engineering | 102 | 1 repos | ~1k | Automated safety check: Pass | None | |
| Codebase Analyzerseverity1/claude-code-auto-memory | 159 | — | ~1.5k | Automated safety check: Pass | MIT |
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microsoft/SkillOpt
Runs an on-demand or nightly sleep cycle that reviews past Claude Code sessions and proposes validated updates to CLAUDE.md and skills.
- Official
CLAUDE.md Improver
anthropics/claude-plugins-official
Finds every CLAUDE.md file in a repository, scores each against quality criteria, shows a report, then makes targeted updates after you approve.
Harness Engineering
10xChengTu/harness-engineering
Set up and improve harness engineering (AGENTS.md, docs/, lint rules, eval systems, project-level prompt engineering) for AI-agent-friendly codebases.
Codebase Analyzer
severity1/claude-code-auto-memory
This skill should be used when the user asks to "initialize auto-memory", "create CLAUDE.md", "set up project memory", or runs the /auto-memory:init command.
Self Improving For Codex
GODGOD126/self-improving-for-codex
Build or maintain a Codex-native self-improving memory loop using global AGENTS.md, a persistent memories directory, and optional nightly refinement automation.
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Questions about Memory Engineering
What does Memory Engineering do?
A skill your agent uses when designing, reviewing, or paying for an agent memory system — adding memory to an agent, choosing between long-context / RAG / graph / agentic memory, auditing what a…. Memory Engineering is an agent skill from alirezarezvani/claude-skills.md or memory directory actually holds, deciding what to keep and what to expire, or when a memory store keeps growing and nobody has said what leaves it.
When should I use Memory Engineering?
Memory Engineering fits situations like: paying for an agent memory system — adding memory to an agent; choosing between long-context / RAG / graph / agentic memory; auditing what a CLAUDE.md; memory directory actually holds.
How do I install Memory Engineering in Claude Code?
Run `npx skills add alirezarezvani/claude-skills --skill memory-engineering -a claude-code`. Or copy the skill folder (engineering/memory-engineering/skills/memory-engineering in alirezarezvani/claude-skills) into .claude/skills/memory-engineering in your project. Claude Code loads it when a task matches its description.
How do I install Memory Engineering in Codex?
Run `npx skills add alirezarezvani/claude-skills --skill memory-engineering -a codex`. Or copy the skill folder (engineering/memory-engineering/skills/memory-engineering in alirezarezvani/claude-skills) into .agents/skills/memory-engineering in your project. Codex loads it when a task matches its description.
Can I use Memory Engineering in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add alirezarezvani/claude-skills --skill memory-engineering -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/memory-engineering, .gemini/skills/memory-engineering, .github/skills/memory-engineering and .opencode/skills/memory-engineering in your project.
What does Memory Engineering need to run?
Going by SKILL.md and its folder, Memory Engineering needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Memory Engineering access the network?
SKILL.md names 1 domain. As links in the text: x.com. This is read from the text; nothing was executed.
Is Memory Engineering safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Memory Engineering use?
Memory Engineering is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Memory Engineering use?
About 1.7k tokens (SKILL.md is roughly 6.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.8k tokens, read only when the agent opens those files.
What are the alternatives to Memory Engineering?
Skills that share tags, products or a category with Memory Engineering: Neat-Freak Knowledge Closeout (KKKKhazix/khazix-skills, 21k stars), SkillOpt Sleep Cycle (microsoft/SkillOpt, 18k stars), CLAUDE.md Improver (anthropics/claude-plugins-official, 38k stars) and Harness Engineering (10xChengTu/harness-engineering, 102 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Memory Engineering?
alirezarezvani (a GitHub user) maintains it in alirezarezvani/claude-skills, which has 27,829 GitHub stars. The repository holds 342 skills in this directory. The repository was last updated on August 30, 2026.
Source: alirezarezvani/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.