Agent skill

Agent Memory Discipline

by sickn33 in sickn33/agentic-awesome-skills

Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards.

CC0-1.0Auto-check passedAgent Workflows

Install Agent Memory Discipline

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill agent-memory-discipline -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills agent-memory-discipline --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/agent-memory-discipline .claude/skills/agent-memory-discipline && rm -rf skills-src

Use ~/.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/

Facts

Skill name
agent-memory-discipline
GitHub stars
47k
Used in
1 other repo
Token cost
~2.9k tokens
SKILL.md length
1,614 words
Files
2 (incl. references)
Skills in repo
1,354
Repo updated
First seen
Licence
CC0-1.0

At a glance

Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards.

  • Works in 6 steps: Recall Before Acting → Save After Deciding → Write It So It Survives → …
  • Tasks that involve Agent memory
  • SKILL.md covers Overview, When to Use This Skill, How It Works and Examples, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Agent Memory Discipline is an agent skill from sickn33/agentic-awesome-skills. Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/entry-format.md`).

It sits in Agent Workflows, covering Agent memory. It works with Model Context Protocol. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is CC0-1.0.

When your agent uses it

  • Tasks that involve Agent memory

Example prompts

  • “Use the agent-memory-discipline skill to rule for when an agent should recall from long-term memory before acting and when it should save decisions…”
  • “/agent-memory-discipline”

Requirements

  • Node.js

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Recall Before Acting
  2. Save After Deciding
  3. Write It So It Survives
  4. Close the Past Instead of Overwriting It
  5. Keep Contradictions Visible
  6. Weigh Evidence and Policy Differently

What it can do on your machine

Read from SKILL.md and the folder at commit ec02547. 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

    No scripts in the folder and no shell commands in SKILL.md.

    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):

    • github.com
    • platform.claude.com
    • code.claude.com
    • modelcontextprotocol.io
    • mnemoverse.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

Agent Memory Discipline loads about 2.9k tokens when it runs, and up to ~3.3k if it reads all its reference files. Until then it costs about 50 tokens; SKILL.md has 1,614 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.3k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit ec02547, republished under its CC0-1.0 licence (© sickn33). 1,614 words, ~2,919 tokens.

Download SKILL.mdSave it as .claude/skills/agent-memory-discipline/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
agent-memory-discipline
description
Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.
category
memory
risk
safe
source
community
source_repo
mnemoverse/agent-memory-discipline
source_type
community
date_added
2026-09-23
author
mnemoverse
tags
agent-memory, long-term-memory, context-engineering, mcp, agent-skills
tools
claude, cursor, gemini
license
CC0-1.0

Agent Memory Discipline

Overview

Connecting a memory tool does not make an agent use it: tools register, the session runs, and nothing gets recalled or saved. This skill supplies the missing part, standing rules for when to read memory and when to write it.

The problem it solves is specific. An agent with memory available still repeats settled questions, reverts corrected habits, and loses decisions between sessions, because nothing tells it when recall and save are due. The rules below make both moments explicit.

When to Use This Skill

  • Use when a memory tool or MCP memory server is connected but the agent is not using it consistently.
  • Use when the user complains that the assistant loses preferences, conventions or past decisions between sessions.
  • Use when setting up persistent memory for a project and the agent needs standing rules for reading and writing it.
  • Use when the user says "remember this", "what did we decide", "recall", or "save this for next time".

How It Works

Before You Start: Any Memory Backend

The agent needs a memory tool it can call. Any backend works, and the rules are identical for each:

  • Files. A memory/ folder of Markdown notes, one fact per file. No dependencies, fully greppable, versionable in git.
  • A local MCP memory server. Keeps everything on the local machine; several open-source options exist.
  • A hosted memory service over MCP. Adds portability across tools and machines at the cost of the data living elsewhere.

Authentication is whatever the chosen backend requires: none for a local folder, the server's own configuration for a local MCP server, an API key or OAuth sign-in for a hosted service. This skill never handles credentials itself and never writes them into memory.

Step 1: Recall Before Acting

Read memory before doing any of these, not after:

  • starting work on a project touched before
  • choosing a library, pattern, or tool
  • writing tests, commits, or documentation, where conventions apply
  • answering "how do we usually do X here"
  • anything the user phrases as "again", "like last time", or "as we agreed"

Skip recall for one-off factual questions, arithmetic, or anything fully specified in the current message. Recall costs a tool call and context; spending it on a self-contained question is waste.

Search with the words the user actually used, plus the project or repository name. If the first search returns nothing useful, try one broader query, then stop and proceed without memory rather than looping.

Step 2: Save After Deciding

Write to memory when one of these has just happened:

  • a decision was made and will still matter next week ("we use pnpm", "the billing module stays untouched")
  • the user corrected the agent, which is the strongest signal there is
  • an approach failed, and why it failed
  • a preference was stated that applies beyond this task
  • a fact about the environment was discovered the hard way (a port, a flag, a service that must be running)

Do not save: the contents of files that can be read again, restatements of the current task, transient state, anything the user marked as temporary, and anything containing secrets, tokens, or personal data.

One memory, one fact. A paragraph containing four decisions cannot be superseded cleanly when one of them changes.

Step 3: Write It So It Survives

A memory that is useless in three weeks was written wrong. Give each entry, in the text if the backend has no fields for it:

  • what was decided or observed, in one sentence
  • why, briefly, because the reason outlives the decision
  • when it became true, and when it stopped being true if it has
  • where it came from: a file, a commit, a conversation, a test run

Prefer the user's own words over a paraphrase. Paraphrase drifts.

Step 4: Close the Past Instead of Overwriting It

When something changes, the old memory is not wrong. It is closed.

If the project moved from Redux to Zustand, "we use Redux" was true from January to June. Deleting it destroys the explanation for every component written in that window. Mark it superseded, keep its validity window, and write the new one alongside.

This is the single most destructive habit in agent memory, and it stays invisible until someone asks a question about old code.

Step 5: Keep Contradictions Visible

If recall returns two entries that disagree, do not pick the closer match and proceed. Surface both, with their dates, and ask or flag. A convention that a recent failure contradicts is exactly the situation where the user needs to be told, not smoothed over.

Step 6: Weigh Evidence and Policy Differently
  • Evidence is what happened: one run, one failure, one observation. Cheap, plentiful, individually unreliable.
  • Policy is what should happen: a convention, a decision, a rule. Expensive, and should be hard to change by accident.

An observation becomes policy when a human confirms it, when it lands in a merged decision record, or when it has worked repeatedly. Never promote a single observation to a rule without one of those.

What Following This Skill Produces

Two things, and nothing else:

  • Recalled context, stated before the work starts. The relevant entries are named with their dates and sources, so the user can see what the agent is relying on. Conflicting entries are shown side by side, not merged.
  • New memory entries after decisions, corrections and failures. One fact each, in this shape:
text
Project uses pnpm, not npm. Stated by the user on 2026-08-11 after a lockfile conflict. Applies to all packages in this repo.

A superseded entry keeps its text and gains an end date and a pointer to the entry that replaced it. The full format, with closing and evidence examples, is in references/entry-format.md.

Examples

Example 1: A Correction

The user says: "stop using npm here, we're on pnpm."

  1. This is a correction, which is the strongest save signal. Save it.
  2. Write the entry:
text
Project uses pnpm, not npm. Stated by the user on 2026-08-11 after a lockfile conflict. Applies to all packages in this repo.
  1. Do not also save "the user was annoyed", "ran npm install", or the lockfile contents.
  2. Next session, before running any package command in this repo, recall first and find it.
Show full SKILL.md (625 more words)Show less
Example 2: A Contradiction

Recall returns two entries:

text
Integration tests run against the staging database (2026-06-02)
Integration tests use a local container; staging is off limits after the incident (2026-08-19)
  1. Do not pick one and continue. State both, with dates.
  2. Ask: "Memory has two rules for integration tests; the August one says staging is off limits. Use the local container?"
  3. After the answer, close the entry that no longer holds, with its end date, and keep the other.
Example 3: Closing an Entry That Stopped Being True

Do not delete the old entry. Add an end date and a pointer to what replaced it, then write the new entry alongside:

text
[closed 2026-06-30, replaced by "State management uses Zustand"] State management uses Redux. Decided in the January architecture review.
State management uses Zustand. Migrated in June after the bundle size review. Applies to all new components.

Best Practices

  • Do: recall before project-specific work, and name the entries you rely on with their dates and sources.
  • Do: save right after a decision, a correction, or a failure, in one sentence with its reason.
  • Do: keep one fact per entry, so a later change can supersede it cleanly.
  • Do: prefer the user's own words over a paraphrase.
  • Don't: overwrite or remove an entry that stopped being true; close it with an end date instead.
  • Don't: save file contents that can be read again, restatements of the current task, or transient state.
  • Don't: promote a single observation to a rule without human confirmation, a merged decision record, or repeated success.

Limitations

  • This skill installs no memory backend and requires no account. Without a memory tool the agent can call, it can only say that memory is unavailable and continue.
  • It decides when to recall and what to save. How well entries are found again depends on the backend's own search.
  • It does not resolve contradictions by itself. It surfaces them with their dates and asks the user which one holds.
  • What happens to an entry the user asks to remove depends on what the backend supports.
  • This skill does not replace environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, or safety boundaries are missing.

Security & Safety Notes

  • The skill contains no shell commands and makes no network calls of its own. It reads and writes memory only through the memory tool the user has already connected.
  • Never save secrets, tokens, passwords, or personal data into memory, and never write credentials into an entry.
  • A hosted memory backend stores entries outside the local machine. Choose the backend with that in mind.
  • When the user asks for an entry to be removed, close or remove it as the backend allows, and confirm what was removed.

Common Pitfalls

  • Problem: The memory tool is unavailable or failing. Solution: Proceed without memory, say so in one line, and do not retry in a loop. Save the pending decision as soon as the tool is back, rather than dropping it.
  • Problem: Recall returns nothing. Solution: Run one broader query, then continue without memory. An empty result is information: the topic is new, so a decision made now is worth saving.
  • Problem: Recall returns too much. Solution: Keep the entries that match the current project and task; ignore the rest rather than pasting them into context.
  • Problem: Two entries disagree. Solution: Show both with their dates and ask which holds. Do not resolve the conflict silently.
  • Problem: A save is rejected or filtered by the backend. Solution: Report it once. Do not rephrase the same fact repeatedly to get it through.
  • @memory-systems: when designing the memory architecture itself (short-term, long-term, graph-based) rather than the habit of using it.
  • @context-engineering: when setting up rules files and session context, which this skill complements with rules for long-term memory.

Additional Resources

Written and maintained by the team behind Mnemoverse, which is one hosted implementation. The rules above are deliberately backend-neutral and were written to be useful without it.

© sickn33, CC0-1.0. 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 1 other file (references) in skills/agent-memory-discipline of sickn33/agentic-awesome-skills.

  • SKILL.md
  • references/entry-format.md

Open the folder on GitHubat commit ec02547

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Agent Memory Discipline 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.

Agent Memory Discipline compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Agent Memory Discipline this skillsickn33/agentic-awesome-skills47k1 repos~2.9kAutomated safety check: PassCC0-1.0
MemPalace Memory SearchMemPalace/mempalace59k—~1.4kAutomated safety check: PassMIT
MemPalace Setup and OperationMemPalace/mempalace59k—~2.2kAutomated safety check: PassMIT
agentmemory Setup and Diagnosticsrohitg00/agentmemory29k—~1kAutomated safety check: NotesApache-2.0
Qmdbreferrari/obsidian-mind4.9k—~1.7kAutomated safety check: PassMIT
Memori MCP Memory UsageMemoriLabs/Memori17k—~3.8kAutomated safety check: PassMIT

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Categories

Questions about Agent Memory Discipline

What does Agent Memory Discipline do?

Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Agent Memory Discipline is an agent skill from sickn33/agentic-awesome-skills. Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards.

When should I use Agent Memory Discipline?

Agent Memory Discipline fits situations like: tasks that involve Agent memory.

How do I install Agent Memory Discipline in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill agent-memory-discipline -a claude-code`. Or copy the skill folder (skills/agent-memory-discipline in sickn33/agentic-awesome-skills) into .claude/skills/agent-memory-discipline in your project. Claude Code loads it when a task matches its description.

How do I install Agent Memory Discipline in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill agent-memory-discipline -a codex`. Or copy the skill folder (skills/agent-memory-discipline in sickn33/agentic-awesome-skills) into .agents/skills/agent-memory-discipline in your project. Codex loads it when a task matches its description.

Can I use Agent Memory Discipline 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 sickn33/agentic-awesome-skills --skill agent-memory-discipline -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-memory-discipline, .gemini/skills/agent-memory-discipline, .github/skills/agent-memory-discipline and .opencode/skills/agent-memory-discipline in your project.

What does Agent Memory Discipline need to run?

SKILL.md names no scripts, command-line tools or credentials: Agent Memory Discipline is instructions for the agent only. Our summary lists: Node.js.

Does Agent Memory Discipline access the network?

SKILL.md names 5 domains. As links in the text: github.com, platform.claude.com, code.claude.com, modelcontextprotocol.io and mnemoverse.com. This is read from the text; nothing was executed.

Is Agent Memory Discipline 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. Review the folder before installing.

What licence does Agent Memory Discipline use?

Agent Memory Discipline is published under the CC0-1.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agent Memory Discipline use?

About 2.9k tokens (SKILL.md is roughly 12k 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 362 tokens, read only when the agent opens those files.

What are the alternatives to Agent Memory Discipline?

Skills that share tags, products or a category with Agent Memory Discipline: MemPalace Memory Search (MemPalace/mempalace, 59k stars), MemPalace Setup and Operation (MemPalace/mempalace, 59k stars), agentmemory Setup and Diagnostics (rohitg00/agentmemory, 29k stars) and Qmd (breferrari/obsidian-mind, 4.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agent Memory Discipline?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,343 GitHub stars. The repository holds 1,354 skills in this directory. The repository was last updated on October 7, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.