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.
Walrus Memory SDK — portable agent memory that works across apps, sessions, and workflows.
$ npx skills add MystenLabs/MemWal --skill memwal -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install MystenLabs/MemWal memwal --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
Claude Code skills documentation · loads skills from .claude/skills/
Install the "memwal" agent skill from https://github.com/MystenLabs/MemWal/tree/dev into .claude/skills/memwal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memwal", 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.
$ npx skills add MystenLabs/MemWal --skill memwal -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install MystenLabs/MemWal memwal --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "memwal" agent skill from https://github.com/MystenLabs/MemWal/tree/dev into .agents/skills/memwal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memwal", 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 MystenLabs/MemWal --skill memwal -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install MystenLabs/MemWal memwal --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "memwal" agent skill from https://github.com/MystenLabs/MemWal/tree/dev into .cursor/skills/memwal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memwal", 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.
$ npx skills add MystenLabs/MemWal --skill memwal -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install MystenLabs/MemWal memwal --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "memwal" agent skill from https://github.com/MystenLabs/MemWal/tree/dev into .gemini/skills/memwal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memwal", 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 MystenLabs/MemWal memwalInstalls 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 MystenLabs/MemWal --skill memwal -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "memwal" agent skill from https://github.com/MystenLabs/MemWal/tree/dev into .github/skills/memwal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memwal", 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 MystenLabs/MemWal --skill memwal -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install MystenLabs/MemWal memwal --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "memwal" agent skill from https://github.com/MystenLabs/MemWal/tree/dev into .opencode/skills/memwal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memwal", 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.
memwalWalrus Memory SDK — portable agent memory that works across apps, sessions, and workflows.
Memwal is an agent skill from MystenLabs/MemWal. Walrus Memory SDK — portable agent memory that works across apps, sessions, and workflows. Use when users say: - "add memory to my app" - "portable agent memory" - "integrate Walrus Memory" - "AI agent memory" - "memory across agents" - "Walrus memory storage" - "setup Walrus Memory" - "recall memories"
Its SKILL.md is about 5.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1848 other files, including scripts (for example `.agents/plugins/marketplace.json`, `.changeset/README.md` and `.changeset/config.json`).
It sits in Agent Workflows, covering Agent memory. The licence is Apache-2.0.
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 3e0534e. It shows what the files ask for, not the result of running them.
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.
Ships 1 file in scripts/, which the agent can run.
Shell commands in SKILL.md call:
pnpmpipFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
relayer.memory.walrus.xyzrelayer-staging.memory.walrus.xyzAlso links to:
memory.walrus.xyzstaging.memory.walrus.xyznpmjs.comdocs.wal.appFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
MEMWAL_PRIVATE_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Memwal loads about 5.4k tokens when it runs. Until then it costs about 78 tokens; SKILL.md has 1,886 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.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
tch, or staging/mainnet mismatch. Check `.env.local` and dashboard credentials |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.
The full file from MystenLabs/MemWal at commit 3e0534e, republished under its Apache-2.0 licence (© MystenLabs). 1,886 words, ~5,354 tokens.
.claude/skills/memwal/SKILL.md (or your agent's skills folder). This skill also uses 1839 other files; get the full folder from GitHub.Walrus Memory enables AI agents to operate reliably across apps and sessions, without losing context. It stores memories on Walrus (decentralized storage), encrypts them with SEAL, enforces ownership onchain via Sui smart contracts, and retrieves them with semantic (vector) search. Memory is portable by design — not tied to a single runtime or provider — and scoped by owner + namespace for isolation and coordination.
Use Walrus Memory when your app or agent needs:
# Install the SDK
pnpm add @mysten-incubation/memwal
# Optional: for Vercel AI SDK integration
pnpm add ai zod
# Optional: for manual client (client-side SEAL encryption)
pnpm add @mysten/sui @mysten/seal @mysten/walrusYou need a delegate key (Ed25519 private key) and account ID (Walrus Memory account object ID on Sui).
Generate them at:
import { MemWal } from "@mysten-incubation/memwal";
const memwal = MemWal.create({
key: process.env.MEMWAL_PRIVATE_KEY!,
accountId: process.env.MEMWAL_ACCOUNT_ID!,
serverUrl: process.env.MEMWAL_SERVER_URL ?? "https://relayer.memory.walrus.xyz",
namespace: "my-app",
});// Store one already-distilled fact and wait until it is indexed.
await memwal.rememberAndWait(
"User prefers dark mode and works in TypeScript.",
undefined,
{ timeoutMs: 30_000 },
);
// Recall by meaning
const result = await memwal.recall({ query: "What are the user's preferences?" });
console.log(result.results);
// Extract facts from free-form text and wait until all accepted facts are indexed.
const analyzed = await memwal.analyzeAndWait(
"I live in Hanoi and prefer dark mode.",
undefined,
{ timeoutMs: 30_000 },
);
console.log(analyzed.facts.map((fact) => fact.text));
// Check relayer health
await memwal.health();Use *AndWait when a workshop UI saves and then immediately recalls in the
same flow. Indexing can lag by a few seconds, so remember() / analyze()
may return before recall can find the new memory. Manual polling is still
available for advanced async UIs:
const accepted = await memwal.remember("User likes Sui.");
const stored = await memwal.waitForRememberJob(accepted.job_id, {
pollIntervalMs: 750,
timeoutMs: 30_000,
});| Entry Point | Import | Description |
|---|---|---|
MemWal | @mysten-incubation/memwal | Default. Relayer handles embedding, SEAL encryption, Walrus upload, vector search |
MemWalManual | @mysten-incubation/memwal/manual | Manual flow — client handles embedding and SEAL encryption |
withMemWal | @mysten-incubation/memwal/ai | Vercel AI SDK middleware — auto recall + save around AI conversations |
| Account utils | @mysten-incubation/memwal/account | Account creation, delegate key management |
| Method | Description | Returns |
|---|---|---|
remember(text, namespace?) | Accept one memory job immediately | { job_id, status } |
rememberAndWait(text, namespace?, opts?) | Store one memory and wait for completion | { id, job_id, blob_id, owner, namespace } |
recall({ query, limit?, topK?, namespace?, maxDistance? }) (preferred) or recall(query, limit?, namespace?) | Semantic search for memories | { results: [{ blob_id, text, distance }], total } |
analyze(text, namespace?) | Extract facts and accept one memory job per fact | { job_ids, facts, fact_count, status, owner } |
analyzeAndWait(text, namespace?, opts?) | Extract facts and wait for all fact jobs to complete | { results, facts, total, succeeded, failed, owner } |
restore(namespace, limit?) | Rebuild missing index entries from Walrus | { restored, skipped, failed, total, namespace, owner, truncated } |
health() | Check relayer health | { status, version } |
getPublicKeyHex() | Get hex-encoded public key | string |
| Method | Description |
|---|---|
rememberManual({ encryptedData, vector, namespace? }) | Send SEAL-encrypted bytes + pre-computed vector; relayer uploads |
recallManual({ vector, limit?, namespace? }) | Search with pre-computed vector (returns blob IDs only) |
embed(text) | Generate embedding vector (no storage) |
interface RememberAcceptedResult {
job_id: string;
status: string;
}
interface RememberJobStatus {
job_id: string;
status: "pending" | "running" | "uploaded" | "done" | "failed" | "not_found";
owner?: string;
namespace?: string;
blob_id?: string;
error?: string;
}
interface RememberResult {
id: string;
job_id?: string;
blob_id: string;
owner: string;
namespace: string;
}
interface RecallMemory {
blob_id: string;
text: string;
distance: number;
}
interface RecallResult {
results: RecallMemory[];
total: number;
}
interface RecallOptions {
limit?: number;
topK?: number; // alias of limit; if both are set, topK wins
namespace?: string;
maxDistance?: number;
}
// `topK` and `limit` are aliases for the same value; if both are provided, `topK` takes precedence.
interface RememberBulkAcceptedResult {
job_ids: string[];
total: number;
status: string;
}
interface AnalyzedFact {
text: string;
id: string;
job_id?: string;
blob_id?: string;
}
interface AnalyzeResult {
job_ids: string[];
facts: AnalyzedFact[];
fact_count: number;
status: string;
owner: string;
}
interface RememberBulkStatusItem {
job_id: string;
status: "pending" | "running" | "uploaded" | "done" | "failed" | "not_found";
blob_id?: string;
error?: string;
}
interface RememberBulkStatusResult {
results: RememberBulkStatusItem[];
}
interface RememberBulkItemResult {
id: string;
blob_id: string;
status: "done" | "failed" | "timeout";
namespace: string;
error?: string;
}
interface RememberBulkResult {
results: RememberBulkItemResult[];
total: number;
succeeded: number;
failed: number;
}
interface AnalyzeWaitResult extends RememberBulkResult {
facts: AnalyzedFact[];
owner: string;
}
interface EmbedResult {
vector: number[];
}
interface RestoreResult {
restored: number;
skipped: number;
failed: number;
total: number;
namespace: string;
owner: string;
truncated: boolean;
}
interface HealthResult {
status: string;
version: string;
mode?: string;
prompt_versions?: {
extract: string;
ask: string;
};
relayerVersion?: string;
apiVersion?: string;
minSupportedSdk?: {
typescript: string;
python: string;
mcp: string;
};
featureFlags?: Record<string, boolean>;
deprecations?: Array<{
surface: string;
deprecatedSince: string;
removalApiVersion: string;
guidance: string;
}>;
build?: {
commit?: string;
buildTimestamp?: string;
};
}facts[].text is the extracted fact text to render in UIs. job_ids[]
aligns with the accepted fact jobs; use analyzeAndWait() when the UI needs
those facts indexed before continuing.
A namespace is an opaque, flat string label scoped to a single owner. It is the unit of memory isolation: a recall in namespace A will never surface entries written to namespace B, even for the same owner, and never surfaces other owners' entries even in the same namespace.
Omit namespace and the server uses the literal string "default". An explicit empty string is rejected with HTTP 400 (namespace cannot be empty).
The server then accepts any non-empty UTF-8 string except:
str::len()) → HTTP 400 namespace exceeds maximum length of 255 bytes\0) → HTTP 400 namespace contains a NUL byte (WALM-439). Tabs, newlines, and other control characters are still allowed so older namespaces stay readable.There is no character whitelist, no case folding, no trim, and no Unicode normalization. Whatever passes validation is stored verbatim and matched with exact equality.
Implication:
"my-app"," my-app"(leading space),"My-App", and"my-app/"are four distinct namespaces. Pick a convention and stick to it. Multi-byte characters (CJK, emoji) consume more than one byte each, so they hit the 255-byte cap sooner than a character count would suggest.
Slashes and dots have no special meaning. "chat/user-42" is a single opaque label, not a path. The server uses WHERE namespace = $1 exact-equality for every read; there is no prefix matching, no parent/child traversal, and no wildcard query. If you need hierarchy, build it in the application layer (e.g. recall across known namespaces and merge client-side).
remember() is always append, never upsertEvery accepted remember() call creates a new memory entry with a freshly generated UUID. Sending the same text to the same (owner, namespace) twice will produce two separate entries that both surface in future recalls. The namespace is metadata for filtering, not a key for deduplication.
await memwal.remember("I prefer dark mode", "prefs");
await memwal.remember("I prefer dark mode", "prefs");
// recall("preferences", { namespace: "prefs" }) → 2 entries, both with the same textIf you need uniqueness, either dedupe before calling remember(), or delete the prior entry first.
| Scenario | Visible to recall? |
|---|---|
| Same owner, same namespace | ✅ |
| Same owner, different namespace | ❌ |
| Different owner, same namespace | ❌ |
| Different owner, different namespace | ❌ |
Cross-namespace and cross-owner reads are not just filtered out of results — the server's SQL WHERE clause excludes them entirely, so they are never decrypted or transferred.
restore(namespace, limit?) rebuilds missing local index entries for a namespace from Walrus. It is a recovery operation, not a sync — already-indexed blobs are left alone.
| Field | Counts | Notes |
|---|---|---|
restored | Blobs the relayer just rebuilt this call | Pulled from Walrus → SEAL decrypted → re-embedded → inserted as a new row |
skipped | On-chain blobs already in the local success index | No work needed; relayer left them as-is. Does not include decrypt/UTF-8 failures. |
failed | Permanent decrypt/UTF-8 failures | On-chain blobs in this page that are negative-cached, plus new permanent failures this call. Older relayers omit the field; SDKs default it to 0. |
total | All on-chain blobs the relayer saw for (owner, namespace) | Before the limit was applied |
namespace | Echo of the request | |
owner | Resolved owner address | |
truncated | Known-retryable-incomplete | true is not a hard failure; false is not completeness |
truncated=true means this restore is known-retryable-incomplete: more missing blobs than limit allowed this call to restore, or the sidecar's owner-wide candidate fetch hit its cap and raising limit can still expand that fetch (limit < 20). Once the sidecar cap is saturated (limit >= 20, cap pinned at 100), truncation follows this call's missing-blob page length, not onchain total. A fully restored namespace does not loop. truncated=false is not proof the sidecar saw every onchain blob; blobs beyond the owner-wide sidecar candidate cap can still be missing. WALM-451 tracks a sourceCapped field for that case. Relayers older than WALM-319 omit truncated; SDKs default it to false.
Permanent decrypt or invalid-UTF-8 failures count in failed, not skipped. Transient download/decrypt/embed errors are still not counted in restored, skipped, or failed and may be retried (truncated=true when a page yields only those). restored + skipped + failed therefore never exceeds total, and falls short of it whenever transient errors leave blobs uncounted.
limit defaults to 10 in both TypeScript and Python SDKs and matches the server-side default. The Python SDK historically defaulted to 50; it is now realigned with the server.limit caps the inspected blob set, newest-first. It does not cap restored independently — if all 10 inspected blobs are already indexed, restored = 0 and skipped = 10.limit to 1–100 (values outside that range are clamped, not rejected).Restore is single-shot — there is no cursor. To rebuild a namespace larger than your chosen limit, call again with a larger limit, or delete local rows you want re-imported first. Pagination is on the roadmap; until it lands, treat restore() as a "top up to N most recent" operation.
Latency scales linearly in limit:
Expect seconds per blob on a cold cache. Use small limits (≤ 50) for interactive flows and run larger restores out-of-band.
recall() returns the closest K memories by vector distance. There is no
default relevance threshold, so small namespaces may return weak filler results
because they are still the closest available matches.
Lower distance means more similar:
| Distance | Rough meaning |
|---|---|
< 0.25 | Duplicate or very close |
0.25 - 0.55 | Related |
0.55 - 0.8 | Weak/noisy |
>= 0.8 | Usually unrelated |
Use SDK-side filtering when you only want clearly relevant results. 0.8 is a
starting point; calibrate it against your own memories and queries because
useful matches can vary by phrasing and dataset:
const memories = await memwal.recall({
query: "what did I eat yesterday?",
limit: 10,
namespace: "reading-tracker",
maxDistance: 0.8,
});Equivalent manual filtering:
const memories = await memwal.recall({
query: "what did I eat yesterday?",
limit: 10,
namespace: "reading-tracker",
});
const relevant = memories.results.filter((memory) => memory.distance < 0.8);| Field | Type | Required | Default | Description |
|---|---|---|---|---|
key | string | Yes | — | Ed25519 delegate private key in hex |
accountId | string | Yes | — | Walrus Memory account object ID on Sui |
serverUrl | string | No | https://relayer.memory.walrus.xyz | Relayer URL |
namespace | string | No | "default" | Default namespace for memory isolation |
| Network | Relayer URL |
|---|---|
| Production (mainnet) | https://relayer.memory.walrus.xyz |
| Staging (testnet) | https://relayer-staging.memory.walrus.xyz |
Delegate private keys belong on the server only. In Next.js App Router, call
Walrus Memory from server actions, route handlers, or other server-only modules that
read MEMWAL_PRIVATE_KEY from server env.
"use server" files can only export async functions; keep constants, schemas,
and reusable client builders in a separate server-only module.
// app/actions/memory.ts
"use server";
import { getMemWal } from "@/lib/memwal";
export async function savePreference(text: string) {
const memwal = getMemWal();
return memwal.rememberAndWait(text, "my-app", { timeoutMs: 30_000 });
}// lib/memwal.ts
import "server-only";
import { MemWal } from "@mysten-incubation/memwal";
export function getMemWal() {
return MemWal.create({
key: process.env.MEMWAL_PRIVATE_KEY!,
accountId: process.env.MEMWAL_ACCOUNT_ID!,
serverUrl: process.env.MEMWAL_SERVER_URL ?? "https://relayer.memory.walrus.xyz",
namespace: "my-app",
});
}Namespace strategy: owner + namespace is the isolation boundary. Use one
namespace per app by default, then split by user, team, or feature when a
single app needs separate memory spaces.
Relayer choice: use staging/testnet for learning and prototypes; use production/mainnet for production data. Do not mix staging credentials with mainnet relayer configs.
import { openai } from "@ai-sdk/openai";
import { streamText } from "ai";
import { withMemWal } from "@mysten-incubation/memwal/ai";
const model = withMemWal(openai("gpt-4o"), {
key: "<your-delegate-key>",
accountId: "<your-account-id>",
serverUrl: "https://relayer.memory.walrus.xyz",
namespace: "chat",
maxMemories: 5,
autoSave: true,
minRelevance: 0.3,
});
const result = streamText({
model,
messages: [{ role: "user", content: "What do you remember about me?" }],
});The middleware automatically:
For OpenClaw agent integration, use the @mysten-incubation/oc-memwal plugin.
openclaw plugins install @mysten-incubation/oc-memwalAdd to ~/.openclaw/openclaw.json:
{
"plugins": {
"slots": { "memory": "oc-memwal" },
"entries": {
"oc-memwal": {
"enabled": true,
"config": {
"privateKey": "${MEMWAL_PRIVATE_KEY}",
"accountId": "0x...",
"serverUrl": "https://relayer.memory.walrus.xyz"
}
}
}
}
}Lifecycle hooks run automatically:
before_prompt_build — injects relevant memories as contextagent_end — captures last response| Symptom | Fix |
|---|---|
health() returns error | Check relayer URL is correct and reachable |
recall() returns empty | Verify namespace matches what was used in remember() |
recall() returns unrelated filler | Recall is top-K without a default relevance threshold; filter by distance, for example distance < 0.8, and calibrate the cutoff against your data |
401 Unauthorized | Usually wrong MEMWAL_PRIVATE_KEY, key not registered on the account, account ID mismatch, or staging/mainnet mismatch. Check .env.local and dashboard credentials |
| SDK import errors | Run pnpm add @mysten-incubation/memwal — check Node.js ≥ 18 |
| Manual client errors | Install peer deps: @mysten/sui @mysten/seal @mysten/walrus |
| Direct Sui reads fail or examples look stale | Prefer SuiGrpcClient from @mysten/sui/grpc; JSON-RPC snippets using SuiClient / getFullnodeUrl may be stale |
forget expectations are unclear | Current relayer POST /api/forget removes vector index rows so memories are unrecallable; Walrus blobs persist until epoch expiry |
Until product confirms a canonical naming pass, these are the working assumptions reflected across this doc, the SDKs, and the relayer. Treat them as descriptive, not authoritative.
| Surface | Canonical term | Notes |
|---|---|---|
| Product / docs / UI | Walrus Memory | Used in marketing copy, user-facing dashboards, and prose docs |
| Package / env vars / internal shorthand | memwal | Used in @mysten-incubation/memwal, pip install memwal, MEMWAL_* env vars, internal logs, and codepaths |
If you're writing user-facing copy, prefer "Walrus Memory". If you're writing an env var, import path, or grep-target, prefer memwal. Don't mass-rename existing identifiers — that requires a coordinated migration outside this skill's scope.
© MystenLabs, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1,839 other files (scripts) in the repository root of MystenLabs/MemWal.
Open the folder on GitHubat commit 3e0534e
Memwal 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 |
|---|---|---|---|---|---|---|
| Memwal this skillMystenLabs/MemWal | 116 | — | ~5.4k | Automated safety check: Notes | Apache-2.0 | |
| Neat-Freak Knowledge CloseoutKKKKhazix/khazix-skills | 21k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Beads Task Memorygastownhall/beads | 28k | — | ~1.2k | Automated safety check: Pass | MIT | |
| Reflect on Session Learningscursor/plugins | 10k | 5 repos | ~1.2k | Automated safety check: Pass | None | |
| MemPalace Memory SearchMemPalace/mempalace | 59k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Compound Learning WriterEveryInc/compound-engineering-plugin | 25k | — | ~2k | Automated safety check: Pass | MIT |
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.
gastownhall/beads
Tracks multi-session work with dependencies in the bd issue tracker so the agent can find ready tasks and recover its context after conversation compaction.
cursor/plugins
Starts three parallel reviewer subagents over the current conversation transcript, then turns their findings into concrete edits to existing skills.
MemPalace/mempalace
Mines project files and conversation exports into a local, searchable memory palace and recalls past work by semantic search through the mempalace CLI.
EveryInc/compound-engineering-plugin
Records one solved and verified problem as a durable learning in the repository, but only when the reasoning is not already clear from the final code, tests or docs.
slopus/happy
Searches past Claude Code, Codex and Cursor sessions and summarizes what was worked on, tried or decided, using extraction scripts instead of reading raw logs.
Categories
Walrus Memory SDK — portable agent memory that works across apps, sessions, and workflows. Memwal is an agent skill from MystenLabs/MemWal. Walrus Memory SDK — portable agent memory that works across apps, sessions, and workflows.
Memwal fits situations like: tasks that involve Agent memory.
Run `npx skills add MystenLabs/MemWal --skill memwal -a claude-code`. Or copy the skill folder (the MystenLabs/MemWal repository) into .claude/skills/memwal in your project. Claude Code loads it when a task matches its description.
Run `npx skills add MystenLabs/MemWal --skill memwal -a codex`. Or copy the skill folder (the MystenLabs/MemWal repository) into .agents/skills/memwal in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add MystenLabs/MemWal --skill memwal -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/memwal, .gemini/skills/memwal, .github/skills/memwal and .opencode/skills/memwal in your project.
Going by SKILL.md and its folder, Memwal needs the command-line tools its instructions call (pnpm and pip) and credentials named MEMWAL_PRIVATE_KEY. Our summary lists: A credential in MEMWAL_PRIVATE_KEY.
SKILL.md names 6 domains. In commands or code: relayer.memory.walrus.xyz and relayer-staging.memory.walrus.xyz; the agent is likely to contact these when it follows the instructions. As links in the text: memory.walrus.xyz, staging.memory.walrus.xyz, npmjs.com and docs.wal.app. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. 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.
Memwal is published under the Apache-2.0 licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.
About 5.4k tokens (SKILL.md is roughly 21k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Memwal: Neat-Freak Knowledge Closeout (KKKKhazix/khazix-skills, 21k stars), Beads Task Memory (gastownhall/beads, 28k stars), Reflect on Session Learnings (cursor/plugins, 10k stars) and MemPalace Memory Search (MemPalace/mempalace, 59k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
MystenLabs (a GitHub organization) maintains it in MystenLabs/MemWal, which has 116 GitHub stars. The repository was last updated on October 7, 2026.
Source: MystenLabs/MemWal on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.