Automated summary
Integrates the Walrus Memory SDK to give AI agents portable, encrypted, onchain-scoped memory with semantic recall.
Strengths
- Provides a minimal TypeScript quick start for storing, extracting, and recalling memories.
- Covers owner and namespace scoping plus delegate keys for sharing memory across apps.
- Documents the `*AndWait` helpers to handle indexing lag in save-then-recall flows.
- Mentions optional dependencies for Vercel AI SDK integration and client-side SEAL encryption.
Limitations
- Requires a Walrus Memory account ID and delegate key obtained from the hosted service.
- Depends on an external relayer (`relayer.memory.walrus.xyz`) and indexing can lag, so immediate recall may need the wait helpers.
- Not intended for large file storage; it is optimized for text memories.
Use when adding persistent, portable memory to an agent or app that needs semantic recall across sessions or across apps.
Avoid for temporary session-only context, large file storage, or cases that do not need encryption or decentralization.
Security analysis
CautionThe skill is largely reference documentation for a memory SDK and contains no destructive or obfuscated commands. Risk is moderate rather than low because it centers on handling a private delegate key and on shipping user memory content to a remote relayer and decentralized storage, and because its plugin config example puts a key in a local JSON config file. With server-side key handling and awareness of data egress, it is acceptable but warrants caution.
- •Core workflow sends user-derived memory text to a third-party relayer (https://relayer.memory.walrus.xyz) and decentralized Walrus storage; users should be told that memory content leaves the local environment, even though it is described as SEAL-encrypted.
- •The SDK is configured with an Ed25519 delegate private key (MEMWAL_PRIVATE_KEY) that authorizes reads/writes on the on-chain account; the doc correctly says to keep it server-side, but the OpenClaw plugin example places a privateKey field directly in ~/.openclaw/openclaw.json, which invites on-disk key storage if env interpolation is not honored.
- •Installation steps run package-manager and plugin-install commands (pnpm add, openclaw plugins install) that pull and execute third-party code from npm; normal supply-chain risk with no version pinning or integrity check shown.
- •No destructive, obfuscated, or exfiltration-style instructions (no curl|sh, no credential exfiltration, no safety-disabling) were found; content is documentation plus example API usage.
- •Requires handling a long-lived Ed25519 delegate private key (MEMWAL_PRIVATE_KEY) that authorizes writes to an on-chain account; the doc does give correct guidance (server-only, env var, never client-side), but a compromised key grants persistent account access.
- •Instructs installing and running third-party packages (`pnpm add @mysten-incubation/memwal`, `openclaw plugins install @mysten-incubation/oc-memwal`) and peer deps, i.e., supply-chain exposure for arbitrary versions.
- •Plugin section directs modification of a global agent config file (`~/.openclaw/openclaw.json`) and wires a remote relayer into the agent's memory lifecycle, meaning recalled third-party content is injected into prompts (indirect prompt-injection / data-persistence surface).
- •No tools are declared, but the body instructs shell commands and network calls to a relayer (data sent to `relayer.memory.walrus.xyz`), so actual execution depends on the host agent's permissions rather than on the skill.
- •Doc is candid about limitations (e.g., `forget` removes index rows but Walrus blobs persist until epoch expiry), which is a privacy caveat worth surfacing to users.
Examples
Add portable AI agent memory to my TypeScript app using @mysten-incubation/memwal with the namespace 'my-app', then store a user preference and recall it by semantic query.Integrate Walrus Memory into my Vercel AI SDK agent so it remembers facts across sessions and retrieves them with semantic search.Show me how to use memwal.analyzeAndWait to extract facts from free-form user text, wait for indexing, and then recall those memories.name: memwal version: 0.0.1 description: | 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"
keywords:
- memwal
- walrus memory
- memory sdk
- ai memory
- portable memory
- walrus storage
- sui blockchain
- delegate key
- semantic search
- vercel ai sdk
Walrus Memory — Portable Agent Memory
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.
When to Use
Use Walrus Memory when your app or agent needs:
- Portable memory — persists outside prompts and context windows, moves across agents, apps, and workflows
- Full owner control — programmable permissions and explicit ownership define how memory is shared and accessed
- Agent coordination — shared memory spaces help agents coordinate across long-running and multi-step workflows
- Semantic recall — retrieve memories by meaning, not just keywords
- Verifiable integrity — memory integrity can be independently verified without centralized trust
- Cross-app memory — not tied to a single runtime or provider, share memory between apps via delegate keys
When NOT to Use
- Temporary conversation context that only matters in the current session
- Large file storage (Walrus Memory is optimized for text memories)
- Use cases that don't need encryption or decentralization
Installation
# 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/walrus
Quick Start
1. Get Your Credentials
You need a delegate key (Ed25519 private key) and account ID (Walrus Memory account object ID on Sui).
Generate them at:
- Production: https://memory.walrus.xyz
- Staging: https://staging.memory.walrus.xyz
2. Initialize the SDK
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",
});
3. Store and Recall Memories
// 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,
});
SDK Entry Points
| 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 |
API Surface
Walrus Memory Methods
| 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, total, namespace, owner } |
| health() | Check relayer health | { status, version } |
| getPublicKeyHex() | Get hex-encoded public key | string |
Lower-Level Methods
| 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) |
All Response Shapes
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;
total: number;
namespace: string;
owner: string;
}
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.
Namespace Semantics
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.
Validation
The server accepts any non-empty string as a namespace. There is no length cap, no character whitelist, no normalization (whitespace, case, Unicode). Whatever you send is stored verbatim and matched with exact equality. If you omit the namespace, the server falls back to the literal string "default".
Implication:
"my-app"," my-app"(leading space),"My-App", and"my-app/"are four distinct namespaces. Pick a convention and stick to it.
Flat, not hierarchical
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).
Overwrite behavior — remember() is always append, never upsert
Every 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 text
If you need uniqueness, either dedupe before calling remember(), or delete the prior entry first.
Isolation guarantees
| 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 Semantics
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.
Response fields
| 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 index | No work needed; relayer left them as-is |
| 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 | |
Silent drops. A blob that cannot be decrypted or embedded (e.g. wrong delegate key, malformed ciphertext, embedding API down) is dropped without counting in restored or skipped. restored + skipped is therefore a lower bound on healthy entries, not a strict equality with total.
Default and limit
limitdefaults to10in both TypeScript and Python SDKs and matches the server-side default. The Python SDK historically defaulted to50; it is now realigned with the server.limitcaps the inspected blob set, newest-first. It does not caprestoredindependently — if all 10 inspected blobs are already indexed,restored = 0andskipped = 10.- There is no enforced server-side maximum, but very large limits will dominate latency (see below).
Pagination
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.
Performance
Latency scales linearly in limit:
- Up to 10 concurrent Walrus aggregator downloads
- Up to 3 concurrent SEAL decrypts (CPU-bound, capped intentionally)
- Embedding requests in parallel (bounded by the relayer's embedding pool)
Expect seconds per blob on a cold cache. Use small limits (≤ 50) for interactive flows and run larger restores out-of-band.
Recall Distance and Filtering
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 - 0.7 | Weak/noisy |
| >= 0.7 | Usually unrelated |
Use SDK-side filtering when you only want clearly relevant results:
const memories = await memwal.recall({
query: "what did I eat yesterday?",
limit: 10,
namespace: "reading-tracker",
maxDistance: 0.7,
});
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 < 0.7);
Configuration
MemWalConfig
| 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 |
Managed Relayer Endpoints
| Network | Relayer URL |
|---|---|
| Production (mainnet) | https://relayer.memory.walrus.xyz |
| Staging (testnet) | https://relayer-staging.memory.walrus.xyz |
Framework and Key Handling
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.
Vercel AI SDK Integration
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:
- Recalls relevant memories before generation
- Extracts and saves facts from conversations after generation
OpenClaw / NemoClaw Plugin
For OpenClaw agent integration, use the @mysten-incubation/oc-memwal plugin.
Install
openclaw plugins install @mysten-incubation/oc-memwal
Configure
Add 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
Troubleshooting
| 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 < 0.7 |
| 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 |
Brand Terminology
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.
Links
- Docs: https://memory.walrus.xyz
- SDK on npm: https://www.npmjs.com/package/@mysten-incubation/memwal
- GitHub: https://github.com/MystenLabs/MemWal
- Dashboard: https://memory.walrus.xyz
- llms.txt: https://docs.wal.app/walrus-memory/llms.txt
From the file to a first result
The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.
View the source and supporting filesThis download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.
1. Download the file
Download SKILL.mdFrom the root of a test project, save the file as .skill-downloads/walrus-memory-sdk-for-ai-agents-mtqry3xt/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.
Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.
mkdir -p '.skill-downloads/walrus-memory-sdk-for-ai-agents-mtqry3xt' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/walrus-memory-sdk-for-ai-agents-mtqry3xt/download' --output '.skill-downloads/walrus-memory-sdk-for-ai-agents-mtqry3xt/SKILL.md.part' && mv '.skill-downloads/walrus-memory-sdk-for-ai-agents-mtqry3xt/SKILL.md.part' '.skill-downloads/walrus-memory-sdk-for-ai-agents-mtqry3xt/SKILL.md'2. Check prerequisites
Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.
Original excerpt: Installation
# 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/walrus
The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.
3. Load the skill
After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.
Claude Code
test -f '.skill-downloads/walrus-memory-sdk-for-ai-agents-mtqry3xt/SKILL.md' && test -s '.skill-downloads/walrus-memory-sdk-for-ai-agents-mtqry3xt/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/memwal' && cp -R '.skill-downloads/walrus-memory-sdk-for-ai-agents-mtqry3xt' '.claude/skills/memwal'Cursor
test -f '.skill-downloads/walrus-memory-sdk-for-ai-agents-mtqry3xt/SKILL.md' && test -s '.skill-downloads/walrus-memory-sdk-for-ai-agents-mtqry3xt/SKILL.md' && mkdir -p '.cursor/skills' && test ! -e '.cursor/skills/memwal' && cp -R '.skill-downloads/walrus-memory-sdk-for-ai-agents-mtqry3xt' '.cursor/skills/memwal'Codex
test -f '.skill-downloads/walrus-memory-sdk-for-ai-agents-mtqry3xt/SKILL.md' && test -s '.skill-downloads/walrus-memory-sdk-for-ai-agents-mtqry3xt/SKILL.md' && mkdir -p '.agents/skills' && test ! -e '.agents/skills/memwal' && cp -R '.skill-downloads/walrus-memory-sdk-for-ai-agents-mtqry3xt' '.agents/skills/memwal'OpenSkills
OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.
Official OpenSkills installationopenskills install './.skill-downloads/walrus-memory-sdk-for-ai-agents-mtqry3xt'openskills read 'walrus-memory-sdk-for-ai-agents-mtqry3xt'Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.
For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.
openskills syncManual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.
4. Try a first task
Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:
Read .skill-downloads/walrus-memory-sdk-for-ai-agents-mtqry3xt/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.
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