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Walrus research

Skill pivyme/suiperpower/core/skills/idea/walrus-research

Research Walrus storage usage patterns and product opportunities. Use when the user wants Walrus research, market gaps, or storage opportunities.From its SKILL.md

Install
npx -y skills add pivyme/suiperpower --skill walrus-research

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

One thing to look at

  • 12 stars12 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

6.7 KB, ~1.6k tokens by cl100k_base, as published. Nobody here has run it

Preamble (run first)

# Suiperpower telemetry. Routes through the CLI so projects.json + Convex stay in sync.
# Silent on failure so the skill never blocks. Opt out: SUIPERPOWER_TELEMETRY=off.
#
# AGENT NOTE: when this skill finishes, run the matching completion command:
#   suiperpower track walrus-research idea completed
# Or use "failed" / "aborted" if it ended that way.
command -v suiperpower >/dev/null 2>&1 && suiperpower track walrus-research idea started >/dev/null 2>&1 &
true

If TEL_PROMPTED is no, before doing real work, ask the user:

Help suiperpower get better. We track which skills get used and how long they take. No code, no file paths, no PII. Change anytime in ~/.suiperpower/config.json.

A) Sure, anonymous B) No thanks

Write the answer to ~/.suiperpower/config.json telemetryTier field and create ~/.suiperpower/.telemetry-prompted. Then continue.

What this skill does

Surveys current Walrus storage usage and turns it into a list of product opportunities. Walks four categories: media products, archive products, identity / verifiable storage, and developer tooling. Output is a ranked candidate list grounded in observed usage patterns and gaps in tooling, not generic "decentralized storage" framings.

The Walrus value proposition is durable, available, content-addressed blob storage with payment in WAL. The gaps are usually at the integration layer (apps, tooling, gateways), not the protocol layer.

When to use it

  • The user wants to build on Walrus but is exploring use cases.
  • The user is mid-validation of a Walrus-adjacent idea and wants traction signals.
  • The user is sponsor-track-aligned (Sui Overflow Walrus track) and needs a load-bearing integration angle.

When NOT to use it

  • The user wants to build a Walrus integration with a chosen idea, route to walrus-storage.
  • The user wants general Sui idea search, route to find-next-sui-idea.
  • The user wants DeepBook research, route to deepbook-research.

If you activated this and the user actually wants something else, consult skills/SKILL_ROUTER.md and hand off.

Inputs

  • The user's interest in Walrus (curious, picking an idea, validating).
  • Optional: a content type the user has in mind (images, video, datasets, archives).
  • Optional: the user's chain experience.

Outputs

A research block written to .suiperpower/idea-context.md (or a new .suiperpower/research-walrus-<timestamp>.md if no idea is chosen yet):

## Walrus research, <timestamp>

### Usage patterns observed
- <pattern>: <evidence>
- ...

### Underserved use cases
1. <use case>: <evidence>, <product idea this enables>
2. ...

### Gaps in tooling
- <gap>: <evidence>, <product idea this enables>

### Risks and constraints
- <risk>: <mitigation>

### Citations
- <Walrus docs link, observability dashboard, sponsor RFP, etc>

Workflow

  1. Confirm scope

    • Open-ended Walrus research, or focused on a specific content type?
  2. Scan Walrus usage signals

    • Read the official Walrus dashboard (suiscan.xyz/mainnet/Walrus or equivalent) for active publishers and aggregators.
    • Survey known consumer apps integrating Walrus (NFT marketplaces using Walrus for media, dapps using it for static assets, etc.).
    • Note volume signals (blobs published per day, total size, payment activity in WAL).
  3. Walk the four categories

    • Media products: NFT marketplaces, content-creator platforms, social media, image / video sharing. Where do existing apps fall short on storage durability, hosting cost, or content-addressing?
    • Archive products: legal documents, scientific datasets, government records, backups. The "permanent storage" angle. Who needs verifiable, long-lived storage today?
    • Identity / verifiable storage: user-controlled storage of identity documents, credentials, signed artifacts. zkLogin + Walrus + capability gates is a Sui-native composition.
    • Developer tooling: Walrus gateways, indexing tools, content-addressing helpers, Walrus-as-a-CDN, frameworks for app developers.
  4. Identify underserved use cases

    • For each category, name at least one use case where current solutions are unsatisfying (web2 risky, IPFS unreliable, S3 expensive at scale).
    • Tie each use case to a specific user (not "users", not "developers", but a named persona).
  5. Identify tooling gaps

    • Is there a public Walrus gateway with a CDN-grade SLA?
    • Is there an indexing layer for blob discovery?
    • Is there a framework for "Walrus + Sui Object" composability?
    • Is there a billing / metering tool for app developers managing WAL spend?
  6. Risks and constraints

    • Walrus pricing in WAL: is the WAL/USD curve at a level that makes the use case unit-economic-positive?
    • Latency and read patterns: Walrus is for blob storage, not realtime database. Use cases that need sub-100ms reads at scale should be flagged.
    • Sponsor risk: Walrus is sponsor-backed; map out the sponsor relationship if the candidate's business model depends on Walrus stability.
  7. Citations

    • Every claim links to docs, a dashboard, an existing project, or a community post.
    • Refuse claims without citations.
  8. Writeback

    • Append to the chosen output file.

Quality gate (anti-slop)

Before reporting done:

  • Is each use case tied to a named persona, not "users"?
  • Are tooling gaps cited with evidence (or explicit "no public solution found, last checked <date>")?
  • Did the analysis include unit-economic considerations (WAL spend per user / blob)?
  • Did the analysis avoid "decentralized storage is the future" framing without specifics?
  • Did the writeback happen?

If any answer is no, the skill keeps working.

References

On-demand references (load when relevant to the user's question):

  • references/walrus-categories.md: Four-category decomposition with category-specific questions.
  • references/walrus-unit-economics.md: How to think about WAL spend, gateway cost, pricing.

Knowledge docs:

  • skills/data/sui-knowledge/sponsor-docs/walrus.md: Walrus integration knowledge doc.
  • skills/data/sui-knowledge/04-protocols-and-sdks.md: SDK and integration overview.

Use in your agent

  • Claude Code: claude "/suiper:walrus-research <your message>"
  • Codex: codex "/walrus-research <your message>"
  • Grok Build: run grok, then /walrus-research <your message> in the session
  • Cursor: paste a chat message that includes a phrase like "Walrus research", or load ~/.cursor/rules/walrus-research.mdc and reference it.

If you activated this and the user actually wants something else, consult skills/SKILL_ROUTER.md and hand off.

What ships with it: 3 files

7.4 KB alongside SKILL.md

agents/

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