SCF Submission Radar

Position a Stellar Community Fund (SCF) idea against the public record of prior submissions, so you can sharpen the pitch, find the right category, and avoid re-proposing something already funded. This is research and positioning only — it does not submit anything to SCF. It runs entirely on LumenLoop's free, read-only Stellar ecosystem MCP (https://mcp.lumenloop.com), which mirrors the public SCF submissions index alongside the project directory and editorial content.

When to use this skill

• The user is drafting an SCF application and wants to know what's been proposed or funded in their space already.
• They ask "has anyone built / pitched X to SCF?" or "what's the competition for my idea?"
• They want to pick the right SCF category and award track for a proposal.
• They have a one-liner for a product and want a positioning brief before they write the long application.
• They found one prior submission and want to see its neighbors / the team's full SCF history.

Related skills

• LumenLoop MCP setup and tool overview → ../lumenloop-mcp-connect/SKILL.md
• Deep due-diligence on a single team behind a submission → ../stellar-project-dossier/SKILL.md
• Map the wider sector your idea sits in → ../stellar-ecosystem-scout/SKILL.md
• Once positioning is clear, plan the build → ask for the stellar-builder-quickstart skill, and for Soroban / dapp / asset / data references see the Stellar docs at https://developers.stellar.org.

The tools you use

find_similar_scf_submissions takes query XOR slug — pass one, not both. Use query for a natural-language idea; use slug (a submission id like recsAQHCghw9HIUvF) to find neighbors of a specific submission.

Recipe

1. Find the closest prior submissions to the idea

Lead with the idea as a natural-language query. Optionally narrow by category (one of the 5 live values) or round, and set limit (8–12 is a good sweep).

find_similar_scf_submissions(
  query: "non-custodial payroll streaming on Stellar for remote teams",
  limit: 10
)

Returns one row per submission:

Scan award_type and description to separate funded prior art (strong signal a problem is validated — and a crowded lane) from proposed-but-not-awarded (an opening, or a warning that reviewers passed). Note every linked_project_slug you'll want to profile.

2. Profile the teams behind the closest matches

For each interesting linked_project_slug, get a slim profile and the team's full SCF history.

get_project(slug: "decaf", compact: true)
get_scf_submissions(slug: "decaf")

get_project with compact: true returns ~500 chars: slug, title, short description, category, tags, twitter_handles, website, other_names — enough to judge maturity and focus without the full row. get_scf_submissions returns every submission linked to that project, so you can see whether they're a one-shot applicant or a repeat grantee, and across which rounds. (You can also pass name: to get_scf_submissions for a fuzzy lookup when you don't have a slug.)

3. Expand from a specific submission

When one prior submission is dead-on, pivot to its neighbors by slug to widen the comparison set without re-guessing keywords.

find_similar_scf_submissions(slug: "recsAQHCghw9HIUvF", limit: 8)

This surfaces submissions embedding-close to that one — often adjacent problem framings the keyword query missed.

4. Reality-check the live ecosystem

A funded submission is past tense; you also want who is shipping now. Run a couple of SHORT search_directory queries on the core capability, then grow the cluster semantically from a good hit.

search_directory(query: "payroll", limit: 10)
search_directory(query: "payments", limit: 10)
find_similar_projects_semantic(slug: "decaf", limit: 8)

Keep search_directory terms short — it is keyword/ILIKE over title/description/other_names, so a long phrase like "automated payroll for distributed teams" returns 0. Use one or two words; reach for find_similar_projects_semantic to expand from a seed. This catches live competitors who never went through SCF and tells you whether your lane is empty, crowded, or integrable.

5. Confirm the category fit

Categories are a controlled vocabulary — propose into one of the live values, not an invented one. Pull them to be sure:

get_categories()

Live values: Applications, Developer Tooling, Education & Community, Financial Protocols, Infrastructure & Services. Match your idea to the same bucket the closest funded prior art used — divergence from peers is a flag to re-read your framing.

Worked example: "on-chain invoice financing for SMEs"

1. find_similar_scf_submissions(query: "on-chain invoice financing and factoring for small businesses on Stellar", limit: 10) → returns prior submissions. Read each award_type: say two are funded under Financial Protocols (RWA / credit angle) and one community proposal in the same lane was not awarded.
2. For the two funded rows, take their linked_project_slug → get_project(slug, compact: true) + get_scf_submissions(slug). You learn one team has applied across two rounds (momentum + a moat), the other was a single award.
3. The closest funded submission is dead-on, so find_similar_scf_submissions(slug: "rec…") surfaces adjacent framings — supply-chain finance, trade receivables — showing where the cluster is dense vs thin.
4. Reality check: search_directory(query: "invoice") and search_directory(query: "RWA") (short!) plus find_similar_projects_semantic from a seed show which issuers are live today.
5. get_categories() confirms Financial Protocols is the right bucket.

You now know: the problem is validated (funded prior art exists), the lane has an incumbent (differentiate hard), the open adjacency is trade-receivables, and the category is Financial Protocols.

Output: a positioning brief

Synthesize into a short brief. Use the bundled template: reference/positioning-brief-template.md. It covers:

• Closest prior submissions — title, round, award_type (funded?), linked_project_slug, one-line takeaway.
• What's differentiated — what your idea does that the closest funded prior art does not.
• Category fit — which of the 5 live categories, matched to peer submissions.
• Suggested framing — the headline and award track to pitch into.
• Overlap / crowding risk — funded incumbents and live (non-SCF) competitors, and whether to differentiate vs integrate.
• Open adjacencies — neighboring problem framings that look under-served.

Always attribute outcomes to the public SCF record (round + award_type), and distinguish "funded" from "merely proposed". This skill positions an idea against that record — it does not apply to SCF on the user's behalf.

Pointers

• Stellar Community Fund program details: https://communityfund.stellar.org
• Build guidance for whatever you pitch: https://developers.stellar.org