Stellarlight

stellar light is the ecosystem data layer for stellar — a single, machine-readable source of truth for projects, code, funding, stablecoins, dev activity, partners, and builders, queryable by humans, tools, and ai agents.

   
Category Ecosystem Visibility
Website https://stellarlight.xyz
Repository https://github.com/Stellar-Light/stellarlight
MCP server https://github.com/Stellar-Light/scout-mcp
Agent skill https://github.com/Stellar-Light/stellar-scout
First Released Jan 2026
Intake renewal (2026q3)
Budget Requested $40,000

Repository note: the codebase has migrated to the Stellar-Light organization; the MCP server and agent-skill repos are already public, and the main repo’s public flip lands this quarter (full-history secrets audit already clean). The packages are live on npm today: @stellar-light/scout-mcp and @stellar-light/api-client.

Project Description

stellar light is the ecosystem discovery and data layer for stellar. it brings together project data, stablecoin analytics, dev activity, github repo intelligence, the partner/anchor directory, funding and rfp data, and ecosystem research into one platform — and, as of this quarter, exposes all of it through a public api, an openapi spec, an mcp server, and a natural-language interface so ai agents and developer tools can consume it directly, not just humans through a ui.

before stellar light, project information was scattered, stablecoin data required checking multiple sources, code/repo activity had no single index, and there was no structured, authoritative data source that an ai agent could query about the stellar ecosystem. stellar light closes those gaps. builders find tools, code, and funded opportunities in minutes. institutions use it as a due-diligence layer. scf reviewers track project health between rounds. and the ecosystem’s emerging ai layer now has a fresh, structured source of truth to build on.

Team & Experience

Stellarlight is run by me (boxy00). i’ve been in the stellar ecosystem for the past 6 years, contributing to its growth via SCF, hackathons, VC programs, events, SDF programs and more.

Retroactive Impact

this quarter stellar light went from “a website you browse” to “a data layer the ecosystem can query.” the platform launched publicly, and — more importantly — the same data became machine-readable so ai agents and tools consume it directly.

public launch. stellarlight.xyz is live and public: the directory, stablecoin explorer, github leaderboard, dev-activity tracker, ideas/rfp platform, hackathon tracker, and blog are all in front of the ecosystem. the launch was covered in an ecosystem interview: https://x.com/lumenloop/status/2069451377223536659.

the agent-native data layer (the big shift). the plan for this quarter was to make the ecosystem’s data ai-queryable and plug into stella, SDF’s ecosystem assistant. stella is being sunset (shutting down at the end of the month), so instead of building for one bot i built an open interface any agent can consume — which turned out far more valuable and future-proof. shipped: a public rest api (24 endpoints across projects, repos, hackathons, builders, funding, research, partners), an openapi 3.1 spec at /api/openapi.json, an mcp server published to npm (@stellar-light/scout-mcp), a typed client (@stellar-light/api-client), and an installable stellar-scout skill. i also built a public skills marketplace at stellarlight.xyz/skills — a curated, filterable catalog of the ai skills, mcp servers, sdks, and tools available to stellar builders (merging SDF’s official skills.stellar.org catalog, stellar light’s own, and community submissions) so builders find and install the right agent tooling in one place. the ecosystem’s data — and the tools to use it — is now a machine surface, not just a ui.

the ecosystem’s ai agents already consume it. stellar light is already being used as a data source by the ecosystem’s emerging ai agents — including raven, the ai agent tyler van der hoeven (kalepail) is building at SDF, which sits on top of stellar light and lumenloop (raph’s research/media layer) as its data layers. i worked directly with tyler this quarter to make stellar light more consumable by raven, and measurably improved how well it routes to and answers from our data by hardening the openapi spec — a large, reproducible jump in correct routing. early proof that the ecosystem’s ai layer needs exactly this kind of fresh, structured, authoritative source, and that stellar light is becoming it.

ai across the platform. beyond serving agents, the platform runs its own ai. an ai data-cleaning pipeline normalizes, de-duplicates, re-categorizes, and flags stale/broken data across 900+ projects so the directory stays accurate at scale. i built the retrieval quality itself: a vector-searchable research corpus (SEPs, SCF handbook, dev docs, papers, security audits, incident reports) plus an indexed-and-scored github repo layer (~2,300 stellar/soroban repos ranked by freshness, traction, and SCF/hackathon/builder authority) so agents find the right code and the right source, not noise. every answer carries a confidence score (relevance + freshness + authority) so consumers — human or agent — know how much to trust it. a natural-language interface (/ask) puts all of this behind a plain-english question; it’s currently in private testing.

the partner portal (an ai product on its own). built the partner layer end to end: a directory of anchors, on/off-ramps, infrastructure, tooling, and audit firms, each enriched directly from the partner’s stellar.toml (supported assets, SEP-6/24/31, on/off-ramp capability, jurisdiction) to match the official stellar anchor directory. on top of it, an ai concierge that matches builders to the right partner from a plain-english need (“i need a USDC off-ramp in mexico”), and a full self-service portal (in beta): partners log in and maintain their profile through an ai-guided chat, get demand signals when builders search for them, and receive quarterly freshness check-ins so their data never goes stale. a claim flow lets real companies take ownership of their listing.

data quality + integrity. golden-answer evaluations, retrieval chunk hygiene, org/builder attribution (“who built X” → the company behind each project), defunct-project handling so dead projects stop ranking as active, and a daily drift guard that asserts the api, the openapi spec, and the docs never disagree.

content. thesis-driven ecosystem reports published on /blog covering the state of stellar, the defi landscape, SCF funding, stablecoins, developer activity, and the hackathon pipeline.

Past Deliverables

2026 Q2

The public API — 24 endpoints (live at /api/openapi.json)

built a full agent-facing rest api. the endpoints:

  • projects — GET /api/projects/search (keyword + semantic project discovery, with org attribution
    • inline code references)
  • repos — GET /api/repos/search (indexed + scored github repos), GET /api/repos/explain (source-grounded answers to deep code questions, routed to the authoritative repo)
  • research — GET /api/research (vector search over the knowledge corpus with confidence scores)
  • hackathons — GET /api/hackathons, GET /api/hackathons/{slug}, GET /api/hackathons/compare (merged curated + live DoraHacks feed)
  • builders — GET /api/builders (stellar passport builder profiles)
  • partners — GET /api/partners, GET /api/partners/{slug}, POST /api/partners/match (ai matchmaking), POST /api/partners/assistant (concierge), POST /api/partners/onboard, POST /api/partners/submit-listing
  • funding — GET /api/rfps (SCF rfps / sponsor briefs)
  • analytics — GET /api/clusters (topic clustering + crowdedness), GET /api/analyze (cross-ecosystem rollups), GET /api/leaderboard (ranked active projects + Electric Capital dev macro)
  • skills — GET /api/skills, GET /api/skills/{name}
  • meta — GET /api/status (health + per-source freshness), GET /api/changelog, POST /api/feedback

every endpoint is documented in an openapi 3.1 spec with stable operationIds and per-endpoint “use when / not for” routing guidance, permissive CORS, and a version header — so codegen tools and ai agents get typed access with no hand-rolled wrappers.

verify: https://stellarlight.xyz/api/openapi.json · sample: https://stellarlight.xyz/api/projects/search?q=defi · https://stellarlight.xyz/api/status

MCP server, typed client, and installable skill

@stellar-light/scout-mcp — an mcp server (18 tools) published to npm, so any mcp client (claude, cursor, etc.) can query the whole data layer. @stellar-light/api-client — a typed typescript sdk on npm. stellar-scout — an installable skill (SKILL.md + references) for coding agents. all three have a public home at stellarlight.xyz/scout — a landing page with one-line install commands, the full tool reference, and worked examples so a builder or agent can go from “never heard of it” to installed in a minute. mirrored to public repos and listed in skill registries.

verify: https://stellarlight.xyz/scout · https://www.npmjs.com/package/@stellar-light/scout-mcp · https://www.npmjs.com/package/@stellar-light/api-client

Skills marketplace

public catalog of ai skills, mcp servers, sdks, and tools for stellar builders at stellarlight.xyz/skills — merges SDF’s official skills.stellar.org skills, stellar light’s own, and approved community submissions, each with an install command and compatibility info. includes a community submission flow.

verify: https://stellarlight.xyz/skills

AI systems

the ai work spans the whole platform, not one feature:

  • retrieval + routing quality work — golden-answer evaluations, chunk hygiene, and a measured, reproducible improvement in how well an external ai agent routes to and answers from our data.
  • ai data-cleaning pipeline — normalizes, de-duplicates, re-categorizes, and flags stale/broken data across 900+ projects so the directory stays accurate at scale.
  • semantic retrieval — vector search (voyage embeddings) over both the research corpus and the project directory, with a keyword→semantic fallback.
  • per-response confidence scoring — every answer carries a relevance + freshness + authority score so consumers know how much to trust it.
  • ai partner concierge — a tool-using assistant that matches builders to partners from a plain-english need and is hallucination-guarded (it can only surface real, indexed partners).
  • ai-guided partner maintenance — logged-in partners update their profile by chatting; the model extracts structured fields from the conversation.
  • natural-language search (/ask) — one question fans out across the project directory, research corpus, and partner data and returns grounded, cited answers; currently in private testing.

verify: https://stellarlight.xyz/partners (live ai concierge) · https://stellarlight.xyz/api/research?q=soroban (semantic retrieval + confidence scores) · /ask is in private testing (not yet public)

Research corpus + code intelligence

vector-searchable knowledge corpus — SEPs, SCF handbook, dev docs, papers, security audits, incident reports — with confidence scoring, plus an indexed-and-scored github repo layer (~2,300 stellar/soroban repos ranked by freshness, traction, and SCF/hackathon/builder authority) surfaced via /api/repos/search and inline on project pages. /api/repos/explain routes deep code questions to the authoritative repo and returns a source-grounded answer.

verify: https://stellarlight.xyz/api/research?q=soroban%20authorization · https://stellarlight.xyz/api/repos/search?q=wallet

Partner / anchor data layer + self-service portal

full partner layer live at stellarlight.xyz/partners: a directory of anchors, on/off-ramps, infrastructure, tooling, and audit firms, each enriched directly from the partner’s stellar.toml (assets, SEP-6/24/31, on/off-ramp, jurisdiction) to match the official stellar anchor directory. an ai concierge for builder→partner matching, and a self-service portal (in beta) where partners log in, maintain their profile through an ai-guided chat, see demand signals when builders search for them, and get quarterly freshness check-ins. a claim flow lets companies take ownership of their listing.

verify: https://stellarlight.xyz/partners

Data sources, pipelines & freshness

integrated and kept fresh via automated pipelines: SDF entity airtable (projects + grants), the github api (dev activity, stars, commit recency, repo metadata), goldsky (stablecoin on-chain data), defillama (defi tvl), rwa.xyz (rwa tvl), dorahacks (live hackathons), stellar passport (builder profiles), electric capital (developer activity), and partners’ own stellar.toml files. 900+ projects, ~2,300 repos, 100+ builders, and the partner directory are refreshed on scheduled jobs, with a /api/status endpoint exposing per-source freshness so consumers always know how current the data is.

verify: https://stellarlight.xyz/api/status

Data quality + integrity

golden-answer evals, retrieval chunk hygiene, org/builder attribution (“who built X”), an “inactive” lifecycle state so defunct projects stop ranking as active, and a daily api ⇄ openapi ⇄ docs drift guard in CI that fails if the live api, the spec, and the docs ever disagree.

verify: https://stellarlight.xyz/api/changelog

Platform, dashboards & quality-of-life

continuous improvements to the human-facing platform across the quarter:

  • hackathon tracker — surfaces upcoming and active stellar hackathons (merged curated + live dorahacks feed) and tracks post-hackathon project status (built / in progress / abandoned), giving scf and the ecosystem visibility into which hackathon projects turn into real products.
  • developer-activity dashboard + leaderboard — a ranked, filterable view of active projects by github stars / open issues / commit recency over selectable time ranges, bundled with an electric capital ecosystem dev macro (monthly active devs, commit trends). the ecosystem’s first automated, transparent view of what’s actually being maintained.
  • entities & organizations — org / company pages that roll each organization’s projects, funding, and activity into a single profile (“who’s behind what”), sorted so the most complete, active orgs lead.
  • project pages — github stats, tvl charts, and blog/rss feeds embedded per project; public transparency + change logs on project data.
  • stablecoin explorer — historical dashboards (14d / 90d / 1y), issuer leaderboard, and top-issuer breakdowns across 22+ verified stellar stablecoins (supply, market cap, holders, volume, defi liquidity, peg stability).
  • ideas + rfp platform — curated project ideas and the live scf rfp section feeding builders directly into scf programs, with difficulty ratings, category filters, and a moderation workflow. this quarter we’re pushing out the current (q2) round of scf rfps — keeping the section populated with the active briefs and surfacing them to builders (also mirrored to the scf gitbook).
  • ui/ux — a cleaner, consistent interface across the whole site, mobile-first layouts, advanced filters, and navigation that connects every surface (directory, ask, partners, skills, leaderboard, hackathons, ideas, blog).

900+ projects and entities indexed and categorized throughout.

verify: https://stellarlight.xyz/leaderboard · https://stellarlight.xyz/hackathons · https://stellarlight.xyz/entities · https://ideas.stellarlight.xyz · https://ideas.stellarlight.xyz/rfps

Content and ecosystem reporting

thesis-driven ecosystem reports published on /blog:

verify: https://stellarlight.xyz/blog

proof: everything above is live and verifiable at stellarlight.xyz, stellarlight.xyz/partners, stellarlight.xyz/skills, stellarlight.xyz/leaderboard, the full api spec at stellarlight.xyz/api/openapi.json, and on npm (@stellar-light/scout-mcp, @stellar-light/api-client). launch interview: https://x.com/lumenloop/status/2069451377223536659.

Proposed Impact

the ecosystem is going ai-native, and stellar light is positioned to be the data layer it runs on. tyler van der hoeven (kalepail) at SDF is building raven — an ai agent designed to become the way the entire stellar ecosystem asks questions: builders scoping a project, institutions doing due diligence, scf reviewers evaluating grants, newcomers finding their footing. raven doesn’t hold the data itself; it sits on top of data layers and surfaces them. two of those layers are stellar light and lumenloop (raph’s research/media layer). stellar light is the authoritative source for the hard, structured stuff — projects, code, repos, funding, partners, builders, and live activity. that is the position this quarter set up, and it’s why q3 matters: as the ecosystem’s ai layer takes off, stellar light becomes load-bearing infrastructure underneath it. the north star for q3 is to make stellar light the deepest, freshest, and most correct data layer that raven — and every builder, institution, and agent — can rely on.

that dependence is no longer a plan — it is externally verifiable today, in raven’s own public repo. none of these links are ours; they are the consumer’s own code and process:

  • raven is live and queries stellar light through a dedicated adapter — verify: https://github.com/kalepail/stellar-raven/blob/main/src/adapters/scout.ts
  • raven’s routing catalog consumes stellar light’s machine-routing metadata (the x-routing extension we shipped for it) as a scored input — verify: https://github.com/kalepail/stellar-raven/commit/baabc06b13ef (“x-routing extension scored as lever 7”)
  • raven’s ci monitors stellar light’s live contract and automatically files a drift review on every release we ship — verify: https://github.com/kalepail/stellar-raven/issues/21
  • raven’s maintainer runs a public, 53-item quality ledger on stellar light — the most-audited data service in his program — and his own tooling marks our fixes fixed-upstream, most closed within days of filing — verify: https://github.com/kalepail/stellar-raven/tree/main/improvements/stellar-light-scout
  • on our side, every release is eval-gated before it reaches raven (recall floors, answer-correctness golden set, contract-honesty probes), and scf award data itself is verdict-verified weekly against communityfund.stellar.org — verify: https://stellarlight.xyz/api/changelog

this two-way loop — his ci reviewing our contract, our evals gating what he consumes — is the working model for how agent data layers should hold each other honest, and stellar light is its reference implementation. funding this quarter funds the load-bearing half of that loop.

1. be the data + code layer raven and the ecosystem depend on. this quarter i already worked directly with tyler to make stellar light more consumable by raven — hardening the api and openapi spec, fixing how our data routes and answers, and reconciling the contract so raven can trust it. q3 goes deeper. the hardest, highest-value ecosystem questions are code-level and current-state (“which crate and version is right,” “which CAP added this host function,” “what’s the current cli path to scaffold a contract”), and no data source answers them well today. i’m extending stellar light to — scoring live soroban repo code, matching it against the docs and CAP/protocol history, layered on top of the projects/funding/partner/repo data already indexed — so that when raven (or any agent) surfaces an answer, it’s grounded in a source that’s correct and current, not a guess. tyler has explicitly flagged this code-truth layer as the piece that makes stellar light irreplaceable rather than duplicative. it’s not a side project — it’s the same data-layer mission, going one level deeper.

2. a continuous eval + improvement loop. the game once the plumbing is in place is: run a large, growing question set against our data (from raven’s evals, from real questions builders ask across the ecosystem, from anywhere), find the low-scoring answers, diagnose why, fix the data or the endpoint, and repeat. i’ll institutionalize this so stellar light measurably improves every cycle instead of drifting — the only way a data layer stays trustworthy as the ecosystem grows around it.

3. the partner portal as a real product. finish the partner layer: the anchor / on-off-ramp / infrastructure / tooling / audit-firm directory with the ai concierge for builders, partner self-service maintenance, and quarterly freshness check-ins. this gives builders a trustworthy “who do i integrate with” answer, gives institutions a real map of stellar’s on/off-ramp and infra providers, and gives partners a reason to keep their own data accurate — a self-sustaining data loop that also feeds raven.

4. maintain, grow, and integrate. keep stellar light healthy and expanding: data-freshness pipelines, ranking and relevance quality, new data sources, uptime, and the discovery surfaces (directory, leaderboard, stablecoin explorer, research corpus, hackathon + rfp pipelines) that builders and scf use directly.

the bet is simple: stellar’s ecosystem is becoming measurable and queryable through ai, and the agents doing it — starting with raven — need a data layer that is fresh, structured, deep, and correct. that layer is stellar light. this quarter proved the direction; q3 makes it the indispensable foundation the ecosystem’s ai layer is built on.

Proposed Deliverables

1. code + current-state intelligence layer — the raven dependency

build the code-truth layer: score live soroban/stellar repo code and match it against the docs and CAP/protocol history, layered on top of the repo/project/funding/partner data already indexed, so that agents (raven and any other) get grounded, current answers to code-level questions — which crate and version is right, which CAP added a host function, the current cli path to scaffold a contract — instead of guesses. expose it through the existing api / openapi / mcp / skill surfaces so it’s consumed the same way as everything else. ecosystem value: the highest-value ecosystem questions are code-level and current-state, and no data source answers them well today; tyler has explicitly flagged this as the piece that makes stellar light irreplaceable rather than duplicative. measurable: code-truth endpoint live and documented in the openapi spec, answering a defined set of code/current-state questions with sourced references.

2. continuous eval + improvement loop

institutionalize a repeatable evaluation cycle: run a large, growing question set — from raven’s evals, from real questions builders ask across the ecosystem, and from our own golden set — against the live data layer, score the answers, diagnose the low-scoring ones, and fix the data or the endpoint. ecosystem value: a data layer only stays trustworthy if it measurably improves as the ecosystem grows around it, rather than drifting. measurable: eval harness running on a regular cadence with a tracked answer-quality score that improves over the quarter, and regressions caught before they ship (drift guard + golden evals in CI).

3. partner portal to general availability

take the partner layer out of beta: onboard real anchors, on/off-ramps, infrastructure, tooling, and audit-firm partners onto the self-service portal, ship the claim + ownership-verification flow, keep the ai concierge matching on real stellar.toml data (assets, SEP-6/24/31, on/off-ramp, jurisdiction), and run the quarterly freshness check-ins so listings stay current. ecosystem value: builders get a trustworthy “who do i integrate with” answer, institutions get a real map of stellar’s on/off-ramp and infra providers, and partners get a reason to keep their own data accurate — a self-sustaining loop that also feeds raven. measurable: portal out of beta, partners live with maintained profiles, concierge matching on structured fields, and freshness check-ins sending.

4. data pipelines, freshness + ranking quality — ongoing

maintain and harden every automated pipeline (sdf airtable, github, goldsky, defillama, rwa.xyz, dorahacks, stellar passport, electric capital, and partners’ stellar.toml) and keep ranking/relevance quality high across project search, repo search, and clusters. add new data sources where they strengthen the layer, and keep the api ⇄ openapi ⇄ docs drift guard green so the contract never lies. ecosystem value: data is only useful if it’s fresh and correct; this keeps stellar light load-bearing infrastructure rather than a stale directory. measurable: pipelines running with no significant downtime, new projects/stablecoins/repos reflected within ~1 week, drift guard green, and /api/status freshness current.

5. scf program support, rfp + hackathon maintenance, and reporting — ongoing

keep the rfp section populated with the current (q2) round of scf rfps and surface them to builders (also mirrored to the scf gitbook); maintain the ideas platform, the hackathon tracker with post-hackathon project status (built / in progress / abandoned), and builder profiles; and publish data-grounded ecosystem reports over the quarter. ecosystem value: stellar light feeds builders directly into scf programs and gives the ecosystem visibility into what’s being built, funded, and shipped. measurable: q2 rfps live and current, ideas + hackathon trackers current, and a set of ecosystem reports published over the quarter.

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