| name | stellar-project-dossier |
| description | Build a due-diligence profile of a single Stellar project from the LumenLoop directory — details, related content, talks, SCF history, and similar projects. Use when the user names a project and asks to vet, profile, research, or summarize it, or wants a dossier or background on a Stellar team. |
| user-invocable | true |
Stellar Project Dossier
Assemble a structured due-diligence dossier on ONE Stellar project: what it is,
its category/tags/links, its Stellar Community Fund (SCF) funding history, what
the ecosystem has written and said about it, and the projects most like it. This
skill runs entirely on LumenLoop's free, read-only Stellar ecosystem MCP
(https://mcp.lumenloop.com) — every fact below comes from a public directory,
the public SCF record, and LumenLoop's editorial/AV/research library.
When to use this skill
- The user names a Stellar project and asks to "vet", "profile", "research",
"do diligence on", or "summarize" it.
- You need a project's funding track record (which SCF rounds, what award type).
- You want to know what articles, talks, podcasts, or events discuss a team —
with citable source urls.
- You need a list of comparable / competing projects to position one team.
- You are prepping a one-pager or background brief on a Stellar builder.
Related skills
- New to the MCP? Connect and learn the toolset first →
../lumenloop-mcp-connect/SKILL.md
- Mapping a whole sector instead of one project →
../stellar-ecosystem-scout/SKILL.md
- Positioning an SCF idea against prior submissions →
../scf-submission-radar/SKILL.md
- Choosing which project to depend on (wallet / oracle / anchor / DEX) →
../stellar-integration-finder/SKILL.md
- Building on Stellar (Soroban contracts, JS stellar-sdk + Freighter + Stellar
Wallets Kit, classic assets + trustlines + SAC, Stellar RPC / Horizon data,
SEP/CAP standards, the Reflector oracle): see the Stellar developer docs at
https://developers.stellar.org.
The dossier pipeline (run in order)
| # | Step | Tool | Yields |
|---|
| 1 | Resolve the slug | search_directory(query) | candidate slugs |
| 2 | Pull the profile | get_project(slug) | full row (links, region, tags) |
| 3 | Funding history | get_scf_submissions(slug) | rounds + award_type |
| 4 | What's written/said | find_content_about_project(slug, limit) | articles/AV/events + urls |
| 5 | Direct quotes | find_av_passages(query) | transcript passages to quote |
| 6 | Comparables | find_similar_projects_semantic(slug, limit) | competitors / neighbors |
| 7 | (Optional) connections | get_related_projects(content_id, content_type) | projects a key item co-mentions |
Each step is independent of the others except for the slug — once you have it
from step 1, steps 2–6 can be issued together.
1. Resolve the slug
The user gives a name; the tools key off a slug. Resolve it first.
search_directory(query="lobstr")
search_directory is keyword/ILIKE over title, description, and other_names.
Keep the term SHORT — one or two words. A long natural-language phrase
("the lobstr non-custodial wallet app") matches nothing (a zero-hit response
includes a recovery hint). It returns one JSON object shaped like:
{ count: 1, projects: [
{ slug: "lobstr", title: "LOBSTR", category: "Applications",
tags: ["Wallet Infra", "Custody"], website: "https://lobstr.co",
description: "Non-custodial Stellar wallet ..." } ] }
Pick the row whose title/website matches the user's intent and lock its
slug. If the user already supplied a slug, skip to step 2. If the name is
ambiguous or returns nothing, try a shorter or alternate term, then confirm the
match with the user before spending the rest of the pipeline on it.
2. Pull the profile
get_project(slug="lobstr")
The default response is the FULL row — long-form description, based_in /
operating_region, twitter_handles, website, all tags, other_names, and
more. That is what a dossier wants. If you only need a quick header (title,
short description, category, tags, links), pass compact: true for a slim
~500-char shape and save tokens:
get_project(slug="lobstr", compact=true)
Record: category (one of the five from get_categories — Applications,
Developer Tooling, Education & Community, Financial Protocols, Infrastructure &
Services), project tags (vocabulary from get_project_tags_vocabulary, e.g.
"Wallet Infra", "Cross-Border Payments", "SDK"), region, and every link.
3. Funding history (SCF)
get_scf_submissions(slug="lobstr")
Returns the project's Stellar Community Fund submissions. For each, note the
round and award_type — this is the funding track record. If the slug
returns nothing but you suspect SCF history under a slightly different name,
fall back to the fuzzy name lookup:
get_scf_submissions(name="LOBSTR")
An empty result is itself a finding: "no SCF submissions on record." State it
plainly rather than omitting the section.
4. What's written and said about it
find_content_about_project(slug="lobstr", limit=10)
Returns content across domains (articles, AV/video, events, research) that
DISCUSSES this project. Each item carries title, url, domain,
publishing_date, and an AI-generated summary / long_summary. These are
LumenLoop's SUMMARIES, not full article text — cite the url for every claim
and never quote the summary as if it were the source's exact words.
Ranking here is recency-aware, so recent coverage (a security incident or
exploit, an audit, a launch) surfaces alongside the canonical/older items — for
a due-diligence dossier, scan explicitly for any incident or audit and date it
(publishing_date, flagged as possibly outdated if old). The corpus is the
source of record for these ecosystem facts and is dated, so cite the date;
reach for live web only for facts it can't hold (e.g. a current on-chain TVL or
an upstream package version), not for "has this project had an incident".
If you want a theme-scoped sweep instead (e.g. only what's been said about the
project's cross-border payments work), use semantic content search and narrow
the types:
search_content_semantic(query="LOBSTR cross-border payments",
types=["articles","av"], limit=8)
5. Direct quotes from talks and podcasts
find_av_passages(query="LOBSTR wallet")
This matches per-chunk inside AV transcripts, so it surfaces the actual
passage where something was SAID — ideal for a pull-quote in the dossier.
Optionally bound it with date_start / date_end to stay recent. Attribute
each quote to its source item and url.
6. Comparables / competitors
find_similar_projects_semantic(slug="lobstr", limit=8)
This is semantic project→project similarity (embeddings), which beats
tag/category matching for finding real competitors and neighbors. Use the
result as the "comparables" section. Spot-check one or two with
get_project(slug, compact=true) to confirm they are genuine peers.
Do NOT use get_related_projects for this — it maps a piece of CONTENT to
the projects it mentions, not project→project. For project similarity always
use find_similar_projects_semantic.
7. (Optional) Ecosystem connections via a key content item
When a step-4 item is especially relevant (a roundup, a partnership
announcement), expand who ELSE it names to map the project's neighborhood. Take
that item's id and its content type and call:
get_related_projects(content_id=4217, content_type="article")
content_type is one of article | av | event | research (plural/alias
accepted); the source must be published. This returns the other projects that
item mentions — useful for "appears alongside" context. Then
get_project(slug, compact=true) any newly surfaced slug worth a line.
Worked example: the "lobstr" dossier
search_directory(query="lobstr") → one strong hit, slug lobstr,
category Applications, tags include "Wallet Infra".get_project(slug="lobstr") → full profile: non-custodial Stellar wallet,
website, twitter handle, region, long description.get_scf_submissions(slug="lobstr") → any SCF rounds + award_type (or
"none on record").find_content_about_project(slug="lobstr", limit=10) → articles and talks
mentioning LOBSTR, each with a url to cite.find_av_passages(query="LOBSTR wallet") → a quotable passage from a talk.find_similar_projects_semantic(slug="lobstr", limit=8) → comparable
wallets/apps to list as the competitive set.- (Optional) pick the most relevant article from step 4 →
get_related_projects(content_id=<id>, content_type="article") → who else
it names.
Assemble the results into the dossier structure below.
Output: the dossier
Produce a single structured brief with these sections:
- Overview — one-paragraph what-it-is (from step 2).
- Profile — category, project tags, region, all links (website, twitter).
- SCF / funding — each submission's round + award_type, or "none on record".
- What people say — 3–6 bullets, each a one-sentence takeaway + the source
url; include one attributed pull-quote from step 5 if available.
- Comparables — 3–8 similar projects (slug + one-line why), from step 6.
- Open questions — gaps the public data did not answer (e.g. no recent
content, no funding record, unclear traction) to flag for follow-up.
Full section-by-section template, with a filled example and citation rules →
reference/dossier-template.md.
Gotchas
search_directory is ILIKE keyword search — SHORT terms only; long phrases
return 0. For topic/concept discovery use the semantic tools.- The content tools return AI SUMMARIES + a source
url, not full article
text. Cite the url; never present a summary as a verbatim quote.
get_related_projects is content→projects (needs content_id +
content_type), NOT project→project. Use find_similar_projects_semantic
for comparables.get_project defaults to a large full row; pass compact: true when you
only need the header.- Empty results are findings — state "none on record" rather than dropping a
section.
- If a tool stops appearing after a server update, reconnect the connector (see
../lumenloop-mcp-connect/SKILL.md).
Further reading
- LumenLoop MCP overview and full tool catalog →
../lumenloop-mcp-connect/SKILL.md
- Building on Stellar (Soroban, stellar-sdk, assets, data, standards,
Reflector) → https://developers.stellar.org
