cyh-skill field-graph
Turn "who actually matters in field X" from a guess into a sourced, ranked graph — for any field, on whatever platform that field actually congregates on.
This Skill supports both Codex and Claude Code. In Codex, invoke it as
$cyh-field-graph; install the repository as cyh-field-graph under
~/.agents/skills/, with agents/openai.yaml providing Codex interface metadata.
Claude Code uses .claude-plugin/plugin.json and the allowed-tools frontmatter
above. Both clients follow the same workflow and safety boundaries. The method is
adapted from SimmerChan's Zhihu field-mapping case study
(zhuanlan.zhihu.com/p/2052179994911171124). It generalizes that case: the graph
algorithm is one thing, the platform it runs on is a swappable provider.
Core idea
Two signals, and the second is the important one:
- in-degree (follower / citation count) = fame.
- cross-source count = how many independent seed accounts endorse a person = peer recognition inside the niche.
PageRank intuition: being endorsed by several important, mutually-independent nodes beats being endorsed by many random nodes. The highest-follower account is often a field-adjacent celebrity, not the center. (In the source article: a 14万-follower name was not the center; a 2万-follower node endorsed by 7 independent core people was.)
And forward beats reverse: who an expert chooses to follow / cite is high-signal (30-40% on-topic); their followers are mostly noise (3-10%).
The pipeline — 5 stages
Run these in order. Stages 1–2 are reasoning; 3–5 use the scripts.
1. Define the field (定域)
State the field boundary in one sentence, and the disqualifier ("… but NOT general-purpose ML celebrities"). This is the yardstick for the Stage-4 domain filter. Without it, ranking drifts to whoever everyone follows.
2. Route the field to its platform (找阵地) → pick a provider
Where do this field's people actually congregate and leave a public endorsement
graph? This decides the provider. See PLATFORMS.md for the
full routing table; the short version:
| Field looks like… | Platform / graph | provider |
|---|---|---|
| open-source / dev tooling / infra | GitHub follows | github |
| academic / research / a paper's field | OpenAlex co-authorship | openalex |
| Chinese tech / 内容创作 / 知乎大V | Zhihu follows | cmd: or authorized browser collection + --edges |
| influencer / 种草 / creator | X, 小红书, 微博, B站 | authorized browser collection or supplied --edges |
| founders / VC / business | LinkedIn, X | authorized browser collection or supplied --edges |
Rule of thumb: use github/openalex when the field lives there (open API).
For API-less platforms, select a browser-collection lane when the user requested
or accepted browser collection; otherwise accept user-supplied --edges data and
report the unavailable collection surface.
3. Seed with the field's top ~50 (取种子)
Bootstrap roughly 40–50 representative head accounts before expansion:
# GitHub: a topic → owners + top contributors of its top repos
scripts/seed_bootstrap.sh --provider github --topic osint --n 40 --contributors > seeds.txt
# OpenAlex: a field → its most-cited authors
scripts/seed_bootstrap.sh --provider openalex --field "natural language processing" --n 50 --mailto you@x.com > seeds.txt
For API-less platforms with an authorized browser-collection lane, get the field's 榜单 / top list via cyh-browser-skill (a "top rec-sys 大V" list, an awards page, a leaderboard) and save it as seeds. Eyeball the list — bad seeds poison the whole graph.
4. Expand + rank (扩散 + 交叉信源排序)
Forward BFS over the seeds' endorsement lists, then rank by cross-source count.
# GitHub, 3 BFS rounds, enrich the head
scripts/field_graph.sh @seeds.txt --rounds 3 --promote 12 --top 40 --enrich --csv out.csv
# OpenAlex (co-authorship graph)
scripts/field_graph.sh @seeds.txt --provider openalex --mailto you@x.com --top 30 --enrich
# any platform you can script one hop of:
scripts/field_graph.sh @seeds.txt --provider 'cmd:<command that prints node {}\047s neighbors>'
# platform with no API: rank an authorized browser harvest or supplied edge file
scripts/field_graph.sh field --edges harvested_follows.tsv --top 40
Then domain-filter (the one manual step): as rounds grow, raw cross-source drifts toward field-adjacent celebrities everyone follows. Keep only nodes whose own work is in the field (Stage-1 boundary). The center = the highest-cross-source node still on-topic after this filter — often a mid-follower specialist.
5. Cross-verify, routed by persona (交叉验证)
A high-ranked node is a handle, not yet a person. Confirm who they are via the source that authoritatively covers their kind — not "check every site":
scripts/cross_verify.sh --gh sokra # auto-detect persona from GitHub
scripts/cross_verify.sh --name "Tri Dao" --persona research
code → GitHub/npm/PyPI · research → arXiv/OpenAlex/ORCID · influencer → 小红书/微博/
B站/X (cyh-browser-skill) · business → LinkedIn/Crunchbase/gsxt.gov.cn. Full routing +
the self-signed-only rule: SOURCES.md.
Providers (Stage 4 engine)
field_graph.sh is platform-agnostic; a provider answers one question — given a
node, which nodes does it endorse?
| provider | edge = | needs | node id |
|---|---|---|---|
github (default) |
who a user follows | gh (authed) |
login |
openalex |
a researcher's co-authors | curl, jq |
author id A… (or a name, auto-resolved — verify the echo) |
cmd:TPL |
stdout of TPL with an unquoted {} = node |
whatever TPL uses |
anything |
--edges FILE |
pre-collected seed<TAB>neighbor TSV |
— | anything |
Adding a field = adding a provider. If you can script "given X, list who X
endorses" for a platform, cmd: plugs it in with zero engine changes. For a
logged-in platform, an authorized browser-collection lane or user-supplied data
feeds --edges.
Keep the {} placeholder unquoted: the engine replaces it with a quoted positional
argument. Provider calls default to a 60-second timeout and 1 MiB output limit per
seed; use --timeout or --max-output-kb when a trusted provider needs more.
Pure shell — no Python, no runtime. github needs gh authenticated
(gh auth status); openalex needs curl + jq. Public REST endpoints only.
Reading the output
- Resolution scales with seeds. ~12 seeds tops out ~3 cross-sources; ~45 seeds reaches ~10. More rounds = finer head, but more drift (Stage 4).
- The center is the highest-cross-source node still on-topic after the domain filter — frequently a mid-follower specialist, not the 10k+/14万 name.
Scope & Red Lines
Maps publicly-active contributors of a field from data they chose to make public (their profile, their public follow/citation graph, their signed work). That is the legitimate use, and the only one.
Do not repurpose this to:
- build an exhaustive dossier on one private individual, or de-anonymize someone who deliberately uses a pseudonym / left their profile blank — stop at deliberate anonymity (in the source article: a core node with a blank GitHub was left un-identified, on purpose);
- pivot from a private identifier (phone, personal email, ID) to a person — this tool takes public accounts as seeds, by design, never the reverse;
- attach private/sensitive attributes (psychoprofiles, location inference, protected characteristics) to people.
Hiring / due-diligence: restrict to job-relevant, self-published professional signals and follow local consent law (PIPL / GDPR / FCRA). Real names inferred from an ID are "unconfirmed" until the person signed them publicly. Aggregating "everything about a person" is out of scope on purpose.