Curation × Trust Network — Use Cases (deep)

A long-form companion to the meta wiki. Each use case below is rated against the 6 structural criteria (abundance / risk / heterogeneity / no-single-arbiter / transitivity / audit-trails). Scale: ★ to ★★★★★.

Software & agent ecosystems

Claude Code Skills — ★★★★★

Abundance: 7,000+ skills (SkillHub) and growing weekly
Risk: Code-execution on dev box; data exfil; credential theft
Quality variance: 13% with critical vulns (Tech Leads Club); also some genuinely excellent
No single arbiter: Anthropic explicitly cannot vet every community skill
Transitivity: Recommendation chains work well ("X uses Y because Z vouched")
Audit: Static scan helps (Skills Directory) but behavioral audits are rare
Catalog: @jacobcole/trusted-claude-skills/index

OpenClaw Skills — ★★★★★

5,400+ skills, 820+ confirmed malicious in audit
Cryptominers, credential harvesters, exfil patterns
Default-untrusted is the only safe stance
Catalog: @jacobcole/trusted-openclaw-skills/index

MCP Servers — ★★★★

Growing fast; many implementations of the same protocol
Each MCP server can issue tool calls; full process trust
No verified registry yet; provenance often murky
Open opportunity for a curated MCP-server trust catalog

AI coding agents (Claude Code, Cursor, Aider, Codex CLI, Antigravity, OpenCode, Continue.dev, etc.) — ★★★

Many overlapping ones, switching cost is days
Risk is mostly time/lock-in, not security (these are themselves trusted at install)
Trust-net signal: which devs you respect actually use which one daily

Browser extensions — ★★★★★

The original case. Same-origin power, vast catalog, occasional malware
Chrome Web Store does some curation; not enough
People still reuse trust-net cues: "this one is by [respected person]"

VS Code / Cursor extensions — ★★★★

Run inside the IDE, can read all open code
Marketplace has done some vetting but high-trust extensions are still community-known

Open-source LLM model weights — ★★★

Hundreds of variants; possible backdoors; license-status varies
Hugging Face is the registry; trust signals are model-card author + benchmarks
Emerging audit space (safetensors provenance, weight-fingerprinting)

Health & body

Bodywork practitioners (chiro, PT, ART, Rolfing, qigong) — ★★★★★

Local, heterogeneous, hard to evaluate from outside
Wrong choice = months of pain or worse
Trust-net works extremely well ("my friend Alice cured her RSI with X") — most existing solutions are wrong-modality (Yelp reviews don't capture this)
See Health Log (private)

Supplements / longevity protocols — ★★★★

Vast literature, lots of hype
Most reviewers are conflicted (selling products) or unqualified
Need a trust net that flags conflict-of-interest disclosures explicitly

Mental-health / contemplative teachers — ★★★★★

Years of life invested per choice
Cult dynamics are a real failure mode
Reputation networks already exist informally; making them legible helps a lot

Diet / nutrition advice — ★★★★

Same shape as supplements but worse — fads, ideology, expert disagreement

People

Investors / VCs — ★★★★★

The existing trust net (warm intros, founder backchannels) is exactly this pattern, just opaque
A semi-private trust-graph wiki is huge value
See Investors Wiki (private)

Founders / collaborators / hires — ★★★★★

"Should I work with this person?" — high stakes, no central truth
Reference-checking is informal trust-graph traversal
Risk: reputational liability of writing things down

Service professionals (lawyers, accountants, doctors) — ★★★★

Yelp doesn't work; word-of-mouth does
Trust net + segment-by-need is the right shape

Information & culture

Books to read — ★★★

Stakes are time, not money/health
Trust-net (Goodreads-friends, blog recommendations) already works decently
Curation value is sorting for a specific need state, not "best ever"

News / commentary sources — ★★★★★

Bias and ownership matters; readers usually can't tell
Existing efforts: media-bias-fact-check, allsides
Strong trust-net case: who in your circle reads which sources, and why
See lists-to-curate items #16 (media bias map) and #14 ("good commentaries")

Academic papers / fields — ★★★★

Trust net = whose reading list do I borrow?
Citation graphs are an implicit trust net but lossy

Quotes / aphorisms / wisdom — ★★

Lower stakes; mostly aesthetic
See Favorite Quotes

Local / activities

Bay Area activities — ★★

Lower stakes, low malice risk
Trust net useful but soft

Restaurants / venues — ★★

Lower stakes, but algorithmic curation has degraded badly
Friend-recs > Yelp consistently

Events / community houses — ★★★

Higher stakes (you're committing time + money + presence)
Friends' attendance signals are gold

Opportunities & alerts

Contests / awards / grants — ★★★

Many exist, hard to find the right one
See lists-to-curate item #24 (Berggruen + others)

Fitness benchmarks — ★★★

Many proposed; few rigorous
See lists-to-curate item #7

Patterns observable across all these

Algorithmic recommendation engines lose to trust nets when (a) stakes are real and (b) the long tail dominates.
Existing trust nets are mostly informal and lossy — phone calls, DM'd intros, Slack "any recs?" threads. Making these legible is the win.
Privacy varies by case — investors and people-graphs need privacy; books and skills don't.
Negative endorsements (denylists) are rarer and more valuable than positive ones. AI Makers' "5 OpenClaw skills to avoid" is exemplary.
The reviewer is the unit of trust, not the review. A good curation system makes reviewers identifiable and stake-holding.

What WikiHub should support to serve these cases

Per-page endorsements with reviewer identity and timestamp (signed if possible)
Reviewer profile pages with their endorsement history, expertise tags, conflicts disclosed
Reusable trust assertions (page → page) so traversal is a graph query, not a re-read
Visibility scoping so private trust nets (e.g. investor wiki) work alongside public ones
Revocation as a diff (git already gives this for free)
Denylists as first-class citizens, not afterthoughts