printf '%s\n' 'EXOPRIORS_API_KEY=exopriors_...' >> .env
set -a && source .env && set +a

echo "$EXOPRIORS_API_KEY"

npx skills add exopriors/skills
npx skills update

import { wrapFetchWithPayment } from 'x402-fetch';

const paidFetch = wrapFetchWithPayment(fetch, walletClient);
const resp = await paidFetch('https://api.exopriors.com/v1/scry/query', {
  method: 'POST',
  headers: { 'content-type': 'text/plain' },
  body: 'SELECT 1 LIMIT 1',
});

Step 1: Get dynamic context + update advisory
GET https://api.exopriors.com/v1/scry/context?skill_generation=20260313
Authorization: Bearer $EXOPRIORS_API_KEY

Step 2: Get schema
GET https://api.exopriors.com/v1/scry/schema
Authorization: Bearer $EXOPRIORS_API_KEY

Step 3: Run query
POST https://api.exopriors.com/v1/scry/query
Authorization: Bearer $EXOPRIORS_API_KEY
Content-Type: text/plain

WITH hits AS (
  SELECT id FROM scry.search('RLHF reinforcement learning human feedback',
    kinds=>ARRAY['post'], limit_n=>100)
)
SELECT e.uri, e.title, e.original_author, e.original_timestamp, e.score
FROM hits h
JOIN scry.entities e ON e.id = h.id
WHERE e.source = 'lesswrong'
  AND e.content_risk IS DISTINCT FROM 'dangerous'
ORDER BY e.score DESC NULLS LAST
LIMIT 20

{
  "columns": ["uri", "title", "original_author", "original_timestamp", "score"],
  "rows": [["https://...", "My RLHF Post", "author", "2025-01-15T...", 142], ...],
  "row_count": 20,
  "duration_ms": 312,
  "truncated": false
}

User wants to search the ExoPriors corpus?
  |
  +-- Ambiguous / conceptual ask? --> Clarify intent first, then use
  |     scry-vectors for semantic search (optionally hybridize with lexical)
  |
  +-- By keywords/phrases? --> scry.search() (BM25 lexical over canonical content_text)
  |     +-- Specific forum?  --> pass mode='mv_lesswrong_posts' or kinds filter
  |     +-- Reddit?          --> START with scry.reddit_subreddit_stats /
  |                              scry.reddit_clusters() / scry.reddit_embeddings
  |                              and trust /v1/scry/schema status before
  |                              using direct retrieval helpers
  |     +-- Large result?    --> scry.search_ids() (id+uri+kind, up to 2000)
  |
  +-- By structured filters (source, date, author)? --> Direct SQL on MVs
  |
  +-- By semantic similarity? --> (scry-vectors skill, not this one)
  |
  +-- Hybrid (keywords + semantic rerank)? --> scry.hybrid_search() or
  |     lexical CTE + JOIN scry.embeddings
  |
  +-- Author/people lookup? --> scry.actors, scry.people, scry.person_accounts
  |
  +-- Academic graph (OpenAlex)? --> scry.openalex_find_authors(),
  |     scry.openalex_find_works(), etc. (see schema-guide.md)
  |
  +-- Need to share results? --> POST /v1/scry/shares
  |
  +-- Need to emit a structured observation? --> POST /v1/scry/judgements
  |
  +-- Scry blocked / missing obvious results? --> POST /v1/feedback

curl -s "https://api.exopriors.com/v1/scry/context?skill_generation=20260313" \
  -H "Authorization: Bearer $EXOPRIORS_API_KEY"

curl -s "https://api.exopriors.com/v1/feedback?feedback_type=bug&channel=scry_skill" \
  -H "Authorization: Bearer $EXOPRIORS_API_KEY" \
  -H "Content-Type: text/plain" \
  --data $'## What happened\n- Query: ...\n- Problem: ...\n\n## Why it matters\n- ...\n\n## Suggested fix\n- ...'

curl -s "https://api.exopriors.com/v1/feedback?limit=10" \
  -H "Authorization: Bearer $EXOPRIORS_API_KEY"

WITH c AS (
  SELECT id FROM scry.search('your query here',
    kinds=>ARRAY['post'], limit_n=>100)
)
SELECT e.uri, e.title, e.original_author, e.original_timestamp
FROM c JOIN scry.entities e ON e.id = c.id
WHERE e.content_risk IS DISTINCT FROM 'dangerous'
LIMIT 50

SELECT subreddit, total_count, latest
FROM scry.reddit_subreddit_stats
WHERE subreddit IN ('MachineLearning', 'LocalLLaMA')
ORDER BY total_count DESC

SELECT entity_id, uri, title, original_author, score, original_timestamp
FROM scry.mv_arxiv_papers
WHERE original_timestamp >= '2025-01-01'
ORDER BY original_timestamp DESC
LIMIT 50

SELECT e.source::text, COUNT(*) AS docs, MAX(e.original_timestamp) AS latest
FROM scry.entities e
WHERE e.original_author ILIKE '%yudkowsky%'
  AND e.content_risk IS DISTINCT FROM 'dangerous'
GROUP BY e.source::text
ORDER BY docs DESC
LIMIT 20

SELECT kind::text, COUNT(*)
FROM scry.entities
WHERE source = 'hackernews'
GROUP BY kind::text
ORDER BY 2 DESC
LIMIT 20

WITH c AS (
  SELECT id FROM scry.search('deceptive alignment',
    kinds=>ARRAY['post'], limit_n=>200)
)
SELECT e.uri, e.title, e.original_author,
       emb.embedding_voyage4 <=> @p_deadbeef_topic AS distance
FROM c
JOIN scry.entities e ON e.id = c.id
JOIN scry.embeddings emb ON emb.entity_id = c.id AND emb.chunk_index = 0
WHERE e.content_risk IS DISTINCT FROM 'dangerous'
ORDER BY distance
LIMIT 50

curl -s -X POST https://api.exopriors.com/v1/scry/estimate \
  -H "Authorization: Bearer $EXOPRIORS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"sql": "SELECT id, title FROM scry.mv_arxiv_papers LIMIT 1000"}'

# 1. Run query and capture results
# 2. POST share
curl -s -X POST https://api.exopriors.com/v1/scry/shares \
  -H "Authorization: Bearer $EXOPRIORS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "kind": "query",
    "title": "Top RLHF posts on LessWrong",
    "summary": "20 highest-scored LW posts mentioning RLHF.",
    "payload": {
      "sql": "...",
      "result": {"columns": [...], "rows": [...]}
    }
  }'

curl -s -X POST https://api.exopriors.com/v1/scry/judgements \
  -H "Authorization: Bearer $EXOPRIORS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "emitter": "my-agent",
    "judgement_kind": "topic_classification",
    "target_external_ref": "arxiv:2401.12345",
    "summary": "Paper primarily about mechanistic interpretability.",
    "payload": {"primary_topic": "mech_interp", "confidence_detail": "title+abstract match"},
    "confidence": 0.88,
    "tags": ["arxiv", "mech_interp"],
    "privacy_level": "public"
  }'

-- Per-source author grouping
SELECT a.handle, a.display_name, a.source::text, COUNT(*) AS docs
FROM scry.entities e
JOIN scry.actors a ON a.id = e.author_actor_id
WHERE e.source = 'twitter'
GROUP BY a.handle, a.display_name, a.source::text
ORDER BY docs DESC
LIMIT 50

-- Find all replies to a root post
SELECT id, uri, title, original_author, original_timestamp
FROM scry.entities
WHERE anchor_entity_id = 'ROOT_ENTITY_UUID'
ORDER BY original_timestamp
LIMIT 100

SELECT reltuples::bigint AS estimated_rows
FROM pg_class
WHERE relname = 'mv_lesswrong_posts'
LIMIT 1

## Scry Result

**Query**: <natural language description>
**SQL**: ```sql <the SQL that ran> ```
**Rows returned**: <N> (truncated: <yes/no>)
**Duration**: <N>ms

<formatted results table or summary>

**Share**: <share URL if created>
**Caveats**: <any data quality notes, e.g., "score is NULL for arXiv">