Agent-Skills.md

Agent Skills: Scry Skill

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UncategorizedID: plurigrid/asi/scry

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plurigrid/asi
plurigridLicense: MIT
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plugins/asi/skills/scry/SKILL.md

Skill Metadata

Name
scry
Description
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Scry Skill

Scry gives you read-only SQL access to the ExoPriors public corpus (229M+ entities) via a single HTTP endpoint. You write Postgres SQL against a curated scry.* schema and get JSON rows back. There is no ORM, no GraphQL, no pagination token -- just SQL.

Skill generation: 2026031701

A) When to use / not use

Use this skill when:

  • Searching, filtering, or aggregating content across the ExoPriors corpus
  • Running lexical (BM25) or hybrid searches
  • Exploring author networks, cross-platform identities, or publication patterns
  • Navigating the OpenAlex academic graph (authors, citations, institutions, concepts)
  • Creating shareable artifacts from query results
  • Emitting structured agent judgements about entities or external references

Do NOT use this skill when:

  • The user wants semantic/vector search composition or embedding algebra (use the scry-vectors skill)
  • The user wants LLM-based reranking (use the scry-rerank skill)
  • The user is querying their own local database

B) Golden Rules

  1. Context handshake first. At session start, call GET /v1/scry/context?skill_generation=2026031701. This endpoint is public; you do not need a key for the handshake itself. Use the returned offerings block for the current product summary budgets, canonical env var, default skill, and specialized skill catalog. If you need a concise shareable bootstrap prompt for another agent, use offerings.public_agent_prompt.copy_text instead of paraphrasing your own. If you need deeper docs, use offerings.canonical_doc_path, each skill's repo_path, and reference_paths instead of guessing where the maintained docs live. If you cache descriptive bootstrap context across turns or sessions, also track surface_context_generation and refresh when it changes. Read lexical_search.status as well: if it is not healthy, stop assuming global scry.search* is reliable and pivot to source-local scry.* / mv_* surfaces or semantic retrieval while the canonical BM25 index recovers. If should_update_skill=true, tell the user to run npx skills update. If the response reports client_skill_generation: null while you're using packaged skills, or if local instructions still mention api.exopriors.com or exopriors.com/console, treat the install as stale and ask the user to run npx skills update before more debugging.

  2. Schema first. ALWAYS call GET /v1/scry/schema before writing SQL. Never guess column names or types. The schema endpoint returns live column metadata and row-count estimates for every view.

  3. Check operational status when search looks wrong. If lexical search, materialized-view freshness, or corpus behavior seems off, call GET /v1/scry/index-view-status before assuming the query or schema is wrong.

  4. Clarify ambiguous intent before heavy queries. If the request is vague ("search Reddit for X", "find things about Y"), ask one short clarification question about the goal/output format before running expensive SQL.

  5. Start with a cheap probe. Before any query likely to run >5s, run /v1/scry/estimate and/or a tight exploratory query (LIMIT 20 plus scoped source/window filters), then scale only after confirming relevance.

  6. Choose lexical vs semantic explicitly. Use lexical (scry.search*) for exact terms and named entities. For conceptual intent ("themes", "things like", "similar to"), route to scry-vectors first, then optionally hybridize.

  7. LIMIT always. Every query MUST include a LIMIT clause. Max 10,000 rows. Queries without LIMIT are rejected by the SQL validator.

  8. Prefer canonical surfaces with tight filters. scry.entities has 229M+ rows, so do not scan it blindly. Use scry.search* for lexical retrieval, scry.chunk_embeddings for chunk-level semantic retrieval, scry.entity_embeddings or scry.entities_with_embeddings only when you want one entity-level vector row per entity, scry.embedding_coverage to inspect public vs staged vs ready source/kind coverage, and source-native tables such as scry.hackernews_items, scry.wikipedia_articles, scry.pubmed_papers, scry.repec_records, scry.openalex_works, scry.bluesky_posts, scry.mailing_list_messages, and scry.openlibrary_* when a corpus no longer lives canonically in scry.entities. Reach for a specific mv_* convenience view only when /v1/scry/schema confirms it is healthy and useful for the task.

  9. Cross-table composition is normal. If the best records live in multiple source-native tables, combine them in one SQL statement with CTEs, UNION ALL, and joins through scry.source_records. This is the intended contract, not a workaround.

  10. Filter dangerous content. Always include WHERE content_risk IS DISTINCT FROM 'dangerous' unless the user explicitly asks for unfiltered results. Dangerous content contains adversarial prompt-injection content.

  11. Raw SQL, not JSON. POST /v1/scry/query takes Content-Type: text/plain with raw SQL in the body. Not JSON-wrapped SQL.

  12. File rough edges promptly. If Scry blocks the task, misses an obvious result set, or exposes a rough edge, submit a brief note to POST /v1/feedback?feedback_type=suggestion|bug|other&channel=scry_skill using Content-Type: text/plain by default (text/markdown also works). Do not silently work around it. Logged-in users can review their submissions with GET /v1/feedback.

For full tier limits, timeout policies, and degradation strategies, see Shared Guardrails.

B.1 API Key Setup (Canonical)

Recommended default for less-technical users: in the directory where you launch the agent, store SCRY_API_KEY in .env so skills and copied prompts use the same place. Canonical key naming for this skill:

  • Env var: SCRY_API_KEY
  • Anonymous bootstrap key format: scry_anon_* from POST /v1/scry/anonymous-key
  • Personal key format: personal Scry API key with Scry access
  • Recommended anonymous client header: X-Scry-Client-Tag: <short-stable-tag>
printf '%s\n' 'SCRY_API_KEY=<your key>' >> .env
set -a && source .env && set +a

Verify:

echo "$SCRY_API_KEY"

Anonymous bootstrap flow when the user wants immediate public access without signup:

CLIENT_TAG="${SCRY_CLIENT_TAG:-dev-laptop}"
ANON_KEY="$(curl -s https://api.scry.io/v1/scry/anonymous-key -X POST -H "X-Scry-Client-Tag: $CLIENT_TAG" | python3 -c 'import json,sys; print(json.load(sys.stdin)[\"api_key\"])')"
curl -s https://api.scry.io/v1/scry/schema \
  -H "Authorization: Bearer $ANON_KEY" \
  -H "X-Scry-Client-Tag: $CLIENT_TAG"
curl -s https://api.scry.io/v1/scry/query \
  -H "Authorization: Bearer $ANON_KEY" \
  -H "X-Scry-Client-Tag: $CLIENT_TAG" \
  -H "Content-Type: text/plain" \
  --data "SELECT 1 LIMIT 1"

Use this for fast trial access only. The anonymous bootstrap lane is intentionally generous for the first few queries and then degrades. For sustained usage, prefer a personal Scry API key. Keep the same X-Scry-Client-Tag value on the same device when staying anonymous so the backend can distinguish a real first-use session from abuse behind shared IPs.

If using packaged skills, keep them current:

npx skills add exopriors/skills
npx skills update

B.1b x402 Query-Only Access

POST /v1/scry/query still supports standard x402, but it is now an explicit paid path rather than the default no-auth bootstrap path. Use x402 when the user already has an x402-capable wallet/client and only needs direct paid query execution. For public trial use, use POST /v1/scry/anonymous-key. For schema/context, shares, judgements, feedback, or repeated multi-endpoint usage, prefer a personal Scry API key.

If the user wants wallet-native durable identity plus a reusable key, use POST /v1/auth/agent/signup first. That binds the wallet to a user and returns a session token plus API key in one flow.

Minimal client shape:

import { wrapFetchWithPayment } from 'x402-fetch';

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

C) Quickstart

One end-to-end example: find recent high-scoring LessWrong posts about RLHF.

Step 1: Get dynamic context + update advisory
GET https://api.scry.io/v1/scry/context?skill_generation=2026031701
Authorization: Bearer $SCRY_API_KEY

Step 2: Get schema
GET https://api.scry.io/v1/scry/schema
Authorization: Bearer $SCRY_API_KEY

Step 3: Run query
POST https://api.scry.io/v1/scry/query
Authorization: Bearer $SCRY_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'
ORDER BY e.score DESC NULLS LAST, e.original_timestamp DESC
LIMIT 20

Response shape:

{
  "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
}

Source-native cross-table example:

WITH hn AS (
  SELECT 'hackernews'::text AS source, hn_id::text AS external_id, score
  FROM scry.search_hackernews_items('interpretability', kinds => ARRAY['post'], limit_n => 20)
),
wiki AS (
  SELECT 'wikipedia'::text AS source, page_id::text AS external_id, score
  FROM scry.search_wikipedia_articles('interpretability', limit_n => 20)
),
hits AS (
  SELECT * FROM hn
  UNION ALL
  SELECT * FROM wiki
)
SELECT h.source, r.uri, r.title, h.score
FROM hits h
JOIN scry.source_records r
  ON r.source = h.source
 AND r.external_id = h.external_id
ORDER BY h.score DESC
LIMIT 20;

D) Decision Tree

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?  --> join/filter `source` explicitly (or use a healthy source-local view if schema confirms it)
  |     +-- 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() (up to 2000 lexical IDs; join for fields)
  |
  +-- 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.chunk_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

E) Recipes

E0. Context handshake + skill update advisory

curl -s "https://api.scry.io/v1/scry/context?skill_generation=2026031701" \
  -H "Authorization: Bearer $SCRY_API_KEY"

If response includes "should_update_skill": true, ask the user to run: npx skills update. If the response shows "client_skill_generation": null while the session is using packaged Scry skills, or if local instructions still point at api.exopriors.com / exopriors.com/console, stop and ask the user to run npx skills update before deeper debugging. If response includes "lexical_search": {"status": "rebuilding"|"degraded"|"stale"|...}, prefer source-local scry.* surfaces or scry.entities_with_embeddings and use /v1/scry/index-view-status for detailed rebuild timing before blaming the query.

E0b. Submit feedback when Scry blocks the task

curl -s "https://api.scry.io/v1/feedback?feedback_type=bug&channel=scry_skill" \
  -H "Authorization: Bearer $SCRY_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- ...'

Success response includes a receipt id. Logged-in users can review their own submissions with:

curl -s "https://api.scry.io/v1/feedback?limit=10" \
  -H "Authorization: Bearer $SCRY_API_KEY"

E1. Lexical search (BM25)

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

Default kinds if omitted: ['post','paper','document','webpage','twitter_thread','grant']. scry.search() broadens once to kinds=>ARRAY['comment'] if that default returns 0 rows. Pass explicit kinds for strict scope (for example comment-only or tweet-only). For source scoping, join back to scry.entities and filter source explicitly. Healthy source-specific MVs can still be useful for source-native score fields such as base_score, but they are optional convenience slices rather than the default path.

E2. Reddit-specific discovery

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

For semantic Reddit retrieval over the embedding-covered subset, use scry.reddit_embeddings or scry.search_reddit_posts_semantic(...).

Direct retrieval helpers (scry.reddit_posts, scry.reddit_comments, scry.mv_reddit_*, scry.search_reddit_posts(...), scry.search_reddit_comments(...)) are currently degraded on the public instance. Check /v1/scry/schema status before using them.

E3. Source-filtered materialized view query

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

score is NULL for arXiv papers on the public surface. Sort by original_timestamp, category, or downstream citation proxies instead.

E4. Author activity across sources

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

E5. Recent entity kind distribution for a source

SELECT kind::text, COUNT(*)
FROM scry.hackernews_items
WHERE original_timestamp >= '2025-01-01'
GROUP BY kind::text
ORDER BY 2 DESC
LIMIT 20

Source-native corpora follow the same pattern:

SELECT kind::text, COUNT(*)
FROM scry.wikipedia_articles
WHERE original_timestamp >= '2025-01-01'
GROUP BY kind::text
ORDER BY 2 DESC
LIMIT 20

Removing the date bound turns this into a large base-table aggregation. Run /v1/scry/estimate first or prefer source-specific MVs when they already cover the question.

E6. Hybrid search (lexical + semantic rerank in SQL)

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.chunk_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

Requires a stored embedding handle (@p_deadbeef_topic). See scry-vectors skill for creating handles.

E7. Cost estimation before execution

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

Returns EXPLAIN (FORMAT JSON) output. Use this for expensive queries before committing. It does not prove BM25 helper health: if scry.search* fails, check /v1/scry/index-view-status and /v1/scry/schema status as well. The /v1/scry/context handshake now also exposes lexical_search.status for cheap degraded-mode detection before you start issuing lexical helpers.

E8. Create a shareable artifact

# 1. Run query and capture results
# 2. POST share
curl -s -X POST https://api.scry.io/v1/scry/shares \
  -H "Authorization: Bearer $SCRY_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": [...]}
    }
  }'

Kinds: query, rerank, insight, chat, markdown. Progressive update: create stub immediately, then PATCH /v1/scry/shares/{slug}. Rendered at: https://scry.io/scry/share/{slug}.

E9. Emit a structured agent judgement

curl -s -X POST https://api.scry.io/v1/scry/judgements \
  -H "Authorization: Bearer $SCRY_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"
  }'

Exactly one target required: target_entity_id, target_actor_id, target_judgement_id, or target_external_ref. Judgement-on-judgement: use target_judgement_id to chain observations.

E10. People / author lookup

-- 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

E11. Thread navigation (replies)

-- 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

anchor_entity_id is the root subject; parent_entity_id is the direct parent.

E12. Count estimation (safe pattern)

Avoid COUNT(*) on large tables. Instead, use schema endpoint row estimates or:

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

Note: pg_class access is blocked on the public Scry SQL surface. Use /v1/scry/schema instead.

F) Error Handling

See references/error-reference.md for the full catalogue. Key patterns:

| HTTP | Code | Meaning | Action | |------|------|---------|--------| | 400 | invalid_request | SQL parse error, missing LIMIT, bad params | Fix query | | 401 | unauthorized | Missing or invalid API key | Check key | | 402 | insufficient_credits | Token budget exhausted | Notify user | | 429 | rate_limited | Too many requests | Respect Retry-After header | | 503 | service_unavailable | Scry pool down or overloaded | Wait and retry |

Auth + timeout diagnostics for CLI users:

  1. If curl shows HTTP 000, that is client-side timeout/network abort, not a server HTTP status. Check --max-time and retry with /v1/scry/estimate first.
  2. If you see 401 with "Invalid authorization format", check for whitespace/newlines in the key: KEY_CLEAN="$(printf '%s' \"$SCRY_API_KEY\" | tr -d '\\r\\n')" then use Authorization: Bearer $KEY_CLEAN.

Quota fallback strategy:

  1. If 429: wait Retry-After seconds, retry once.
  2. If 402: tell the user their token budget is exhausted.
  3. If 503: retry after 30s with exponential backoff (max 3 attempts).
  4. If query times out: simplify (use MV instead of full table, reduce LIMIT, add tighter WHERE filters).

G) Output Contract

When this skill completes a query task, return a consistent structure:

## 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">

Handoff Contract

Produces: JSON with columns, rows, row_count, duration_ms, truncated Feeds into:

  • rerank: ensure SQL returns id and content_text columns for candidate sets
  • scry-vectors: save entity IDs for embedding lookup and semantic reranking Receives from: none (entry point for SQL-based corpus access)

Related Skills

  • scry-vectors -- embed concepts as @handles, search by cosine distance, debias with vector algebra
  • scry-rerank -- LLM-powered multi-attribute reranking of candidate sets via pairwise comparison

For detailed schema documentation, see references/schema-guide.md. For the full pattern library, see references/query-patterns.md. For error codes and quota details, see references/error-reference.md.