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drop another .md file to compare - side-by-side diff against database

database

Searches and pulls answers from your business data.
description: "Triggers on prompt mention of 'database'."
personal 2 files 10 recent evals

What it does for you

Searches and pulls answers from your business data.

What it produces

A recent result, so you can see the kind of work it returns.

loading…

How to get it

These run inside the Snappy workspace. Want this working in your business? I set skills like this up with you, in one focused week.

Work with me
For developers how this skill is built, graded, and how it runs

at a glance- the short version

actorExported functions in state/lib/database.ts.
auditorNone wired yet - eval is manual (Robert review).
eval modeshape
categoryIntegrations
stages3
dependsxano, content-engine

what's inside - the parts that make up a skill 3/4 present

A skill is just a few plain-text files. Only the main one is required. The rest are optional, added as the work needs them. This is what the skill is made of; how it runs is just below.

The skill
state/skills/database/SKILL.md present
the skill itself, in plain text
The main file. It says what the skill is and lays out the steps in plain English.
Code
state/lib/database.ts present
code the skill can run
Reusable code this skill can call when it needs to.
Scripts
state/bin/database/ not present
helper scripts
Optional. Added when a skill has a few commands to run.
Loader
state/skills/database/AGENTS.md present
what the AI loads on the fly
Loaded automatically the moment this skill is needed. Kept short on purpose.

how it's graded - what counts as a good run 4 criteria · 1 deterministic · 3 judge

Each row is one thing a good run has to get right. deterministic means a quick check decides, pass or fail. judge means the AI reads the result and rates it. Grading each piece on its own (instead of one overall score) shows exactly where a run fell short, so the fix is obvious.

name
kind
check
list_tables_correctness
judge
The output of listTables() accurately reflects the tables present in the connected Xano database, as if manually verified against the Xano dashboard or direct database inspection.
describe_table_accuracy
judge
The output of describeTable() for a given table name correctly details its schema, including column names, data types, and primary key information, matching Xano's definition.
query_result_validity
judge
Executed queries via query() method return results that are consistent with direct execution against the Xano database or content engine, confirming data integrity and query correctness.
no_xano_api_errors
deterministic
The execution log (e.g., `state/log/pending-eval.ndjson` or application logs) must not contain any Xano API error codes or connection failures during database operations.

how it runs - the shared frame every skill uses 5/5 present

Every skill runs the same way. One part does the work, a separate part checks it, and a short loader hands the AI exactly what it needs for the job. Anything this skill doesn't use shows a one-line note saying why, on purpose, not by accident.

makes the work The worker
present
Exported functions in state/lib/database.ts. the worker
Does the actual work. Whatever it produces is what gets checked next.
checks the work The reviewer
present
None wired yet - eval is manual (Robert review). the checker
A separate checker grades the work, so the part that made it can't approve its own work.
frame
learns Self-correction
present
fixes itself learns from gaps
When a run hits a gap, the skill gets edited on the spot [FIXED] or queued for a bigger rewrite [LOGGED], so it keeps getting better.
tidies up Background fixes
present
queued for rewrite runs in the background
Bigger fixes that can't be made on the spot get queued and rewritten in the background later.
remembers Run history
present
state/log/pending-eval.ndjson pending runs
Every run is written down here, then reviewed by hand each week.
Critical rules the things this skill must not get wrong
  1. content-engine is NOT Xano. It's raw SQL against rb-content-engine.fly.dev (Neon Postgres proxy). Don't confuse the two. The lib was split 2026-04-16 (state/index.md).
  2. Xano writes go through Metadata API: createTable, addField, deleteField, createApiGroup, createApi, updateApi. Xanoscript push is PUT /api/{id} (no editor).

what it has learned - fixes written back in over time sample

When a run hits something this skill didn't handle, the fix gets written back into the skill so it doesn't happen again. FIXED means it was corrected on the spot. LOGGED means it's queued for a bigger rewrite. Either way, the skill gets a little better and never makes the same mistake twice.

  1. Loading feedback rows…

how the work flows- who makes it, who checks it

inputs xanocontent-engine
actor Exported functions in state/lib/database.ts.
1 generator
invoke
actor = Exported functions in state/lib/database.ts.
import from `state/lib/database.ts` — `listTables()`, `describeTable()`, `query()
auditor None wired yet - eval is manual (Robert review).
2 auditor
inspect
auditor = None wired yet - eval is manual (Robert review).
listTables()` against the target workspace before describing or querying
3 data
eval log
`state/log/pending-eval.ndjson` (manual eval — skill: "database")

SKILL.md- the skill, written out in plain English

database

Xano database catalog and content engine queries.

Ported from kernel snappy-database in Phase 0.5. See state/lib/database.ts for the full API surface.

Steps

  • listTables() - see state/lib/database.ts
  • describeTable() - see state/lib/database.ts
  • query() - see state/lib/database.ts

Eval

Actor: the exported functions in state/lib/database.ts. Auditor: none wired yet - eval is manual (Robert review). File a state/log/pending-eval.ndjson row on each run.

Score convention:

OutcomeScore
Pass on first try1.0
Failed first, auto-fix applied, re-check passed0.5
Still failing or unrecoverable0.0

Gotchas

via the Phase 0.5 driver. Only these rewrites were applied: already in state/lib/)

  1. realpathSync(process.argv[1]) CLI guard wrapped in try/catch
  • See the kernel SKILL.md for the original long-form guidance if you need it

(read-only reference at the kernel path above).

Graduation

This skill is prose. Graduate by defining a deterministic auditor and flipping eval: auto.

Rubric

criteria:
  - name: list_tables_correctness
    kind: judge
    check: "The output of listTables() accurately reflects the tables present in the connected Xano database, as if manually verified against the Xano dashboard or direct database inspection."
  - name: describe_table_accuracy
    kind: judge
    check: "The output of describeTable() for a given table name correctly details its schema, including column names, data types, and primary key information, matching Xano's definition."
  - name: query_result_validity
    kind: judge
    check: "Executed queries via query() method return results that are consistent with direct execution against the Xano database or content engine, confirming data integrity and query correctness."
  - name: no_xano_api_errors
    kind: deterministic
    check: "The execution log (e.g., `state/log/pending-eval.ndjson` or application logs) must not contain any Xano API error codes or connection failures during database operations."

AGENTS.md- what the AI loads when this skill comes up

database - loader

Per-turn rules for the database skill. Full reference: state/skills/database/SKILL.md. Do not skip these.

Critical Rules

  • content-engine is NOT Xano. It's raw SQL against rb-content-engine.fly.dev (Neon Postgres proxy). Don't confuse the two. The lib was split 2026-04-16 (state/index.md).
  • Xano writes go through Metadata API: createTable, addField, deleteField, createApiGroup, createApi, updateApi. Xanoscript push is PUT /api/{id} (no editor).

Commands

| ui dashboard | state/skills/database/resources/ui.openui | |invoke: import from state/lib/database.ts - listTables(), describeTable(), query() |verify: listTables() against the target workspace before describing or querying |eval log: state/log/pending-eval.ndjson (manual eval - skill: "database")

OpenUI Resource

  • Skill-owned OpenUI Lang resource: state/skills/database/resources/ui.openui. Read it before rendering or editing this skill's generated component surface.
  • Treat this resource as a first-class artifact of the skill, not a generic chat response. Improve it when the skill's user-facing output needs to become richer.
  • System resources compose OpenUI primitives and inherit SnappyChat tokens. Use ui_contract: branded in SKILL.md only for deliberate platform or client visuals.

Known Pitfalls

  • For raw SQL, use state/lib/content-engine.ts (split out 2026-04-16) - NOT database.ts and NOT xano.ts
  • For Xano REST CRUD on records, use state/lib/xano.ts (read+write surface listed in state/index.md)
  • Wiki page state/docs/xano/schema.md is the canonical table reference, copied from kernel snappy-database/tables.md

Self-Test

An agent reading this should correctly:

  1. [ ] Route a "run this SQL" request to content-engine.ts, not database.ts
  2. [ ] Use Metadata API for schema changes, REST for record CRUD
  3. [ ] Read state/docs/xano/schema.md for table shapes

Self-report

If this loader fell short, append a line:

echo "[$(date -u +%FT%TZ)] database: <what was missing>" >> state/log/loader-feedback.log

<!-- kernel-ok: historical attribution for the PID-loader pattern. -->

Self-correcting loader (PID feedback)

This is the rule that makes loaders learn. Ported from snappy-kernel, where it demonstrably worked: the snappy-dom-cartographer skill mapped Skool admin UI once, every later ship-agent inherited the full map for free.

Before you finish, do two things:

1. Fix gaps (P - proportional). MANDATORY. If this loader didn't cover your case - if you had to read another file, run an undocumented command, work around a wrong selector, or discover a quirk - you MUST attempt an Edit to this AGENTS.md before you log.

  • Default action: edit this file. Surgical. Add the missing line, table

row, or code example where it belongs. No restructuring.

  • Only LOGGED is allowed when: the fix needs >10 lines, spans multiple

files, or requires a structural rewrite. In that case the state/regen/drain.sh queue picks it up asynchronously.

  • "I didn't have time" / "it's minor" / "the next agent will figure it out"

are NOT valid reasons. Minor gaps compound - that's the whole point of this loop.

  • Concrete example of a valid P-fix: discover agent-browser screenshot

takes path positionally not as --output <path>. One line in a table. Edit it in. Do not just log it.

  • The goal: the next agent never has to leave the loader.

2. Log the result.

echo "[$(date -u +%FT%TZ)] database: <what was missing or fixed> [FIXED|LOGGED]" >> state/log/loader-feedback.log
  • FIXED = you patched this loader inline (P-fix).
  • LOGGED = too large for inline; the PostToolUse enqueue + Stop-hook drain

will rewrite the loader from scratch on next session-end.

Do not skip this. Every agent run must leave the system better than it found it. The loader is the setpoint; you are the sensor; the gap is the error signal; closing the gap is the correction.

api.ts- the code it can call

#!/usr/bin/env npx tsx
/**
 * snappy-database/api.ts -- Xano database catalog and content engine queries.
 *
 * Xano metadata API for table listing/schema, Neon Postgres for content_atoms SQL.
 *
 * Usage:
 *   npx tsx api.ts tables
 *   npx tsx api.ts describe contacts
 *   npx tsx api.ts query "SELECT count(*) FROM content_atoms"
 *
 * Or import as module:
 *   import { listTables, describeTable, query } from "./database.ts";
 */

import { env } from "./env.ts";
import { realpathSync } from "fs";

function xanoBase(): string {
  return env("XANO", false) || "https://xnwv-v1z6-dvnr.n7c.xano.io";
}

function xanoToken(): string {
  return env("XANO_METADATA_TOKEN");
}

async function xanoMeta(path: string): Promise<any> {
  const res = await fetch(`${xanoBase()}${path}`, {
    headers: { Authorization: `Bearer ${xanoToken()}` },
  });
  if (!res.ok) {
    throw new Error(`Xano metadata ${path}: ${res.status} ${res.statusText}`);
  }
  return res.json();
}

/** List all tables in the Xano workspace. */
export async function listTables(): Promise<{ name: string; id: number }[]> {
  const data = await xanoMeta("/api:meta/table");
  return (data || []).map((t: any) => ({ name: t.name, id: t.id }));
}

/** Get schema (columns) for a specific table. */
export async function describeTable(tableName: string): Promise<any> {
  const tables = await xanoMeta("/api:meta/table");
  const table = (tables || []).find((t: any) => t.name === tableName);
  if (!table) throw new Error(`Table not found: ${tableName}`);
  return xanoMeta(`/api:meta/table/${table.id}/column`);
}

/** Run raw SQL against the content engine Neon Postgres. */
export async function query(sql: string): Promise<any> {
  const baseUrl = "https://rb-content-engine.fly.dev";
  const res = await fetch(`${baseUrl}/api/query`, {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({ sql }),
  });
  if (!res.ok) {
    throw new Error(`Content engine query failed: ${res.status} ${res.statusText}`);
  }
  return res.json();
}

// --- CLI ---

if ((() => { try { return import.meta.url === `file://${realpathSync(process.argv[1])}`; } catch { return false; } })()) {
  (async () => {
    const [, , cmd, ...args] = process.argv;

    switch (cmd) {
      case "tables": {
        const tables = await listTables();
        for (const t of tables) {
          console.log(`${t.id}\t${t.name}`);
        }
        break;
      }
      case "describe": {
        const [tableName] = args;
        if (!tableName) { console.error("Usage: api.ts describe <table>"); process.exit(1); }
        const cols = await describeTable(tableName);
        console.log(JSON.stringify(cols, null, 2));
        break;
      }
      case "query": {
        const sql = args.join(" ");
        if (!sql) { console.error("Usage: api.ts query <sql>"); process.exit(1); }
        const result = await query(sql);
        console.log(JSON.stringify(result, null, 2));
        break;
      }
      default:
        console.log("Usage: npx tsx api.ts [tables|describe|query] ...");
    }
  })();
}

scripts- helper scripts it can run

prose-only skill - 1 inline code block live in SKILL.md above (no state/bin/ sidecar yet).

how we check it- the checks, plus the last 10 runs

rubric shape schema-shape check (no inline rubric)
recent mean 1.00 · 10 runs actor/auditor: unverifiable
deps xano content-engine
timestamp verb score primary_issue artifact
2026-04-25 04:11Z - 1.00 - -
2026-04-21 15:59Z - 1.00 - -
2026-04-21 15:56Z - 1.00 - -
2026-04-21 03:53Z - 1.00 - -
2026-04-25 04:11Z - 1.00 - -
2026-04-21 15:59Z - 1.00 - -
2026-04-21 15:56Z - 1.00 - -
2026-04-21 03:53Z - 1.00 - -
2026-04-25 04:11Z - 1.00 - -
2026-04-21 15:59Z - 1.00 - -