Exported functions in state/lib/database.ts. .md file to compare - side-by-side diff against database
database
description: "Triggers on prompt mention of 'database'."
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.
For developers how this skill is built, graded, and how it runs
at a glance- the short version
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.
state/skills/database/SKILL.md
present
state/lib/database.ts
present
state/bin/database/
not present
state/skills/database/AGENTS.md
present
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.
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.
state/log/pending-eval.ndjson - 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).
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.
- Loading feedback rows…
how the work flows- who makes it, who checks it
import from `state/lib/database.ts` — `listTables()`, `describeTable()`, `query()
listTables()` against the target workspace before describing or querying
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()- seestate/lib/database.tsdescribeTable()- seestate/lib/database.tsquery()- seestate/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:
| Outcome | Score |
|---|---|
| Pass on first try | 1.0 |
| Failed first, auto-fix applied, re-check passed | 0.5 |
| Still failing or unrecoverable | 0.0 |
Gotchas
via the Phase 0.5 driver. Only these rewrites were applied: already in state/lib/)
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-engineis NOT Xano. It's raw SQL againstrb-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 isPUT /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: brandedin 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) - NOTdatabase.tsand NOTxano.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.mdis the canonical table reference, copied from kernelsnappy-database/tables.md
Self-Test
An agent reading this should correctly:
- [ ] Route a "run this SQL" request to
content-engine.ts, notdatabase.ts - [ ] Use Metadata API for schema changes, REST for record CRUD
- [ ] Read
state/docs/xano/schema.mdfor table shapes
Self-report
If this loader fell short, append a line:
echo "[$(date -u +%FT%TZ)] database: <what was missing>" >> state/log/loader-feedback.log
<!-- kernel-ok: historical attribution for the PID-loader pattern. -->
Self-correcting loader (PID feedback)
This is the rule that makes loaders learn. Ported from snappy-kernel, where it demonstrably worked: the snappy-dom-cartographer skill mapped Skool admin UI once, every later ship-agent inherited the full map for free.
Before you finish, do two things:
1. Fix gaps (P - proportional). MANDATORY. If this loader didn't cover your case - if you had to read another file, run an undocumented command, work around a wrong selector, or discover a quirk - you MUST attempt an Edit to this AGENTS.md before you log.
- Default action: edit this file. Surgical. Add the missing line, table
row, or code example where it belongs. No restructuring.
- Only
LOGGEDis allowed when: the fix needs >10 lines, spans multiple
files, or requires a structural rewrite. In that case the state/regen/drain.sh queue picks it up asynchronously.
- "I didn't have time" / "it's minor" / "the next agent will figure it out"
are NOT valid reasons. Minor gaps compound - that's the whole point of this loop.
- Concrete example of a valid P-fix: discover
agent-browser screenshot
takes path positionally not as --output <path>. One line in a table. Edit it in. Do not just log it.
- The goal: the next agent never has to leave the loader.
2. Log the result.
echo "[$(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
| 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 | - | - |