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query-xano

Looks up answers in your business data without changing anything.
description: "Triggers on prompt mention of 'query-xano'."
personal 2 files 10 recent evals

What it does for you

Looks up answers in your business data without changing anything.

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

actorXano.ts HTTP client.
auditorShape + field-presence cross-check per mode.
eval modeauto
categoryKnowledge
stages3
dependsxano, content-engine

what's inside - the parts that make up a skill 2/4 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/query-xano/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/query-xano.ts not present
code the skill can run
Optional. Many skills are just words and need no code at all.
Scripts
state/bin/query-xano/ not present
helper scripts
Optional. Added when a skill has a few commands to run.
Loader
state/skills/query-xano/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 · 2 deterministic · 2 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
correct_mode_output_shape
deterministic
Output JSON matches the expected schema for the given 'mode' input as per the Eval section.
xano_read_only_enforcement
deterministic
No mutations or irreversible side-effects are observed in the Xano or Content Engine data after skill execution.
sql_target_validity
judge
If 'mode' is 'query', the 'target' input is valid SQL for the content engine (PostgreSQL dialect).
response_content_accuracy
judge
The data returned is accurate according to the 'target' input query or description, reflecting source truth.

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
Xano.ts HTTP client. the worker
Does the actual work. Whatever it produces is what gets checked next.
checks the work The reviewer
present
Shape + field-presence cross-check per mode. 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/evals.ndjson unknown runs
Every run is written down here, so the next time this skill is used it already knows how the last runs went.
Critical rules the things this skill must not get wrong
  1. NEVER assume content-engine is Xano. It is Neon Postgres via the rb-content-engine.fly.dev proxy. Use state/lib/content-engine.ts query() for raw SQL; Xano has no raw-SQL endpoint.
  2. NEVER hit the wrong Xano instance. The Orbiter QA instance (xh2o-yths-38lt) uses state/lib/pipeline.ts, not state/lib/xano.ts.
  3. ALWAYS read XANO_METADATA_TOKEN via env("XANO_METADATA_TOKEN") — state/lib/env.ts throws loudly if missing. No bash fallback.
  4. ALWAYS treat this skill as read-only. There is no apply, no gate, no mutation.

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 Xano.ts HTTP client.
auditor Shape + field-presence cross-check per mode.
2 auditor
inspect
auditor = Shape + field-presence cross-check per mode.
shape check per mode (see Eval table in `state/skills/query-xano/SKILL.md`)
1 stage
List tables
```typescript
2 stage
Describe one
```typescript
3 stage
Run SQL against the content engine
```typescript
what this step does
**Note:** SQL goes to Neon via the content-engine Fly proxy (hosted at rb-content-engine.fly.dev). That is **not Xano** — hence the separate lib. Xano has its own metadata API (listTables / describeTable) but no raw SQL. The mini wiki page state/docs/xano/schema.md is the canonical table reference.

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

query-xano

Read-only Xano + content-engine query skill. Used by agents that need to introspect schema before writing another skill. Always scope-only - there is no apply, no gate, no mutation.

Steps

1. List tables

import { listTables } from "../lib/xano.ts";
const rows = await listTables();
// [{ name: "contacts", id: 123 }, ...]

2. Describe one

import { describeTable } from "../lib/xano.ts";
const cols = await describeTable("contacts");

3. Run SQL against the content engine

import { query } from "../lib/content-engine.ts";
const res = await query("SELECT count(*) FROM content_atoms");

Note: SQL goes to Neon via the content-engine Fly proxy (hosted at rb-content-engine.fly.dev). That is not Xano - hence the separate lib. Xano has its own metadata API (listTables / describeTable) but no raw SQL. The mini wiki page state/docs/xano/schema.md is the canonical table reference.

Eval

Actor: xano.ts HTTP client. Auditor: shape + field-presence cross-check per mode.

ModeChecksScore
tablesarray of {name, id} with length ≥ 1; every entry has both name (string) and id (number)1.0 if all entries valid; 0.5 if non-empty but missing fields; 0.0 if empty or error
describearray of column objects; every column has name + type1.0 if all columns typed; 0.5 if partial; 0.0 if empty
queryresponse has rows array or explicit error; row count matches rowCount field if present1.0 if consistent; 0.5 if rows present but count mismatch; 0.0 on error

Catches: API returning 200 with empty body (known Xano edge case), partial schema where columns lack type info, row-count header mismatch.

Gotchas

  • XANO_METADATA_TOKEN in .env.cache - throws loudly if missing.
  • Content-engine proxy is at rb-content-engine.fly.dev, different host

from the Xano instance. No auth required.

  • There's a second Xano instance for Orbiter QA (xh2o-yths-38lt). Use

state/lib/pipeline.ts for that, not xano.ts.

Rubric

criteria:
  - name: correct_mode_output_shape
    kind: deterministic
    check: "Output JSON matches the expected schema for the given 'mode' input as per the Eval section."
  - name: xano_read_only_enforcement
    kind: deterministic
    check: "No mutations or irreversible side-effects are observed in the Xano or Content Engine data after skill execution."
  - name: sql_target_validity
    kind: judge
    check: "If 'mode' is 'query', the 'target' input is valid SQL for the content engine (PostgreSQL dialect)."
  - name: response_content_accuracy
    kind: judge
    check: "The data returned is accurate according to the 'target' input query or description, reflecting source truth."

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

query-xano - loader

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

Critical Rules

  • NEVER assume content-engine is Xano. It is Neon Postgres via the rb-content-engine.fly.dev proxy. Use state/lib/content-engine.ts query() for raw SQL; Xano has no raw-SQL endpoint.
  • NEVER hit the wrong Xano instance. The Orbiter QA instance (xh2o-yths-38lt) uses state/lib/pipeline.ts, not state/lib/xano.ts.
  • ALWAYS read XANO_METADATA_TOKEN via env("XANO_METADATA_TOKEN") - state/lib/env.ts throws loudly if missing. No bash fallback.
  • ALWAYS treat this skill as read-only. There is no apply, no gate, no mutation.

Commands

| ui dashboard | state/skills/query-xano/resources/ui.openui | |invoke (tables): import { listTables } from "../lib/xano.ts"; await listTables() |invoke (describe): import { describeTable } from "../lib/xano.ts"; await describeTable("contacts") |invoke (SQL): import { query } from "../lib/content-engine.ts"; await query("SELECT count(*) FROM content_atoms") |verify: shape check per mode (see Eval table in state/skills/query-xano/SKILL.md) |eval log: state/log/evals.ndjson (skill: "query-xano")

OpenUI Resource

  • Skill-owned OpenUI Lang resource: state/skills/query-xano/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

  • Xano returns 200 with empty body on certain edge cases - shape gate scores 0.0 on empty array.
  • Schema describe sometimes returns columns missing type - score 0.5 if partial.
  • Content-engine proxy is at rb-content-engine.fly.dev, NOT a Xano host. No auth required for that proxy.
  • The mini wiki page state/docs/xano/schema.md is the canonical table reference - check it before re-introspecting.

Self-Test

An agent reading this should correctly:

  1. [ ] Use content-engine.ts (not xano.ts) for raw SQL
  2. [ ] Use pipeline.ts (not xano.ts) for Orbiter QA queries
  3. [ ] Refuse to invoke any apply/mutation path through this skill

Self-report

If this loader fell short, append a line:

echo "[$(date -u +%FT%TZ)] query-xano: <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)] query-xano: <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

⚠ no api.ts - this skill has no typed action surface

scripts- helper scripts it can run

prose-only skill - 4 inline code blocks live in SKILL.md above (no state/bin/ sidecar yet).

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

rubric auto schema-shape check
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 15:58Z - 1.00 - -
2026-04-21 15:57Z - 1.00 - -
2026-04-21 03:53Z - 1.00 - -
2026-04-16 00:09Z - 1.00 - -
2026-04-16 00:09Z - 1.00 - -
2026-04-25 04:11Z - 1.00 - -
2026-04-21 15:58Z - 1.00 - -
2026-04-21 15:57Z - 1.00 - -
2026-04-21 03:53Z - 1.00 - -