Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add DMDuFresne/questllens --skill questllens-explore-a-databasegit clone --depth 1 https://github.com/DMDuFresne/questllensWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dmdufresne/questllens/questllens-explore-a-database)<a href="https://agentmods.dev/skills/dmdufresne/questllens/questllens-explore-a-database"><img src="https://agentmods.dev/badge/skills/dmdufresne/questllens/questllens-explore-a-database/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dmdufresne/questllens/questllens-explore-a-database"><img src="https://agentmods.dev/badge/skills/dmdufresne/questllens/questllens-explore-a-database.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00098 | $0.00954 |
| Opus 5.5 | $0.00039 | $0.00382 |
| Sonnet 5.5 | $0.00020 | $0.00191 |
| Haiku 4.5 | $0.00010 | $0.00095 |
Grade A, and why
questllens-explore-a-database scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
questllens-explore-a-database
Orientation, in order. Each step narrows the next; skipping ahead is how you end up querying a table that has no rows in the window you care about.
1. Inventory
list_tables — every table and materialized view, with the designated timestamp,
partitioning strategy, and WAL flag. Read three things off it:
- Which tables have no designated timestamp. These are reference/dimension tables.
SAMPLE BYandLATEST ONdon't apply to them. - The partition unit (DAY/HOUR/MONTH). It tells you the granularity at which a time predicate becomes cheap.
- Materialized views vs base tables. An MV is derived; don't reason about ingestion from it.
2. Meaning before structure
If the operator supplied a domain context, it is already in the tool descriptions — read it first. It's the only source that explains why a table exists.
Then describe_table per interesting table. Two optional flags earn their cost:
with_time_range=true— min/max designated timestamp and total row count. Do this before writing any time filter. One cheap aggregate that tells you whether the table covers the period the user is asking about. Answering "there's no data" when you queried the wrong window is the most common avoidable error here.with_symbol_stats=true— distinct count per SYMBOL column against its declared capacity. Use it to learn cardinality (isdevice_id12 devices or 400,000?) and to spot capacity overruns. It's a full scan — worth it once per table, not per question.
describe_table also surfaces dedup keys (UPSERT KEYS). A table with dedup enabled
silently replaces rows matching those keys — so a duplicate-looking count isn't necessarily a
data-quality problem.
3. Shape of the data
get_sample_data— actual rows. Setlatest=trueto see the newest rows rather than the oldest, which is almost always what you want on a time-series table. Project withcolumnson wide tables; narrow withwherewhen you need a specific slice.get_table_stats— null percentage and distinct count per column. On a large table passsample_rowsto bound it to the latest N rows; without it you are scanning everything.search_columns— when the user names a column and you don't know which table holds it.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 73 lines · 98 tokens per session scan A eaaa3d9f6d7e
questllens-explore-a-database is a skill published in the GitHub repository DMDuFresne/questllens (0 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 98 tokens to every session and 954 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
questllens-explore-a-database compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| questllens-explore-a-databaseDMDuFresne/questllens | Skill | 0 | 2mo ago | Apache-2.0 | A | 98 | Claude Code, Codex |
| create-prprisma/orm | Skill | 48k | yesterday | Apache-2.0 | A | 48 | Claude Code, Codex |
| schema-explorationlangchain-ai/deepagents | Skill | 31k | yesterday | MIT | A | 57 | Claude Code, Codex |
| atlas-mssqlariga/atlas | Skill | 8.8k | 2d ago | Apache-2.0 | A | 163 | Claude Code |
| ha-data-storesshiwenwen/hope-agent | Skill | 1.9k | yesterday | MIT | A | 115 | Claude Code, Codex |
| nornicdb-cypher-queriesorneryd/NornicDB | Skill | 924 | yesterday | MIT | A | 79 | Claude Code, Codex |
| volcengine-rds-postgresqlbytedance/agentkit-samples | Skill | 484 | 2d ago | Apache-2.0 | A | 63 | Claude Code, Codex |