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 OKHP3/skillz --skill sql-server-table-reconciliationgit clone --depth 1 https://github.com/OKHP3/skillzWrote 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/okhp3/skillz/sql-server-table-reconciliation)<a href="https://agentmods.dev/skills/okhp3/skillz/sql-server-table-reconciliation"><img src="https://agentmods.dev/badge/skills/okhp3/skillz/sql-server-table-reconciliation/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/okhp3/skillz/sql-server-table-reconciliation"><img src="https://agentmods.dev/badge/skills/okhp3/skillz/sql-server-table-reconciliation.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 | $0.00056 | $0.01404 |
| Opus 5 | $0.00028 | $0.00702 |
| Sonnet 5 | $0.00011 | $0.00281 |
| Haiku 4.5 | $0.00006 | $0.00140 |
Grade A, and why
sql-server-table-reconciliation 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 7d 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.
This is a copy
91% identical to sql-server-table-reconciliation — 13 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQL Server Table Reconciliation
Compare identical tables across two SQL Server instances using Python with mssql-python driver and Apache Arrow. Detect missing rows, column mismatches, schema drift, and produce a reconciliation report.
Workflow
- Collect connection details for source and target
- Identify primary key / composite key
- Detect schema differences
- Extract data via Arrow for efficient columnar transfer
- Compare rows and columns
- Generate reconciliation report
Collect Inputs
| Parameter | Required | Description |
|---|---|---|
| Source server | Yes | Source SQL Server (e.g. prod-server.database.windows.net) |
| Source database | Yes | Source database name |
| Target server | Yes | Target SQL Server (e.g. staging-server.database.windows.net) |
| Target database | Yes | Target database name |
| Tables | Yes | Comma-separated schema.table names, or schema.* wildcard (e.g. dbo.Orders,dbo.Items or dbo.*) |
| Auth mode | Yes | sql (user/password) or entra (Azure AD/token) |
| Primary key | Auto-detect | Column(s) forming the row identity. Auto-detect from metadata if not provided. |
| Columns to compare | All | Subset of columns, or all non-PK columns |
| Chunk size | 100000 |
Rows per batch for large tables |
| Output format | console |
console, csv, parquet, or json |
Bundled Script
The reconciliation logic is provided as a standalone script at scripts/reconcile.py. Invoke it with the appropriate arguments based on user inputs:
python scripts/reconcile.py \
--source-server <source_server> \
--source-database <source_database> \
--target-server <target_server> \
--target-database <target_database> \
--tables "<table_spec>" \
--auth <sql|entra> \
--chunk-size <chunk_size> \
--output <console|csv|json>
Optional arguments
| Argument | Description |
|---|---|
--primary-key |
Comma-separated PK column(s). Omit to auto-detect. |
--columns |
Comma-separated columns to compare. Omit to compare all non-PK columns. |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 159 lines · 56 tokens per session scan A f09e45ab5e9d
sql-server-table-reconciliation is a skill published in the GitHub repository OKHP3/skillz (3 stars, last pushed yesterday), licensed MIT. It adds 56 tokens to every session and 1,404 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to sql-server-table-reconciliation, differing in 13 lines, and is treated as a copy.
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