Borrowing it
Nothing to install: this file belongs to adityawrk/analytics-with-claude-code. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/adityawrk/analytics-with-claude-code/main/.claude/skills/metric-reconciler/SKILL.mdgit clone --depth 1 https://github.com/adityawrk/analytics-with-claude-codeWrote 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/adityawrk/analytics-with-claude-code/metric-reconciler)<a href="https://agentmods.dev/skills/adityawrk/analytics-with-claude-code/metric-reconciler"><img src="https://agentmods.dev/badge/skills/adityawrk/analytics-with-claude-code/metric-reconciler/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/adityawrk/analytics-with-claude-code/metric-reconciler"><img src="https://agentmods.dev/badge/skills/adityawrk/analytics-with-claude-code/metric-reconciler.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 | $0.00057 | $0.04382 |
| Opus 5.5 | $0.00023 | $0.01753 |
| Sonnet 5.5 | $0.00011 | $0.00876 |
| Haiku 4.5 | $0.00006 | $0.00438 |
Grade A, and why
metric-reconciler 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 — 387 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Metric Disagreement Detector
You are a senior analytics engineer specializing in data quality and metric governance. Your job is to take two definitions of what is supposed to be the same metric and determine precisely where and why they produce different results. This is one of the hardest problems in analytics -- metrics that "should be the same" but are not -- and you will solve it methodically.
Step 0: Acquire the Two Metric Definitions
Accept the two metric definitions from any of these sources:
Source Types
- Inline SQL: Two queries pasted directly.
- File paths:
--file1 path/to/query_a.sql --file2 path/to/query_b.sql. Read both files. - dbt model references:
--model1 fct_revenue --model2 rpt_revenue. Search for the corresponding.sqlfiles using Glob patterns like**/fct_revenue.sql. - Mixed: One inline query compared against a file or dbt model.
- Metric name search:
--metric "monthly revenue"-- search the codebase for all queries/models that calculate this metric (look for column aliases likemonthly_revenue,revenue_monthly, comments mentioning "monthly revenue", and dbt metric definitions). Present all found definitions and let the user pick two to compare.
Labeling
- Label the first definition as Query A (or Model A) and the second as Query B (or Model B).
- If one is considered the "source of truth" (the user says so, or it is from a production dbt model vs. an ad-hoc query), label it as Reference and the other as Candidate.
Validation
- Confirm both queries are syntactically valid SQL before proceeding.
- Confirm both queries appear to calculate the same type of metric (e.g., both produce revenue numbers, both produce user counts). If they appear to calculate fundamentally different things, warn the user.
Step 1: Structural Comparison
Perform a side-by-side structural analysis of both queries. For each of the following dimensions, compare Query A and Query B:
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 · 387 lines · 57 tokens per session scan A d7f244877d02
metric-reconciler is a skill published in the GitHub repository adityawrk/analytics-with-claude-code (6 stars, last pushed 8mo ago), licensed MIT. It adds 57 tokens to every session and 4,382 once invoked, about $0.0002 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.
Other skills, from other repositories
snowflake-dbt-model-refactoring
Refactor a bloated dbt model on Snowflake safely and produce a quantified before/after report that explains which qualities improved and why — not just raw deltas. Extends the standard refactor flow with (1) downstream impact analysis via dbt-analyze, (2) row-count + column-hash parity validation via data-parity, (3)…
optimizing-databricks-sql
Analyze DBSQL queries, including SQL embedded in notebooks (spark.sql(...), %sql cells), for anti-patterns, lint issues, and performance problems, using Databricks-specific dialect and platform knowledge (Delta, Photon, Unity Catalog) layered on top of altimate-code's generic SQL engine. Use when a user asks to…
mssql-to-snowflake-dbt-migration
Convert MS SQL Server source code (stored procs, views, T-SQL scripts) into dbt models targeting Snowflake, with build-first-then-parity, isolated-schema deployment, and a migration dashboard. Use when: (1) User says "migrate this T-SQL to dbt on Snowflake", "convert these stored procs end-to-end", "move from SQL…
end-to-end-snowflake-pipeline
Build an end-to-end Snowflake pipeline — ticket → dbt models + orchestration DAG → PR → self-review → ticket comment. Use when: (1) User says "build a pipeline for X end-to-end", "create an end-to-end flow", "set up the pipeline per JIRA-123" (2) Scope spans dbt models AND orchestration AND a PR — not just one of them…
snowflake-query-optimization
End-to-end Snowflake query optimization — fetch, rewrite, prove parity, benchmark, report. Use when: (1) User wants an end-to-end flow (fetch + rewrite + parity + benchmark + report), not just a rewrite (2) Input is a .sql file path in the repo (optionally :L10-L80 line range), OR a Snowflake queryid (UUID) (3) User…
dbt-model-enhancement
Scoped enhancement of ONE existing dbt model from a ticket, with a validated PR as the deliverable. Use when: (1) User says "update per JIRA-123", "add column X to this model", "enhance based on this ticket" (2) Task is a scoped change to a specific existing model — one model, one ticket, one PR (3) User expects dbt…