metric-diagnostics

A workflow for finding out why a measurement changed or differs from what was expected.

In plain words
What is it for?
Use it to investigate metric movements, anomalies, gaps, and discrepancies. It can support follow-up recommendations when paired with a decision-analysis workflow.
Why use it?
It helps separate verified causes from likely or unresolved explanations by reproducing the measurement and checking possible drivers.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/xiaomimimo/mimo-code/metric-diagnostics
Any agent
npx skills add XiaomiMiMo/MiMo-Code --skill metric-diagnostics
Clone the repo
git clone --depth 1 https://github.com/XiaomiMiMo/MiMo-Code

Made for: Claude Code, Codex.

Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,868 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00036 $0.01868
Opus 5 $0.00018 $0.00934
Sonnet 5 $0.00007 $0.00374
Haiku 4.5 $0.00004 $0.00187

Measured yesterday against content hash c9293227b592, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

metric-diagnostics 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 yesterday.

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.

packages/opencode/src/skill/builtin/.bundle/data-analytics/workflows/metric-diagnostics/SKILL.md · 134 lines

How it starts

The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Use $gather-business-context when business context is needed to understand the metric, analysis period, ownership, or plausible explanations.

Use $product-business-analysis when the task asks for a recommendation or tradeoff decision after diagnosing the movement.

Use $analyze-data-quality when dashboard trust, grain, freshness, or source disagreement could affect the metric.

Metric Diagnostics

Use this skill to diagnose why a metric changed or differs from expectation. Reproduce the metric, define the comparison, quantify the movement, validate likely drivers, and state what is verified, likely, unresolved, and useful to do next.

Clarify with the user when a missing input would materially change the analytical frame or recommendation. Otherwise make a reasonable assumption, state it, and proceed.

Skill Configuration

Source Discovery And Verification

Use the relevant semantic layer as a starting map, not a boundary.

  1. Explore all possible sources. Search every connected or provided source that could contain task-relevant data or change the interpretation. Within each structured-data source, run fresh catalog or metadata discovery for relevant schemas, datasets, tables, views, models, and metrics. Known sources, tables, dashboards, and semantic mappings are starting points, not stopping points.
  2. Compare duplicates and conflicts. When sources overlap or disagree, compare ownership, freshness, definition, grain, coverage, and directness. Use the best authoritative source, or combine complementary sources when needed. Note material conflicts, explain why the selected source or sources control the answer, and verify selected data through live reads before concluding.

Source Access Guardrail

Before querying sources, building artifacts, or drawing conclusions, determine whether the answer requires a specific source of truth.

If a required source is unavailable, stop that path. Tell the user what source is needed, ask them to make it available or provide a reviewed fallback, and do not treat weaker substitutes as equivalent.

Read the full file on GitHub · 134 lines

Files

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.

Changes

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.

  1. yesterday First seen · 134 lines · 36 tokens per session scan A c9293227b592

Subscribe to this mod's changes

metric-diagnostics is a skill published in the GitHub repository XiaomiMiMo/MiMo-Code (12,904 stars, last pushed 2d ago), licensed MIT. It adds 36 tokens to every session and 1,868 once invoked, about $0.0002 per session on Opus 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 2026-08-30.

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