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 ai-analyst-lab/ai-analyst-plugin --skill metricsgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-pluginWrote 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/ai-analyst-lab/ai-analyst-plugin/metrics)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst-plugin/metrics"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/metrics.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00094 | $0.01229 |
| Opus 5 | $0.00047 | $0.00615 |
| Sonnet 5 | $0.00019 | $0.00246 |
| Haiku 4.5 | $0.00009 | $0.00123 |
Grade A, and why
metrics 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 8d 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Metrics
Purpose
Browse, search, and display metric definitions from the active dataset's metric dictionary. Provides quick access to how metrics are defined, computed, and validated.
When to Use
- User says
/metricsor "show me the metrics" or "what metrics do we track?" - During analysis, to confirm a metric's definition before computing it
- When writing a metric spec, to check for existing definitions
Deference rule: metric-shaped meaning questions ("what does ARR mean here?", "how is churn defined?") come here first; if the metric is not in the dictionary and the term looks organizational (a product, team, or general glossary term), hand off to the business skill before suggesting metric-spec.
Invocation
/metrics — list all metrics for the active dataset
/metrics {id} — show full spec for a specific metric
/metrics category={cat} — filter by category (e.g., monetization)
/metrics search={term} — search metric names and descriptions
Instructions
Step 1: Load Metric Dictionary
- Read
.knowledge/active.yamlto identify the active dataset. - Read
.knowledge/datasets/{active}/metrics/index.yamlfor the metric list. - If no metrics directory exists: "No metric dictionary for this dataset. Use the metric-spec skill to define metrics."
Step 2: Execute Command
List all (/metrics):
- Display as a table: id, name, category, direction, validation_status
- Group by category
- Show total count
- If dictionary is empty AND user mentions specific analysis context (e.g., "revenue analysis", "conversion analysis"):
- Read
.knowledge/datasets/{active}/schema.mdto explore available tables/columns - Suggest 3-5 relevant metrics the user could define for their analysis context
- Include suggested SQL formulas for each
- Reference the metric-spec skill for formalization
- Read
Show specific (/metrics {id}):
- Read
.knowledge/datasets/{active}/metrics/{id}.yaml - Display: name, category, owner, full definition (formula, unit, direction, granularity), source tables, dimensions, guardrails, typical range, validation status
- If metric not found:
- Suggest closest match from index (fuzzy string match on name)
- Fallback search strategy: Search
working/,outputs/, and.knowledge/analyses/for recent usage of the metric name - If found in recent work, extract the formula/definition used and offer to formalize it
- If not found anywhere, suggest defining it via metric-spec skill
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.
- 8d ago First seen · 98 lines · 94 tokens per session scan A 69d75ebfa53e
metrics is a skill published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (32 stars, last pushed 12d ago), licensed MIT. It adds 94 tokens to every session and 1,229 once invoked, about $0.0005 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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