Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/fergupa/claude_pluginsnpx agentmods add commands/fergupa/claude_plugins/metrics-reviewWrote 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/commands/fergupa/claude_plugins/metrics-review)<a href="https://agentmods.dev/commands/fergupa/claude_plugins/metrics-review"><img src="https://agentmods.dev/badge/commands/fergupa/claude_plugins/metrics-review/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/commands/fergupa/claude_plugins/metrics-review"><img src="https://agentmods.dev/badge/commands/fergupa/claude_plugins/metrics-review.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 | $0.00011 | $0.01031 |
| Opus 5.5 | $0.00004 | $0.00412 |
| Sonnet 5.5 | $0.00002 | $0.00206 |
| Haiku 4.5 | $0.00001 | $0.00103 |
Grade A, and why
metrics-review 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 1mo 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Metrics Review
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Review and analyze product metrics, identify trends, and surface actionable insights.
Workflow
1. Gather Metrics Data
If ~~product analytics is connected:
- Pull key product metrics for the relevant time period
- Get comparison data (previous period, same period last year, targets)
- Pull segment breakdowns if available
If no analytics tool is connected, ask the user to provide:
- The metrics and their values (paste a table, screenshot, or describe)
- Comparison data (previous period, targets)
- Any context on recent changes (launches, incidents, seasonality)
Ask the user:
- What time period to review? (last week, last month, last quarter)
- What metrics to focus on? Or should we review the full product metrics suite?
- Are there specific targets or goals to compare against?
- Any known events that might explain changes (launches, outages, marketing campaigns, seasonality)?
2. Organize the Metrics
Structure the review using the metrics hierarchy from the metrics-tracking skill: North Star metric at the top, L1 health indicators (acquisition, activation, engagement, retention, revenue, satisfaction), and L2 diagnostic metrics for drill-down.
If the user has not defined their metrics hierarchy, help them identify their North Star and key L1 metrics before proceeding.
3. Analyze Trends
For each key metric:
- Current value: What is the metric today?
- Trend: Up, down, or flat compared to previous period? Over what timeframe?
- vs Target: How does it compare to the goal or target?
- Rate of change: Is the trend accelerating or decelerating?
- Anomalies: Any sudden changes, spikes, or drops?
Identify correlations:
- Do changes in one metric correlate with changes in another?
- Are there leading indicators that predict lagging metric changes?
- Do segment breakdowns reveal that an aggregate trend is driven by a specific cohort?
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.
- 1mo ago First seen · 115 lines · 11 tokens per session scan A c19a1cb501d8
metrics-review is a command published in the GitHub repository fergupa/claude_plugins (2 stars, last pushed 8mo ago), licensed Apache-2.0. It adds 11 tokens to every session and 1,031 once invoked, about $0.0000 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 2026-09-03.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.