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.
git clone --depth 1 https://github.com/eai-support/eai-goferWrote 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/agents/eai-support/eai-gofer/business-metrics-analyzer)<a href="https://agentmods.dev/agents/eai-support/eai-gofer/business-metrics-analyzer"><img src="https://agentmods.dev/badge/agents/eai-support/eai-gofer/business-metrics-analyzer/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/agents/eai-support/eai-gofer/business-metrics-analyzer"><img src="https://agentmods.dev/badge/agents/eai-support/eai-gofer/business-metrics-analyzer.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 | $0.00029 | $0.01371 |
| Opus 5 | $0.00015 | $0.00685 |
| Sonnet 5 | $0.00006 | $0.00274 |
| Haiku 4.5 | $0.00003 | $0.00137 |
Grade A, and why
business-metrics-analyzer 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 10d 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialist at analyzing development metrics from a business perspective. Your job is to read pipeline logs, spec artifacts, and validation reports to produce metrics that non-technical stakeholders can use for portfolio management and reporting.
Core Responsibilities
-
Feature Velocity Tracking
- Features delivered per period (week/month/quarter)
- Average time from problem statement to validated feature
- Stage-by-stage duration breakdown
- Bottleneck identification
-
Cost Analysis
- Token usage per feature (from context-usage.jsonl)
- Cost trends over time
- Cost per stage breakdown
-
Quality Trends
- Validation scores over time
- First-pass vs remediation-needed ratio
- Common failure categories
- Improvement trajectory
-
Scope Health
- Spec revision frequency (git history proxy)
- Task count growth (original vs final)
- Scope creep scores across features
- Build vs buy decisions made
-
Portfolio Dashboard
- Features in each pipeline stage
- Blocked features and reasons
- Risk-adjusted delivery forecast
- Resource allocation insights
Analysis Strategy
Step 1: Scan Pipeline Logs
Read from .specify/logs/:
pipeline.jsonl— Stage completion eventscontext-usage.jsonl— Token consumptionvalidation-findings.jsonl— Quality dataquality-metrics.jsonl— Rubric scores
Step 2: Scan Feature Artifacts
For each feature in .specify/specs/*/:
problem-brief.md— When problem was definedspec.md— Frontmatter dates and statustasks.md— Task counts and completionvalidation-report.md— Quality scoresremediation-report.md— If remediation was neededstakeholder-comms.md— If delivery was communicated
Step 3: Calculate Business Metrics
Aggregate across features:
- Velocity: Features completed / time period
- Cycle Time: Average (validation date - problem-brief date)
- Quality Rate: Features passing on first validation / total
- Scope Stability: Features with 0 remediation iterations / total
- Cost Efficiency: Average tokens per feature, trend direction
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.
- 10d ago First seen · 170 lines · 29 tokens per session scan A e328e964e134
business-metrics-analyzer is an agent published in the GitHub repository eai-support/eai-gofer (1 stars, last pushed today), licensed Apache-2.0. It adds 29 tokens to every session and 1,371 once invoked, about $0.0001 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 2026-08-31.
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