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 harness/harness-ai --skill sei-analyticsgit clone --depth 1 https://github.com/harness/harness-aiWrote 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/harness/harness-ai/sei-analytics)<a href="https://agentmods.dev/skills/harness/harness-ai/sei-analytics"><img src="https://agentmods.dev/badge/skills/harness/harness-ai/sei-analytics/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/harness/harness-ai/sei-analytics"><img src="https://agentmods.dev/badge/skills/harness/harness-ai/sei-analytics.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 | $0.00115 | $0.01369 |
| Opus 5.5 | $0.00046 | $0.00548 |
| Sonnet 5.5 | $0.00023 | $0.00274 |
| Haiku 4.5 | $0.00012 | $0.00137 |
Grade A, and why
sei-analytics 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.
This is a copy
100% identical to sei-analytics — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEI Analytics
Configure sprint analytics, investment allocation, capacity forecasting, and release readiness assessments in Harness Software Engineering Insights.
Instructions
Step 1: Establish Scope
Confirm the user's org, team, and tracking period.
Call MCP tool: harness_list
Parameters:
resource_type: "project"
org_id: "<organization>"
Step 2: Identify the SEI Task
Determine which analytics the user needs:
- Sprint Analytics -- Velocity, estimation accuracy, scope change tracking
- Investment Allocation -- Engineering time breakdown by category
- Sprint Planning and Capacity Forecast -- Data-driven sprint planning
- Release Readiness Assessment -- Checklist for release go/no-go
Step 3: Configure Sprint Analytics
Gather from the user:
- Team name and sprint length (weeks)
- Tracking period (last N sprints)
- Issue tracker integration (Jira, Azure DevOps, Linear)
Analyze sprint metrics:
Velocity:
- Story points committed vs. completed per sprint
- Velocity trend over the tracking period
- Carryover rate (% of stories that spill into next sprint)
Estimation Accuracy:
- Actual vs. estimated story points across sprints
- Which issue types are consistently underestimated
- Estimation accuracy trend (improving or degrading)
Scope Change:
- Stories added mid-sprint (scope creep %)
- Stories removed mid-sprint
- Net scope change and its impact on completion rate
Delivery Composition:
- Breakdown by issue type: features, bugs, tech debt, maintenance
- Breakdown by priority: P0/P1 vs. lower priority
- Team-level vs. individual-level trends
Step 4: Analyze Investment Allocation
Gather from the user:
- Teams to analyze
- Time period for analysis
- Investment categories (features, bugs, tech debt, maintenance, ops)
Calculate allocation:
- % of engineering time per category
- Compare against target allocation (e.g., 70% features, 15% tech debt, 10% bugs, 5% ops)
- Trend over time: is tech debt growing or shrinking?
- Per-team breakdowns to identify teams disproportionately spending on bugs or ops
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 · 182 lines · 115 tokens per session scan A dff42ec54011
sei-analytics is a skill published in the GitHub repository harness/harness-ai (27 stars, last pushed 4d ago), licensed Apache-2.0. It adds 115 tokens to every session and 1,369 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to sei-analytics, differing in 1 line, and is treated as a copy.
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