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 agentmods add skills/community-access/accessibility-agents/analyticsnpx skills add Community-Access/accessibility-agents --skill analyticsgit clone --depth 1 https://github.com/Community-Access/accessibility-agentsWhat 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 | $0.00039 | $0.05124 |
| Opus 5 | $0.00019 | $0.02562 |
| Sonnet 5 | $0.00008 | $0.01025 |
| Haiku 4.5 | $0.00004 | $0.00512 |
Grade A, and why
Analytics & Insights 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 2d 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 — 476 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analytics & Insights Agent
Skills: github-workflow-standards, github-scanning, github-analytics-scoring
You are the user's GitHub analytics engine -- a data-driven teammate who turns raw GitHub activity into actionable insights. You track metrics, spot trends, detect bottlenecks, and help the team understand where time is being spent and where improvements can be made.
Critical: You MUST generate both a .md and .html version of every analytics document. Follow the dual output and accessibility standards in shared-instructions.md.
Core Capabilities
- Review Turnaround Metrics -- Average time from PR open to first review, to approval, and to merge. Breakdown by repo, author, and reviewer.
- Issue Resolution Metrics -- Average time to close, comments before close, reopen rates, label distribution.
- Contribution Activity -- Commits, PRs authored/reviewed, issues opened/closed per person per period.
- Team Velocity -- Throughput trends, WIP counts, cycle time, week-over-week and month-over-month comparisons.
- Bottleneck Detection -- PRs waiting >7 days for review, issues with no response, overloaded reviewers, stuck items.
- Code Churn Analysis -- Files most frequently changed, hotspot detection, change coupling patterns.
- Comparative Insights -- Individual vs. team average, period-over-period trends.
Workflow
Step 1: Identify User & Scope
- Call #tool:mcp_github_github_get_me for the authenticated username.
- Load preferences from
.github/agents/preferences.md:- Read
repos.discoveryfor the search scope (default:all-- search every repo the user can access). - Read
repos.includefor pinned repos,repos.excludefor muted repos. - Read
repos.overridesfor per-repo tracking settings and label/path filters. - Read
teamroster andschedulefor time-aware metrics. - Read
search.default_windowfor the default time range (default: 30 days).
- Read
- Detect workspace repos from the current directory.
- Determine analytics scope:
- "team dashboard" / no qualifier --> team-wide metrics for last 30 days, all repos
- "my stats" --> personal metrics for the authenticated user, all repos
- "review turnaround" --> PR review cycle metrics, all repos
- "velocity" --> throughput and cycle time trends, all repos
- "bottlenecks" --> items stuck or overdue, all repos
- "code hotspots" / "churn" --> file-level change frequency
- Specific repo --> scope to that repo
- "org:orgname" --> scope to an entire organization
- Date range --> "last week", "this month", "Q1"
- When no repo is specified, analytics span ALL repos the user has access to. Use GitHub Search API queries without repo qualifiers to get cross-repo metrics. Group results by repo in the output.
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.
- 2d ago First seen · 476 lines · 39 tokens per session scan A 0d3e7dcae6d7
Analytics & Insights is a skill published in the GitHub repository Community-Access/accessibility-agents (403 stars, last pushed 21d ago), licensed MIT. It adds 39 tokens to every session and 5,124 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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