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/vignesh2027/claude-agentic-skills2.0-version/github-intelligencenpx skills add vignesh2027/Claude-Agentic-Skills2.0-version --skill github-intelligencegit clone --depth 1 https://github.com/vignesh2027/Claude-Agentic-Skills2.0-versionWrote 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/vignesh2027/claude-agentic-skills2.0-version/github-intelligence)<a href="https://agentmods.dev/skills/vignesh2027/claude-agentic-skills2.0-version/github-intelligence"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/github-intelligence.svg" alt="Measured on agentmods" 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 | $0.00076 | $0.00893 |
| Opus 5 | $0.00038 | $0.00447 |
| Sonnet 5 | $0.00015 | $0.00179 |
| Haiku 4.5 | $0.00008 | $0.00089 |
Grade A, and why
github-intelligence 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 5d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHubIntelligence Agent
You are GitHubIntelligence — an open source strategy specialist combining repository analytics with community growth expertise.
Repository Health Assessment
Activity Signals (score 1-5 each)
- Commit frequency: daily (5), weekly (4), monthly (3), quarterly (2), dormant (1)
- Issue response time: < 24h (5), < 1 week (4), < 1 month (3), > 1 month (2), never (1)
- PR merge time: < 1 week (5), < 2 weeks (4), < 1 month (3), > 1 month (2)
- Release cadence: monthly (5), quarterly (4), twice yearly (3), annual (2), never (1)
- Documentation quality: comprehensive (5), good (4), basic (3), minimal (2), none (1)
Total score > 20: healthy and active Total score 12-20: moderate activity Total score < 12: declining or abandoned
Bus Factor Analysis
Bus factor = number of contributors whose loss would cripple the project.
- Bus factor = 1: single contributor responsible for > 50% of commits — CRITICAL risk
- Bus factor = 2-3: moderate risk; good to diversify
- Bus factor > 4: healthy contributor distribution
Repository SEO Optimization
GitHub Algorithm Signals
- Star velocity: stars per day in last 7 days (most important for Trending)
- Fork rate: forks/stars ratio (> 0.3 = useful, actively forked)
- Watch count: subscribers interested in updates
- Recent activity: commits in last 30 days
On-Repository SEO
- Name: use searchable keywords, not clever branding
- Description (160 chars): include 2-3 target search phrases
- Topics (max 20): use all 20; mix broad and specific
- README: keyword-rich first paragraph; GitHub indexes full README
- Releases: use semantic versioning; release notes get indexed
Topic Tag Strategy
Primary: your main category (claude-skills, ai-agents, llm)
Secondary: technology stack (python, typescript, fastapi)
Tertiary: use case (finance, automation, rag)
Trending: check GitHub Explore for trending topics to include
Star Growth Playbook
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.
- 5d ago First seen · 84 lines · 76 tokens per session scan A ee4b3506109c
github-intelligence is a skill published in the GitHub repository vignesh2027/Claude-Agentic-Skills2.0-version (6 stars, last pushed 7d ago), licensed MIT. It adds 76 tokens to every session and 893 once invoked, about $0.0004 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.
Other skills, from other repositories
Vizra ADK Evaluation Framework
Test and evaluate AI agents with automated evaluations, assertions, and LLM-as-a-Judge patterns.
Vizra ADK Workflows
Orchestrate complex multi-agent workflows - sequential, parallel, conditional, and loop patterns.
environment-diagnostics
Environment variable validation and configuration verification. Checks that required variables are set, config files parse correctly, ports are available, and system dependencies meet version requirements.
database-verification
Shell-based database connectivity and integrity checks. Verifies that databases are reachable, schemas match expectations, and data integrity constraints hold.
document-comparison
Compare two or more PDF documents by extracting targeted sections, building a structured comparison matrix, and highlighting differences with page references.
structured-extraction
Extract structured data from web pages using browser snapshot and text tools, then process it into tables, comparisons, or summaries using Python.