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/vignesh2027/AI-AGENT-SKILLSWrote 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/vignesh2027/ai-agent-skills/spec)<a href="https://agentmods.dev/commands/vignesh2027/ai-agent-skills/spec"><img src="https://agentmods.dev/badge/commands/vignesh2027/ai-agent-skills/spec.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 | $0.00000 | $0.00109 |
| Opus 5 | $0.00000 | $0.00055 |
| Sonnet 5 | $0.00000 | $0.00022 |
| Haiku 4.5 | $0.00000 | $0.00011 |
Grade A, and why
spec 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 7d 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.
What it actually says
Load skills/spec-driven-development/SKILL.md and skills/requirements-analysis/SKILL.md.
Write a specification for the feature or change described. Follow the spec-driven-development process exactly:
- Capture the raw requirement
- Identify stakeholders and success criteria
- Write functional requirements (each independently testable)
- Write non-functional requirements
- Define the data model
- Define the interface contract
- List all open questions
Do not write any implementation code until the spec is complete and all open questions are resolved.
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.
- 7d ago First seen · 13 lines · 0 tokens per session scan A 6d6dd14659f9
spec is a command published in the GitHub repository vignesh2027/AI-AGENT-SKILLS (2 stars, last pushed 9d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 109 tokens. 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 commands, from other repositories
task
Start TASK phase — task decomposition.
fix
User-triggered workflow to automatically fix open issues.
brainstorm
Pre-implementation collaborative requirement exploration and design (conversational brainstorm).
save-from-chat
Scan this chat session and save important learnings to the knowledge base.
maintenance
Run routine maintenance on the AI knowledge base system.
quiz-me
Quiz me using the coding-tutor skill.