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/build)<a href="https://agentmods.dev/commands/vignesh2027/ai-agent-skills/build"><img src="https://agentmods.dev/badge/commands/vignesh2027/ai-agent-skills/build.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 | $0.00121 |
| Opus 5 | $0.00000 | $0.00060 |
| Sonnet 5 | $0.00000 | $0.00024 |
| Haiku 4.5 | $0.00000 | $0.00012 |
Grade A, and why
build 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 8d 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/incremental-implementation/SKILL.md and skills/test-driven-development/SKILL.md.
Implement the feature or fix described. Follow incremental implementation:
- Identify the vertical slice (smallest end-to-end working version)
- Build the walking skeleton first (hardcoded, no edge cases)
- Write tests for the skeleton
- Flesh out the implementation incrementally
- Add edge cases with tests
- Leave the system in a deployable state at every commit
Do not implement the entire feature in one pass. Each increment must be testable and deployable.
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.
- 8d ago First seen · 12 lines · 0 tokens per session scan A f90626c30f84
build is a command published in the GitHub repository vignesh2027/AI-AGENT-SKILLS (2 stars, last pushed 10d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 121 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
cpp-test
Enforce TDD workflow for C++. Write GoogleTest tests first, then implement. Verify coverage with gcov/lcov.
tdd
A command for test-driven development, a method where you write a failing test first, then code to pass it, and finally improve the code.
api-aqa-flow
Workflow for backend API test automation: TMS / Issue Tracker test cases → automated API tests, HITL-gated.
test-driven-development
Use when implementing any feature or bugfix, before writing implementation code - write the test first, watch it fail, write minimal code to pass; ensures tests actually verify behavior by requiring failure first.
feature-implement-execute
Phase 4 of develop: Execute the implementation plan with per-task TDD, quality gates, and completion verification.
usage-add
PitWay: Accumulate measured planning or qa token usage onto a milestone.