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 commands/goktug/ai-crew/buildgit clone --depth 1 https://github.com/Goktug/ai-crewWrote 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/goktug/ai-crew/build)<a href="https://agentmods.dev/commands/goktug/ai-crew/build"><img src="https://agentmods.dev/badge/commands/goktug/ai-crew/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 | $0.00014 | $0.00161 |
| Opus 5 | $0.00007 | $0.00081 |
| Sonnet 5 | $0.00003 | $0.00032 |
| Haiku 4.5 | $0.00001 | $0.00016 |
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 4d 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
Invoke the ai-crew:incremental-implementation skill alongside ai-crew:test-driven-development.
Pick the next pending task from the plan. For each task:
- Read the task's acceptance criteria
- Load relevant context (existing code, patterns, types)
- Write a failing test for the expected behavior (RED)
- Implement the minimum code to pass the test (GREEN)
- Run the full test suite to check for regressions
- Run the build to verify compilation
- Commit with a descriptive message
- Mark the task complete and move to the next one
If any step fails, follow the ai-crew:debugging-and-error-recovery skill.
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.
- 4d ago First seen · 19 lines · 14 tokens per session scan A 5829b29be203
build is a command published in the GitHub repository Goktug/ai-crew (6 stars, last pushed 1mo ago), licensed MIT. It adds 14 tokens to every session and 161 once invoked, about $0.0001 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 commands, from other repositories
plan-execute
Execute a validated plan: worktree isolation, TDD scaffolding, level-based parallel agents, quality gate with smoke test, PR creation and merge. Handles everything through to merged PR.
build
Implement the next task incrementally — build, test, verify, commit.
work-flow-feature
Complete workflow for developing a new feature, from exploration to merge.
build
Implement the next task incrementally — build, test, verify, commit.
desarrollar
Implementar la issue siguiendo TDD y hacer commits con historia de usuario.
build
Implement the next task incrementally — build, test, verify, commit.