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/impactbrussels/ainativeos/agentic-build-loopnpx skills add impactbrussels/AINativeOS --skill agentic-build-loopgit clone --depth 1 https://github.com/impactbrussels/AINativeOSWhat 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 | $0.00174 | $0.01130 |
| Opus 5 | $0.00087 | $0.00565 |
| Sonnet 5 | $0.00035 | $0.00226 |
| Haiku 4.5 | $0.00017 | $0.00113 |
Grade A, and why
agentic-build-loop 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Build Loop
The cost of syntax is zero. An agent writes correct code faster than you can, in any language, so the job is not to type lines. You specify the system and the tests that judge it, and the agents iterate until the suite goes green. Hand them a vague request and they build the wrong thing fluently. Hand them a spec and a harness and they have nowhere to hide. You are the conductor; the architecture is the score.
The method
Full build loop, eval gates, a worked build, anti-patterns and a copyable template:
references/agentic-build-method.md.
Step 1: Write the spec and the acceptance tests first
Before any code, write two things the agent cannot infer: what the feature must do, and the handful of tests that decide whether it did. Plain language is fine if that is all you have. The tests are the contract the agent builds to. No target, no grip, and the agent drifts while you call the drift a feature.
Step 2: Plan mode, framework first
Run the agent in plan mode and ask for the structure, not the logic: the modules, their boundaries, their interfaces, the skeleton empty of muscle. Read that hierarchy against your architecture and validate it before a single line of logic. You can move a wall on a diagram for nothing. You cannot move it once the roof is on.
Step 3: Let agents fill the logic
Only when the frame is right, release the agents to write the logic. They run the tests, read the failures, fix, and run again until the suite passes. Review every diff. A passing demo proves the agent typed something plausible, not that it built the right thing.
Step 4: Keep the master file current
The agents read one master file every session (CLAUDE.md, AGENTS.md, .cursor/rules, GEMINI.md).
At the end of the session, append the trade-offs you made: the boundary you moved, the constraint you
added, the thing an agent must never do next time. The file is where the multiplier lives.
Output
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 82 lines · 174 tokens per session scan A 0568b8b0bf93
agentic-build-loop is a skill published in the GitHub repository impactbrussels/AINativeOS (1 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 174 tokens to every session and 1,130 once invoked, about $0.0009 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.
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