Borrowing it
Nothing to install: this file belongs to nikzlabs/shipit. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/nikzlabs/shipit/main/.agents/skills/prompt-architecture/SKILL.mdgit clone --depth 1 https://github.com/nikzlabs/shipitWrote 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/nikzlabs/shipit/prompt-architecture)<a href="https://agentmods.dev/skills/nikzlabs/shipit/prompt-architecture"><img src="https://agentmods.dev/badge/skills/nikzlabs/shipit/prompt-architecture/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nikzlabs/shipit/prompt-architecture"><img src="https://agentmods.dev/badge/skills/nikzlabs/shipit/prompt-architecture.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00075 | $0.00934 |
| Opus 5.5 | $0.00030 | $0.00374 |
| Sonnet 5.5 | $0.00015 | $0.00187 |
| Haiku 4.5 | $0.00007 | $0.00093 |
Grade A, and why
prompt-architecture 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 1mo 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt architecture
LLM prompts — agent system instructions, voice cleanup, session naming — are content, not logic. Keep the two separated.
Prompt text is data: it lives in .md files
Co-locate the .md with the code that composes it. Prose reviews as prose, diffs cleanly, and needs no backtick or ${} escaping.
Load it with loadPrompt(import.meta.url, "./x.md") (orchestrator/load-prompt.ts), at module top level — once at init, never per call. A missing file then throws at boot rather than mid-turn.
Not a bundler ?raw import: production runs TS via tsx with no bundler, so fs.readFileSync(new URL(...)) is what actually works.
Examples: agents/<id>/system-prompt.md, voice/cleanup-prompt.md, orchestrator/prompts/*.md.
A per-call hole is still data. session-namer.ts loads session-naming-prompt.md at module level and fills its {{MESSAGE}} per call through buildSessionNamePrompt. The file read happens once; only the substitution is per call, which is composition and belongs in TypeScript. A prompt whose text varies per request is not a reason to inline it.
Prompt composition is code
Axis branching and fragment selection stay in TypeScript. In agent-instructions.ts, renderInstructions fills {{TOKEN}} holes in prompts/skeleton.md via fillPromptTokens, which throws on an unfilled token — that's the guard ensuring no literal {{FOO}} ever reaches the model.
There are two axes: agentId (Parallel-sessions wording) and the session mode, which has three values — std, ops (docs/128), and sandbox (docs/211). isOps and isSandbox are mutually exclusive, and ops wins if both are passed. Every (agentId, mode) pair is precomputed, so don't write code or tests that assume a single isOps boolean.
The prompt-cache contract is load-bearing
Every (agentId, mode) variant renders once at module load into PRECOMPUTED_INSTRUCTIONS, a module-level ReadonlyMap (typed as read-only; not runtime-frozen). The per-turn path is a pure lookup, which keeps the CLI string byte-stable so the Anthropic prompt cache stays warm.
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.
- 1mo ago Changed 24b04129201b
- 1mo ago Changed 4deeeb8e7978
- 1mo ago First seen · 52 lines · 75 tokens per session scan A f8c48f67e220
prompt-architecture is a skill published in the GitHub repository nikzlabs/shipit (9 stars, last pushed today), licensed Apache-2.0. It adds 75 tokens to every session and 934 once invoked, about $0.0003 per session on Opus 5.5. 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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