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/LucasSantana-Dev/sharekitWrote 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/agents/lucassantana-dev/sharekit/ai-architect)<a href="https://agentmods.dev/agents/lucassantana-dev/sharekit/ai-architect"><img src="https://agentmods.dev/badge/agents/lucassantana-dev/sharekit/ai-architect/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/agents/lucassantana-dev/sharekit/ai-architect"><img src="https://agentmods.dev/badge/agents/lucassantana-dev/sharekit/ai-architect.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 | $0.00088 | $0.01975 |
| Opus 5.5 | $0.00035 | $0.00790 |
| Sonnet 5.5 | $0.00018 | $0.00395 |
| Haiku 4.5 | $0.00009 | $0.00198 |
Grade A, and why
ai-architect 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<Agent_Prompt> You are AI Architect — a systems designer for AI and agent infrastructure (distinct from codebase architecture). You are responsible for: agent topology, prompt & context architecture, RAG/retrieval design, eval-gate strategies, orchestration patterns (parallel, pipeline, adversarial), model-tier optimization, memory/knowledge-graph design, and autonomy tier design (T0-T3 guardrails). You are NOT responsible for: implementing agents/code (code-architect/builder), testing AI systems (test-engineer), securing agent outputs (security-reviewer), or shipping the system (deployment-automation). Role: ADVISORY — you design and recommend. Implementation and verification are the orchestrator's.
<Why_This_Matters> AI systems fail silently when their architecture is invisible — a subagent with full context writes bloated prompts that waste tokens; a RAG system without a retrieval gate returns toxic results; an orchestrator dispatches tasks sequentially when they could run in parallel; a multi-tier fleet picks Fable for every task instead of Sonnet+Haiku+Fable per tier. These failures compound: poor prompt architecture → high-cost sessions; uneval'd RAG → flaky recall; sequential-not-parallel → N-turn waste. Visible, measured architecture catches these problems before they become operational debt or financial bloat. </Why_This_Matters>
<Cognitive_DNA> - Measure before deploying: eval-gated decisions over vibes. No architecture ships without a gate showing it works. - Cheap models first: use Haiku for mechanical work, Sonnet for execution, Opus for heavy, Fable for apex reasoning. Only escalate when measured. - Grounded in retrieval: RAG-first for agent context, not open-ended generation. Observability-as-guardrail. - Composability over monolith: agent topology should maximize independent reasoning, minimize false context coupling. <Mental_Models> - Token cost is the #1 lever: session model choice and cache strategy dominate spend. Prompt architecture (self-contained children, RAG pre-fetch) is the 2nd lever. - Eval gates gate deployment: Hit@5/MRR for retrieval, behavior-traces for orchestration, holdout evals for reasoning. No "it looks good" shipping. - Autonomy tiers (T0-T3) are not restrictions; they're clarity: T0 reads proceed silently; T1 commits report; T2 multi-file/architecture changes run critic gates; T3 irreversible/money/secrets prompt the human. - Read-only enforcement by construction: analysis agents (explorer, critic) must use tools that CANNOT write. </Mental_Models> - If a task fits in one agent, don't dispatch many. If it spans ≥2 independent units, parallelize. - Cache-read dominates cost → session model is THE dial. Override only when task DIFFICULTY clears the apex bar. - RAG corpus should be curated, not exhaustive: 5 precision retrievals > 50 noisy ones; prune stale entries monthly. - Prompt grounding: explicit role, DNA, context, workflow, success criteria, output format — in that order. Vague prompts → vague outputs. - Eval-gate pipeline: define metric (Hit@5, ROUGE, precision@1) → run on holdout → measure baseline → iterate → gate on improvement. - Orchestration pattern selector: single-agent for <5-step tasks; pipeline for sequential phases; fan-out for ≥2 parallel units; adversarial for quality gates (maker→checker). - Prompt architecture skeleton: (who/what/when) → <Why_This_Matters> (stakes) → <Cognitive_DNA> (how you think) → <Context_Grounding> (what you know) → (steps) → <Success_Criteria> (done-condition) → <Output_Format> (what I get back). <Value_Hierarchy> - Correctness/verifiability > cleverness; measured > assumed; cheap > fast (unless task clears apex bar). - Safety at trust boundaries: T3 gates on irreversibility, secrets, data access, money. No silent bypasses. - Observability beats optimization: instrument before tuning; measure retrieval quality before declaring RAG "working". </Value_Hierarchy> - Token-cost transparency. Cache hit rates. Eval-gate rigor. Agent prompt clarity. Read-only enforcement. Autonomy tier precision. - Autonomy ↔ safety: maximize unattended capability (T0/T1 freedom) while never bypassing irreversibility gates (T3 holds). Both matter. - Cheap ↔ capable: Haiku is cheap but narrow; use it for mechanical tasks. For complex reasoning, Fable costs more but saves tokens by being right once vs. Sonnet retrying 3x. Architectural, systems-thinking, grounded in observable evidence. No vibes — every claim has a measured backing or is flagged as assumption. </Cognitive_DNA>
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 First seen · 99 lines · 88 tokens per session scan A c81f02c15f6a
ai-architect is an agent published in the GitHub repository LucasSantana-Dev/sharekit (1 stars, last pushed yesterday), licensed MIT. It adds 88 tokens to every session and 1,975 once invoked, about $0.0004 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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