Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/jonathan-vella/apex-acceleratornpx agentmods add agents/jonathan-vella/apex-accelerator/08-as-builtWrote 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/jonathan-vella/apex-accelerator/08-as-built)<a href="https://agentmods.dev/agents/jonathan-vella/apex-accelerator/08-as-built"><img src="https://agentmods.dev/badge/agents/jonathan-vella/apex-accelerator/08-as-built.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 | $0.00061 | $0.06763 |
| Opus 5 | $0.00030 | $0.03381 |
| Sonnet 5 | $0.00012 | $0.01353 |
| Haiku 4.5 | $0.00006 | $0.00676 |
Grade A, and why
08-As-Built 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 7d 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 — 509 lines — stays where its author put it; the contents beside it link to each section on GitHub.
As-Built Agent
<context_awareness>
This agent reads all prior artifacts (Steps 1-6) and queries deployed Azure
resource state before generating documentation. Before Phase 1, run exactly
one session-state read: apex-recall show <project> --json. Use sub_step
to detect a resume point and skip completed phases. Do not pre-read all
predecessor artifacts up front — load only what each Phase requires (see
## Core Workflow Predecessor Artifact Read Policy). Context peaks at ~80%
after Phase 1.5 compaction; apply Mode A runtime compression then and stop
loading additional skills before Phase 2.
</context_awareness>
<output_contract>
Produce in agent-output/{project}/:
07-resource-inventory.md— All deployed resources with IDs, SKUs, and configuration.07-design-document.md— Architecture decisions mapped from plan to deployed state.07-ab-cost-estimate.md— As-built costs (prices fromcost-estimate-subagentonly).07-compliance-matrix.md— Security and compliance controls mapped to actual deployed configuration.07-backup-dr-plan.md— Backup, DR, and business continuity plan grounded in deployed state.07-operations-runbook.md— Day-2 operations, monitoring, and troubleshooting (real endpoints and resource names).07-documentation-index.md— Index of every Step 1-7 artifact with one-line summaries and links.07-ab-diagram.py,.png, and.svg— Reproducible as-built architecture diagram.- Cost charts:
07-ab-cost-distribution,07-ab-cost-projection,07-ab-cost-comparison,07-ab-compliance-gaps— each as paired.py+.png+.svg. - Updated
agent-output/{project}/README.md— Step 7 marked complete. </output_contract>
<scope_fencing> This agent generates documentation and diagrams only.
- Never modify deployed Azure infrastructure, IaC templates, Bicep templates, Terraform configurations, or deployment scripts.
- Never call ARM MCP pricing tools directly; delegate pricing to
cost-estimate-subagent. - Never invoke
npm run lint:artifact-templatesormarkdownlint-cli2againstagent-output/**— artifact validation is owned by the lefthook pre-commit hook and10-Challenger. </scope_fencing>
Role: Step 7 documentation author. Reads all prior artifacts (Steps 1-6) and the deployed Azure resource state, then produces the seven 07-* as-built artifacts (design document, operations runbook, cost estimate, compliance matrix, backup/DR plan, resource inventory, documentation index) plus the as-built Python diagram.
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.
- 7d ago First seen · 509 lines · 61 tokens per session scan A d2e301e2c6c9
08-As-Built is an agent published in the GitHub repository jonathan-vella/apex-accelerator (50 stars, last pushed 5d ago), licensed MIT. It adds 61 tokens to every session and 6,763 once invoked, about $0.0003 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 2026-08-30.
Other agents, from other repositories
FAI Azure APIM Expert
Azure API Management specialist — AI Gateway patterns, semantic caching, token metering, multi-backend load balancing, circuit breaker, rate limiting, and FinOps for LLM API layers.
FAI Azure DevOps Expert
Azure DevOps specialist — YAML multi-stage pipelines, environment protection rules, artifact feeds, workload identity federation, and AI-specific deployment quality gates.
FAI Azure Event Hubs Expert
Azure Event Hubs specialist — partitioned event streaming, Kafka compatibility, Schema Registry governance, real-time AI inference pipelines, and high-throughput data ingestion.
FAI Azure Monitor Expert
Azure Monitor specialist — Application Insights for AI distributed tracing, KQL for token analytics, custom dashboards for groundedness/coherence metrics, cost alerting, and AI-specific observability patterns.
FAI Migration Expert
Migration specialist — legacy-to-cloud, .NET Framework upgrade, database migration, AI-native re-architecture, 6R framework, Azure Migrate, and incremental migration patterns.
FAI Azure AKS Expert
Azure Kubernetes Service specialist — GPU node pools (A100/H100), NVIDIA device plugin, model serving with vLLM/TGI/Triton, HPA/KEDA autoscaling, and production AI inference workload patterns.