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 skills add cdeust/ai-architect-mcp --skill stage-5-reviewgit clone --depth 1 https://github.com/cdeust/ai-architect-mcpWrote 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/cdeust/ai-architect-mcp/stage-5-review)<a href="https://agentmods.dev/skills/cdeust/ai-architect-mcp/stage-5-review"><img src="https://agentmods.dev/badge/skills/cdeust/ai-architect-mcp/stage-5-review/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/cdeust/ai-architect-mcp/stage-5-review"><img src="https://agentmods.dev/badge/skills/cdeust/ai-architect-mcp/stage-5-review.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 | $0.00004 | $0.02624 |
| Opus 5 | $0.00002 | $0.01312 |
| Sonnet 5 | $0.00001 | $0.00525 |
| Haiku 4.5 | $0.00000 | $0.00262 |
Grade A, and why
stage-5-review 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 11d 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 — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Allostatic Priming
You are a hostile reviewer. Your job is to find what fails, not to validate what passes. Run the full verification suite and loop back to generation if anything is below threshold. You assume the PRD is wrong until the algorithms prove otherwise. You run 7 verification algorithms — not opinions.
Trigger
USE WHEN: PRD review, verify PRD, run verification, check PRD quality, review specification, 7 algorithms, compound score, evaluator, quality gate, review loop NOT FOR: generate PRD — stage 4, implement — stage 6, health check — stage 0
Survival Question
"Have all 7 verification algorithms run and returned scores above threshold — with no claim marked as overclaiming?"
Before you start
ai_architect_load_context(stage_id=4, finding_id="{findingID}")— load PRD from Stage 4ai_architect_load_context(stage_id=4, finding_id="{findingID}-interview")— load interview results (advisory warnings if PROVISIONAL)ai_architect_load_session_state(session_id="{sessionID}")— confirm currentStage = 5, check retryCount
Missing Stage 4 PRD = BLOCK. Missing Stage 4.5 interview result = BLOCK.
Input contract
| Field | Type | Source | Required |
|---|---|---|---|
| 9 PRD files | MD/JSON | StageContext[stage-4] | YES — BLOCK if missing |
| Interview result | JSON | StageContext[stage-4.5] | YES — BLOCK if missing |
| Advisory warnings | list | Interview result | NO — only if PROVISIONAL |
| retryCount | int | PipelineState | YES |
Operations
1. Run Standard Chain-of-Verification (Algorithm 1)
ai_architect_verify_claim(
content="{prd_requirement_claim_text}",
claim_type="atomic_fact",
context="{prd_content}",
priority=80
)
→ Chain of Verification: systematic verification of each requirement claim
→ Returns per-claim evaluation with score and verdict
2. Run Zero-LLM Graph Verification (Algorithm 3)
ai_architect_verify_graph(
graph_data={
"nodes": [
{"node_id": "{uuid}", "claim_id": "{uuid}", "label": "FR-001", "node_type": "requirement"},
{"node_id": "{uuid}", "claim_id": "{uuid}", "label": "AC-001", "node_type": "test"},
{"node_id": "{uuid}", "claim_id": "{uuid}", "label": "STORY-001", "node_type": "specification"}
],
"edges": [
{"source_id": "{FR_uuid}", "target_id": "{AC_uuid}", "relationship": "tests", "weight": 1.0},
{"source_id": "{STORY_uuid}", "target_id": "{FR_uuid}", "relationship": "requires", "weight": 1.0}
]
}
)
→ Verify complete acyclic traceability graph
→ Detect orphan IDs, cycles, contradictions
→ Binary pass/fail per structural check
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.
- 11d ago First seen · 258 lines · 4 tokens per session scan A 7bf032732991
stage-5-review is a skill published in the GitHub repository cdeust/ai-architect-mcp (1 stars, last pushed 4mo ago), licensed MIT. It adds 4 tokens to every session and 2,624 once invoked, about $0.0000 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 skills, from other repositories
test-reporting
Run the Level 2 dummy agent integration test suite and produce a detailed HTML report with per-test input → outcome analysis.
reproducibility-validate
Run a workflow multiple times and compare outputs to produce a similarity score and pass/fail verdict.
eval-workflow
Run evaluation tests against a multi-agent workflow to assess orchestration quality and failure archetype resistance.
eval-agent
Run evaluation tests against an agent to assess quality and archetype resistance.
auto-test-execution
Automatically execute tests when code-generating agents modify source files, enforcing the execute-before-return pattern.
Vizra ADK Evaluation Framework
Test and evaluate AI agents with automated evaluations, assertions, and LLM-as-a-Judge patterns.