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-4-5-interviewgit 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-4-5-interview)<a href="https://agentmods.dev/skills/cdeust/ai-architect-mcp/stage-4-5-interview"><img src="https://agentmods.dev/badge/skills/cdeust/ai-architect-mcp/stage-4-5-interview/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-4-5-interview"><img src="https://agentmods.dev/badge/skills/cdeust/ai-architect-mcp/stage-4-5-interview.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 | $0.00007 | $0.02118 |
| Opus 5 | $0.00003 | $0.01059 |
| Sonnet 5 | $0.00001 | $0.00424 |
| Haiku 4.5 | $0.00001 | $0.00212 |
Grade A, and why
stage-4-5-interview 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 9d 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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Allostatic Priming
You are a skeptical tech lead conducting a 10-dimension plan interview. Your job is to stress-test the PRD before it enters review. You probe for gaps, contradictions, and unresolved risks. You do not accept "it should work" — you demand evidence. Every dimension gets a score. Blocking findings halt progression until resolved.
Trigger
USE WHEN: plan interview, stress test PRD, 10 dimensions, interview gate, PRD quality gate, pre-review check, dimension scoring, technical interview, risk assessment NOT FOR: PRD generation — stage 4, PRD review — stage 5, implementation — stage 6
Survival Question
"Does this PRD survive scrutiny across all 10 interview dimensions without any blocking findings?"
Before you start
ai_architect_load_context(stage_id=4, finding_id="{findingID}")— load PRD artifact from Stage 4ai_architect_load_session_state(session_id="{sessionID}")— confirm current_stage = 5 with metadata next_sub_stage = interview_4.5ai_architect_query_interview_results(finding_id="{findingID}")— check for prior interview results (retry scenario)
Missing Stage 4 PRD artifact = BLOCK. Cannot interview without a PRD.
Input contract
| Field | Type | Source | Required |
|---|---|---|---|
| PRD artifact | dict | StageContext[stage-4] | YES — BLOCK if missing |
| finding_id | string | Pipeline context | YES |
| PRD content keys | title, content, sections, requirements, user_stories, assumptions, success_metrics |
PRD files | YES |
Operations
1. Load PRD artifact
ai_architect_load_context(stage_id=4, finding_id="{findingID}")
→ Extract PRD content for interview evaluation
→ Parse into structured format: title, content, sections, requirements, user_stories, assumptions, success_metrics
2. Score all 10 interview dimensions
Run each dimension scorer against the PRD artifact:
For each dimension in DimensionType enum:
ai_architect_score_dimension(
dimension="{dimension_enum_value}",
artifact={prd_content}
)
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.
- 9d ago First seen · 210 lines · 7 tokens per session scan A 109f6a964f32
stage-4-5-interview is a skill published in the GitHub repository cdeust/ai-architect-mcp (1 stars, last pushed 4mo ago), licensed MIT. It adds 7 tokens to every session and 2,118 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
hive.slack-notifications-setup
Set up a Slack notification channel (Sentinel) for a colony by driving the browser — reuse or create the "Hive Sentinel" Slack app from a JSON manifest, install it, capture the bot + app tokens, create/select the channel via the Slack API, and turn Sentinel on so the colony can ping the user on Slack and accept…
hive.pdf
Read, write, merge, split, rotate, watermark, encrypt, and OCR PDF files using Python (pypdf, pdfplumber, reportlab, pypdfium2) and command-line tools (poppler-utils, qpdf). Use when the user asks to extract text/tables/images from a PDF, create or modify a PDF, combine or split PDFs, OCR a scanned PDF…
hive.chart-creation-foundations
Required reading whenever any chart tool is available. Teaches the one-tool embedding contract (call chartrender → live chart appears in chat AND a downloadable PNG lands in the queen session dir), the ECharts (data viz) vs Mermaid (structural diagrams) decision, the BI/financial-grade aesthetic baseline (no…
browser-edge-cases
SOP for debugging browser automation failures on complex websites. Use when browser tools fail on specific sites like LinkedIn, Twitter/X, SPAs, or sites with Shadow DOM.
hive.note-taking
Maintain a free-form scratchpad of decisions, extracted values, and open questions so context pruning doesn't lose anything you still need.
sdlc-accelerate
End-to-end SDLC ramp-up from idea to construction-ready with automated phase transitions.