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 QBall-Inc/the-bulwark --skill mock-detectiongit clone --depth 1 https://github.com/QBall-Inc/the-bulwarkWrote 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/qball-inc/the-bulwark/mock-detection)<a href="https://agentmods.dev/skills/qball-inc/the-bulwark/mock-detection"><img src="https://agentmods.dev/badge/skills/qball-inc/the-bulwark/mock-detection/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/qball-inc/the-bulwark/mock-detection"><img src="https://agentmods.dev/badge/skills/qball-inc/the-bulwark/mock-detection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00013 | $0.04310 |
| Opus 5 | $0.00006 | $0.02155 |
| Sonnet 5 | $0.00003 | $0.00862 |
| Haiku 4.5 | $0.00001 | $0.00431 |
Grade A, and why
mock-detection scanned grade A with 1 finding 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 10d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
const mockSpawn = jest.spyOn(child_process, 'spawn') Copies of this mod
1 near-identical copy found in the catalogue:
- mock-detection — 88% identical, 1,044 lines differ
How it starts
The opening of the file, as written. The whole thing — 534 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mock Detection
Prompt template for deep mock appropriateness analysis using call graph tracing. Designed for a Sonnet sub-agent to detect T1-T4 violations and track violation scope.
When to Use This Skill
This is an internal skill loaded by the orchestrator during Test Audit pipeline.
| Context | Action |
|---|---|
/test-audit invoked |
Orchestrator loads this skill for Stage 2 |
| Test Audit pipeline triggered by hook | Orchestrator loads this skill for Stage 2 |
| Need deep mock analysis | Load directly as prompt template for Sonnet |
| Files flagged by test-classification | Analyze only needs_deep_analysis: true files |
DO NOT use for:
- Direct user invocation (not user-invocable)
- Surface-level classification (use
test-classificationskill) - Full audit synthesis (use
test-auditskill)
Role in Test Audit Pipeline
This skill provides the second stage prompt template:
test-audit (P0.8) orchestrates:
Stage 1: test-classification (Haiku) → classification YAML
Stage 2: mock-detection (Sonnet) → violations YAML ← THIS SKILL
Stage 3: synthesis (Sonnet) → audit report
The orchestrator loads this skill and constructs a 4-part prompt for a general-purpose Sonnet sub-agent.
4-Part Prompt Template
GOAL
Analyze flagged test files for T1-T4 violations using mock appropriateness rubric and call graph analysis. Track the full scope of each violation for test effectiveness calculation.
CONSTRAINTS
- Do NOT modify any files
- Only analyze files with
needs_deep_analysis: truefrom classification - Use call graph analysis to detect broken integration chains
- Track violation scope (all affected lines, not just violation line)
- Provide full context for each violation (line, snippet, reason, fix)
- Complete within 50 tool calls
CONTEXT
Classification output: {classification_yaml_path}
Files to analyze: List of files with needs_deep_analysis: true
Mock appropriateness rubric: See "Mock Appropriateness Rubric" section below
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 534 lines · 13 tokens per session scan A 90d81574c1ed
mock-detection is a skill published in the GitHub repository QBall-Inc/the-bulwark (8 stars, last pushed yesterday), licensed MIT. It adds 13 tokens to every session and 4,310 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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