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 agentmods add agents/terryc21/workflow-audit/layer4-semantic-evaluationgit clone --depth 1 https://github.com/Terryc21/workflow-auditWhat 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 | $0.00000 | $0.01988 |
| Opus 5 | $0.00000 | $0.00994 |
| Sonnet 5 | $0.00000 | $0.00398 |
| Haiku 4.5 | $0.00000 | $0.00199 |
Grade A, and why
layer4-semantic-evaluation 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 2d 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Layer 4: Semantic Evaluation
Note: Concrete examples in this file are drawn from a real workflow-audit run on the Stuffolio codebase. The evaluation dimensions (discovery, efficiency, feedback, recovery) and persona structure are reusable methodology; specific personas, workflows, and findings are project-specific. A full sample scan lives in
examples/sample-stuffolio-scan/.
Purpose
Layer 4 evaluates workflows from the user's perspective. While Layers 1-3 analyze code structure, Layer 4 asks: "Does this workflow help the user achieve their goal?"
User Goals Analysis
Goal Categories
| Category | User Intent | Example |
|---|---|---|
| Discovery | "What can this app do?" | Sees promotion card, explores |
| Action | "I want to do X" | Add item, track warranty |
| Problem-Solving | "I have a problem" | Damaged item, expiring warranty |
| Optimization | "Make things better" | Clean up, organize, export |
Goal-to-Workflow Mapping
For each user goal, trace the optimal path:
goal: "Track warranty for my new Apple device"
user_type: "New user with Apple product"
optimal_workflow:
steps: 3
path:
- "See AppleCare+ card on dashboard"
- "Tap card, select device"
- "Enter warranty details"
actual_workflow: "Matches optimal (after recent fix)"
rating: "Good"
Evaluation Criteria
Criterion 1: Goal Discoverability
Can users find the feature that helps them?
| Rating | Definition |
|---|---|
| Excellent | Feature appears contextually when relevant |
| Good | Feature is in logical location |
| Fair | Feature exists but hidden |
| Poor | Feature hard to find or missing |
Criterion 2: Path Efficiency
How many steps to reach the goal?
| Rating | Steps | Example |
|---|---|---|
| Optimal | 1-2 | Tap → Done |
| Acceptable | 3-4 | Tap → Select → Configure → Done |
| Suboptimal | 5+ | Navigation → Scroll → Search → Tap → Done |
| Broken | ∞ | Cannot complete |
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.
- 2d ago First seen · 241 lines · 0 tokens per session scan A 52f44e69d604
layer4-semantic-evaluation is an agent published in the GitHub repository Terryc21/workflow-audit (58 stars, last pushed 5d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,988 tokens. 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.
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