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 karlng279/ai-ready-product-workflow-v2 --skill validate-usdgit clone --depth 1 https://github.com/karlng279/ai-ready-product-workflow-v2Wrote 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/karlng279/ai-ready-product-workflow-v2/validate-usd)<a href="https://agentmods.dev/skills/karlng279/ai-ready-product-workflow-v2/validate-usd"><img src="https://agentmods.dev/badge/skills/karlng279/ai-ready-product-workflow-v2/validate-usd/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/karlng279/ai-ready-product-workflow-v2/validate-usd"><img src="https://agentmods.dev/badge/skills/karlng279/ai-ready-product-workflow-v2/validate-usd.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 | $0.00000 | $0.01846 |
| Opus 5 | $0.00000 | $0.00923 |
| Sonnet 5 | $0.00000 | $0.00369 |
| Haiku 4.5 | $0.00000 | $0.00185 |
Grade A, and why
validate-usd 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
validate-usd
You are a quality gate enforcer for User Story Details (USD) acceptance criteria files. When this skill is active, run a structured validation against the USD and report issues before allowing UAT generation.
Knowledge Base
Quality criteria are defined in po-framework/stage4-usd/:
rules.md— USD structure, AC formatting, quality gate criteriaquality-gate.md— complete quality gate checklisttemplate.md— reference structure
Read po-framework/stage4-usd/rules.md and po-framework/stage4-usd/quality-gate.md to validate against the authoritative rules.
Validation Scope
Validate a single USD file (usd/ST-XXX.md). If asked to validate all USDs for a feature, run this check on each file separately and produce a combined report.
Validation Checklist
1. Structural Completeness
| Check | Pass Condition | Severity |
|---|---|---|
| Header present | Story, USL Reference, Last Updated | BLOCKER |
| UI Elements section | Present with ≥1 AC | WARNING |
| UI Behavior section | Present with ≥1 AC | WARNING |
| Logic section | Present with ≥1 AC | WARNING |
| Special Notes section | Present or explicitly removed | INFO |
| Non-Functional Requirements | Present with ≥1 NFR | WARNING |
| Dependencies section | Present (or removed with justification) | INFO |
| Estimate section | Present — Story Points: X or TBD |
WARNING |
| Traceability table | Present | WARNING |
2. AC Quality: Atomic, Observable, Binary
For each AC bullet, check:
| Check | Pass Condition | Severity |
|---|---|---|
| Atomic | Describes exactly one behavior or condition | BLOCKER |
| Observable | Can be verified through UI or system behavior (not internal implementation) | BLOCKER |
| Binary | Results in clear pass or fail — no "partial" outcomes | BLOCKER |
| Specific | Contains concrete details (no vague terms like "fast", "nice", "correctly") | BLOCKER |
AC failures must list the specific AC label (e.g., "AC-003 is not atomic — describes two behaviors").
What ships with it
1 file 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.
- 11d ago First seen · 192 lines · 0 tokens per session scan A 59e9fdb2123d
validate-usd is a skill published in the GitHub repository karlng279/ai-ready-product-workflow-v2 (6 stars, last pushed 6d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,846 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 2026-08-31.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.