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 jabrena/plinth --skill 025-quality-attribute-discoverygit clone --depth 1 https://github.com/jabrena/plinthWrote 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/jabrena/plinth/025-quality-attribute-discovery)<a href="https://agentmods.dev/skills/jabrena/plinth/025-quality-attribute-discovery"><img src="https://agentmods.dev/badge/skills/jabrena/plinth/025-quality-attribute-discovery/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/jabrena/plinth/025-quality-attribute-discovery"><img src="https://agentmods.dev/badge/skills/jabrena/plinth/025-quality-attribute-discovery.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 | $0.00078 | $0.00709 |
| Opus 5 | $0.00039 | $0.00354 |
| Sonnet 5 | $0.00016 | $0.00142 |
| Haiku 4.5 | $0.00008 | $0.00071 |
Grade A, and why
025-quality-attribute-discovery 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 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.
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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quality Attribute Discovery
Guide identification and prioritization of the quality attributes a future solution must satisfy, before architecture and design decisions begin. This is an interactive SKILL.
What is covered in this Skill?
- Identifying candidate quality attributes (for example performance, security, availability, maintainability, scalability, usability, observability) relevant to the problem
- Grounding each candidate in evidence from the problem frame, root causes, assumptions, and context map, not a generic checklist
- Prioritizing candidate quality attributes by stakeholder impact and risk
- Stopping at a prioritized discovery list rather than selecting or recording an architectural decision
- Explicitly not producing ADRs or an architecture direction itself
Constraints
Discover and prioritize candidate quality attributes as input to later architecture work; do not make or record the architecture decision here. When this technique is orchestrated by another workflow, the orchestrator owns clarifying-question sequencing; when applied standalone, ask directly.
- MUST read
references/025-quality-attribute-discovery.mdbefore applying Quality Attribute Discovery guidance - MUST ground each candidate quality attribute in evidence from the problem frame, root causes, assumptions, or context map
- MUST prioritize candidate quality attributes by stakeholder impact and risk, not list them unordered
- MUST stop at a prioritized discovery list without selecting or recording an architecture decision
- MUST NOT record an architectural decision, ADR, or design direction as part of this skill's output
- MUST NOT invent a quality attribute or priority when the available content is vague or ambiguous; flag the gap for a clarifying question instead
When to use this skill
- Discover the quality attributes for this problem
- Identify non-functional requirements before design begins
- Prioritize candidate quality attributes for this issue
- Apply quality attribute discovery before architecture decisions
- Draft the Quality Attribute Discovery section of a Functional Specification
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.
- 10d ago First seen · 65 lines · 78 tokens per session scan A 4883cdb35bc7
025-quality-attribute-discovery is a skill published in the GitHub repository jabrena/plinth (437 stars, last pushed yesterday), licensed Apache-2.0. It adds 78 tokens to every session and 709 once invoked, about $0.0004 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 2026-08-30.
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