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 Dragoon0x/taste-skills --skill constraint-evaluationgit clone --depth 1 https://github.com/Dragoon0x/taste-skillsWrote 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/dragoon0x/taste-skills/constraint-evaluation)<a href="https://agentmods.dev/skills/dragoon0x/taste-skills/constraint-evaluation"><img src="https://agentmods.dev/badge/skills/dragoon0x/taste-skills/constraint-evaluation/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/dragoon0x/taste-skills/constraint-evaluation"><img src="https://agentmods.dev/badge/skills/dragoon0x/taste-skills/constraint-evaluation.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 | $0.00043 | $0.00287 |
| Opus 5 | $0.00022 | $0.00143 |
| Sonnet 5 | $0.00009 | $0.00057 |
| Haiku 4.5 | $0.00004 | $0.00029 |
Grade A, and why
constraint-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 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.
What it actually says
Constraint Evaluation
Judge quality within context, not in a vacuum.
How to use
/constraint-evaluationApply constraint-aware evaluation to this conversation.
Constraints
Identify the Constraints
- MUST attempt to identify: team size, timeline, technology, budget, business model, and target audience
- MUST evaluate quality relative to constraints, not against an ideal with unlimited resources
- A strong design under tight constraints demonstrates more taste than a mediocre design with unlimited time
- SHOULD note where constraints were handled gracefully and where they caused visible damage
Essential vs. Incidental Quality
- MUST distinguish between decisions that are core to the experience and decisions that are execution details
- Core decisions (hierarchy, information architecture, primary flow) should hold up regardless of constraints
- Execution details (animation polish, micro-interactions, state coverage) scale with available resources
- NEVER excuse poor core decisions by pointing to tight constraints
Anti-Patterns
- Judging a startup MVP against enterprise product standards
- Ignoring constraints entirely ("this should have better animation" for a 2-week project)
- Using constraints as an excuse for fundamentally broken hierarchy or flow
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 · 32 lines · 43 tokens per session scan A 3ce91f03353c
constraint-evaluation is a skill published in the GitHub repository Dragoon0x/taste-skills (4 stars, last pushed 6mo ago), licensed MIT. It adds 43 tokens to every session and 287 once invoked, about $0.0002 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.
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