Owl-Listener/designer-skills is a collection of AI-agent skills, commands, and plugins for design work, covering research, design systems, interfaces, interaction, and delivery. Designers and developers use it inside coding assistants to guide design tasks, and the catalogue entries represent selected parts of that larger collection.
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 Owl-Listener/designer-skills --skill research-repositorygit clone --depth 1 https://github.com/Owl-Listener/designer-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/owl-listener/designer-skills/research-repository)<a href="https://agentmods.dev/skills/owl-listener/designer-skills/research-repository"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/research-repository/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/owl-listener/designer-skills/research-repository"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/research-repository.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00043 | $0.01156 |
| Opus 5 | $0.00022 | $0.00578 |
| Sonnet 5 | $0.00009 | $0.00231 |
| Haiku 4.5 | $0.00004 | $0.00116 |
Grade A, and why
research-repository 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 12d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Repository
You are an expert in organizing research so it compounds in value rather than disappearing into shared drives.
What You Do
You design and maintain the systems, tagging conventions, and rituals that keep research findable and used — so teams don't repeat studies, can build on prior work, and can make decisions backed by accumulated evidence.
Why Repositories Fail
Most research is conducted well and then effectively lost. Common failure modes:
- Findings live in project folders organized by team, not by topic — no one knows what exists
- Reports are long and unstructured — hard to find a specific insight in a 40-page deck
- Tagging is inconsistent or absent — search doesn't work
- Repository exists but no one adds to it — no maintenance culture
- Insights and raw data are mixed — teams can't tell what's an observation and what's a conclusion
Repository Architecture
Three Layers
- Insights: discrete, standalone findings ("Users don't understand the difference between X and Y") — the most reusable unit
- Studies: the research projects that produced insights (interview series, usability test, survey) — provides context for evaluating insight validity
- Raw data: transcripts, recordings, survey exports — the evidence behind insights; not the primary search target Design the repository so insights are the primary entry point — not studies, not raw data.
Insight Structure
Each insight should have:
- Statement: one clear sentence (past tense, specific)
- Confidence: High (multiple studies, large sample) / Medium (single study, validated) / Low (one session, early signal)
- Method: how it was gathered (interview, usability test, survey, analytics)
- Date: when gathered
- Sample: who (segment, n)
- Tags: topic, feature area, user segment, sentiment
- Source links: back to the study and raw data
- Related insights: manually or automatically linked
Tagging System
The tagging system is the most critical design decision in a repository. Define tags before populating:
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.
- 12d ago First seen · 81 lines · 43 tokens per session scan A 340e7ac3d5c2
research-repository is a skill published in the GitHub repository Owl-Listener/designer-skills (2,619 stars, last pushed 6d ago), licensed MIT. It adds 43 tokens to every session and 1,156 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 2026-08-30.
Other skills, from other repositories
ui-review
Review UI code for StyleSeed design-system compliance, accessibility, mobile ergonomics, spacing discipline, and implementation quality.
writing-workflow-skills
Use when adding a new workflow skill to pi-thinkrail-workflow, changing an existing workflow skill's role, trigger, handoff, or structure, or checking a workflow skill against the workflow system's rules. Not for authoring general-purpose skills outside this package.
node-inspect-debugger
See runtime state a console.log cannot reach — set real breakpoints, step, and dump the scope chain of a paused Node/TypeScript process. Use on "set a breakpoint", "inspect runtime state", "console.log isn't enough", "step through this", "what's in this closure at runtime", "attach a debugger". Not a logging or…
systematic-debugging
Root-cause a bug already in front of you, instead of guessing at fixes. Use on triggers like "root cause this", "why is this failing", "debug systematically", "this test is flaky", "it works locally but not in CI", or when a fix attempt has already failed once. Enforces a phased evidence-first process before any code…
code-quality
Drive static-analysis code quality in pi-agent-dashboard with Biome (analyze → fix → test), in changed-files or whole-repo mode. Use when asked to "improve code quality", "lint and fix", "clean up warnings", "fix Biome issues", "run static analysis", or when setting a code-quality goal. Skip for one-line edits.
autonomous-run
Prepare, start, inspect, resume, or stop a finite local overnight coding run after a human has accepted a Wayfinder terminal spec; coordinates a declared Claude/Codex maker and independent checker without pushing, merging, or writing to external systems.