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/designpowers --skill design-retrospectivegit clone --depth 1 https://github.com/Owl-Listener/designpowersWrote 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/designpowers/design-retrospective)<a href="https://agentmods.dev/skills/owl-listener/designpowers/design-retrospective"><img src="https://agentmods.dev/badge/skills/owl-listener/designpowers/design-retrospective/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/designpowers/design-retrospective"><img src="https://agentmods.dev/badge/skills/owl-listener/designpowers/design-retrospective.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 | $0.00055 | $0.02148 |
| Opus 5 | $0.00028 | $0.01074 |
| Sonnet 5 | $0.00011 | $0.00430 |
| Haiku 4.5 | $0.00006 | $0.00215 |
Grade A, and why
design-retrospective 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 — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design Retrospective
A retrospective is not a post-mortem. Post-mortems examine failures. Retrospectives examine the whole process — wins, misses, surprises, and taste evolution. This skill runs after a project ships and turns hindsight into foresight for the next one.
When to Use
- After
verification-before-shippingpasses and the project is declared complete - When the user says "let's reflect" or "what did we learn?"
- At natural project milestones (end of a major phase, end of a sprint)
- When restarting work on a project after a break — retrospect on what came before
Process
Step 1: Gather Evidence
Before reflecting, assemble the full record:
- Read
design-state.md— the decisions log, handoff chain, and open questions - Read the taste profile — what was known at the start vs now
- Review user overrides — every correction, redirect, and override from the handoff chain
- Review critique findings — what the design-critic and accessibility-reviewer flagged
- Review fix rounds — how many, what was fixed, what kept coming back
- Check the original brief — compare what was asked for vs what was delivered
Step 2: Evaluate What Worked
For each major design decision that survived to shipping:
### What Worked
| Decision | Why It Worked | Evidence |
|----------|--------------|----------|
| [Decision from state log] | [Why this was the right call] | [User approved, critic passed, no fix rounds needed] |
| ... | ... | ... |
Look for:
- Decisions that sailed through critique without issues
- Choices the user explicitly praised
- Patterns that emerged naturally and felt right
- Accessibility approaches that enhanced rather than constrained the design
- Moments that revealed something characteristic about how the user designs (to note in the record — not to apply later)
Step 3: Evaluate What Didn't Work
For decisions that required rework, debate, or user correction:
### What Didn't Work
| Decision | What Went Wrong | Root Cause | Fix Rounds |
|----------|----------------|------------|------------|
| [Original decision] | [What happened] | [Why it happened — misread brief? ignored taste? wrong assumption?] | [How many iterations to fix] |
| ... | ... | ... | ... |
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 · 267 lines · 55 tokens per session scan A e05d4a39cfde
design-retrospective is a skill published in the GitHub repository Owl-Listener/designpowers (245 stars, last pushed 2mo ago), licensed MIT. It adds 55 tokens to every session and 2,148 once invoked, about $0.0003 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
accessibility-a11y
Semantic HTML, keyboard navigation, focus states, ARIA labels, skip links, and WCAG contrast requirements. Use when ensuring accessibility compliance, implementing keyboard navigation, or adding screen reader support.
tailwind-shadcn
Tailwind CSS utility patterns with shadcn/ui component usage, theming via CSS variables, and responsive design. Use when styling components, installing shadcn components, implementing dark mode, or creating consistent design systems.
anti-slop-frontend
A mechanical, countable anti-slop checklist for AI-generated frontend. Catches the specific signatures an undirected model defaults to: AI-purple glows, Inter-everywhere, em-dashes, div-based fake screenshots, eyebrow-on-every-section, beige+brass "premium" palettes, generic Jane Doe / Acme data. Advisory layer that…
frontend-mockup-loop
Plan, build, and iterate UX-friendly frontend mockups via a ground→contract→mockup→test→fix→learn loop, acting as an expert UX designer who grounds every decision in externally documented public design rules (Nielsen heuristics, Laws of UX, WCAG, GOV.UK/USWDS/Material). Uses the bundled servemockup, scoremockup, and…
frontend-mockup-loop-dashboard
Dashboard-specific adapter on the generic frontend-mockup-loop skill: binds the 7-step design loop to pi-agent-dashboard component sources, theme-system tokens, and isolated verification. Use when designing/redesigning any pi-agent-dashboard client surface. Triggers: "design a dashboard screen", "mockup a dashboard…
goga-define-experience
Define the user experience required to achieve the established product goals and solve the identified problem.