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# Issue #954 Verification — maps skill guest_house / camp_site / bakery
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Status: PASS
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## Drift noted
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Issue #954 asked for validation on `upstream/main` (commit `c5a814b23`).
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Fresh `forge/main` did not contain `skills/productivity/maps/`, so the forge branch was behind upstream for this feature cluster.
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This branch ports the upstream maps skill files into the forge checkout and adds regression coverage.
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## Automated verification
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Command:
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```bash
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pytest -q tests/skills/test_maps_client.py
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```
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Result:
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- 5 passed
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Coverage added:
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- maps skill files exist in the repo
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- `guest_house` category maps to `tourism=guest_house`
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- `camp_site` category maps to `tourism=camp_site`
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- `bakery` expands to both `shop=bakery` and `amenity=bakery`
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- dual-key bakery results dedupe correctly
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- skill documentation lists the new categories and supersedes `find-nearby`
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## Manual evidence
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### 1) guest_house lookup
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Command:
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```bash
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python3 skills/productivity/maps/scripts/maps_client.py nearby --near "Bath, United Kingdom" --category guest_house --limit 3
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```
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Observed results:
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- Henrietta House — 390.3 m
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- The Windsor — 437.2 m
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- The Old Rectory Bed & Breakfast — 495.7 m
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All returned `tourism=guest_house` in the raw tags.
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### 2) camp_site lookup
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Command:
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```bash
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python3 skills/productivity/maps/scripts/maps_client.py nearby --near "Yosemite Valley, California" --category camp_site --limit 5
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```
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Observed result:
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- Yellow Pine Administrative Campground — 90.3 m
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Returned `tourism=camp_site` in the raw tags.
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### 3) bakery lookup via `shop=bakery`
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Command:
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```bash
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python3 skills/productivity/maps/scripts/maps_client.py nearby --near "Lawrenceville, New Jersey" --category bakery --radius 5000 --limit 10
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```
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Observed results:
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- The Gingered Peach — 713.8 m
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- WildFlour Bakery — 741.9 m
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Both returned `shop=bakery` in the raw tags.
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### 4) bakery lookup via `amenity=bakery`
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Command:
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```bash
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python3 skills/productivity/maps/scripts/maps_client.py nearby --near "20735 Stevens Creek Boulevard, Cupertino, CA" --category bakery --radius 600 --limit 5
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```
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Observed result:
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- Paris Baguette — 28.6 m
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Returned `amenity=bakery` in the raw tags (and also includes `shop=bakery`), proving the dual-key union query reaches amenity-tagged bakeries too.
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## Conclusion
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PASS.
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- `guest_house` resolves correctly
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- `camp_site` resolves correctly
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- `bakery` resolves through both supported keys
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- forge/main drift from upstream/main was real and is addressed on this branch
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190
optional-skills/dogfood/adversarial-ux-test/SKILL.md
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190
optional-skills/dogfood/adversarial-ux-test/SKILL.md
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---
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name: adversarial-ux-test
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description: Roleplay the most difficult, tech-resistant user for your product. Browse the app as that persona, find every UX pain point, then filter complaints through a pragmatism layer to separate real problems from noise. Creates actionable tickets from genuine issues only.
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version: 1.0.0
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author: Omni @ Comelse
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license: MIT
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metadata:
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hermes:
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tags: [qa, ux, testing, adversarial, dogfood, personas, user-testing]
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related_skills: [dogfood]
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---
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# Adversarial UX Test
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Roleplay the worst-case user for your product — the person who hates technology, doesn't want your software, and will find every reason to complain. Then filter their feedback through a pragmatism layer to separate real UX problems from "I hate computers" noise.
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Think of it as an automated "mom test" — but angry.
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## Why This Works
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Most QA finds bugs. This finds **friction**. A technically correct app can still be unusable for real humans. The adversarial persona catches:
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- Confusing terminology that makes sense to developers but not users
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- Too many steps to accomplish basic tasks
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- Missing onboarding or "aha moments"
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- Accessibility issues (font size, contrast, click targets)
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- Cold-start problems (empty states, no demo content)
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- Paywall/signup friction that kills conversion
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The **pragmatism filter** (Phase 3) is what makes this useful instead of just entertaining. Without it, you'd add a "print this page" button to every screen because Grandpa can't figure out PDFs.
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## How to Use
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Tell the agent:
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```
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"Run an adversarial UX test on [URL]"
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"Be a grumpy [persona type] and test [app name]"
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"Do an asshole user test on my staging site"
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```
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You can provide a persona or let the agent generate one based on your product's target audience.
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## Step 1: Define the Persona
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If no persona is provided, generate one by answering:
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1. **Who is the HARDEST user for this product?** (age 50+, non-technical role, decades of experience doing it "the old way")
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2. **What is their tech comfort level?** (the lower the better — WhatsApp-only, paper notebooks, wife set up their email)
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3. **What is the ONE thing they need to accomplish?** (their core job, not your feature list)
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4. **What would make them give up?** (too many clicks, jargon, slow, confusing)
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5. **How do they talk when frustrated?** (blunt, sweary, dismissive, sighing)
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### Good Persona Example
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> **"Big Mick" McAllister** — 58-year-old S&C coach. Uses WhatsApp and that's it. His "spreadsheet" is a paper notebook. "If I can't figure it out in 10 seconds I'm going back to my notebook." Needs to log session results for 25 players. Hates small text, jargon, and passwords.
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### Bad Persona Example
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> "A user who doesn't like the app" — too vague, no constraints, no voice.
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The persona must be **specific enough to stay in character** for 20 minutes of testing.
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## Step 2: Become the Asshole (Browse as the Persona)
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1. Read any available project docs for app context and URLs
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2. **Fully inhabit the persona** — their frustrations, limitations, goals
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3. Navigate to the app using browser tools
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4. **Attempt the persona's ACTUAL TASKS** (not a feature tour):
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- Can they do what they came to do?
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- How many clicks/screens to accomplish it?
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- What confuses them?
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- What makes them angry?
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- Where do they get lost?
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- What would make them give up and go back to their old way?
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5. Test these friction categories:
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- **First impression** — would they even bother past the landing page?
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- **Core workflow** — the ONE thing they need to do most often
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- **Error recovery** — what happens when they do something wrong?
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- **Readability** — text size, contrast, information density
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- **Speed** — does it feel faster than their current method?
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- **Terminology** — any jargon they wouldn't understand?
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- **Navigation** — can they find their way back? do they know where they are?
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6. Take screenshots of every pain point
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7. Check browser console for JS errors on every page
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## Step 3: The Rant (Write Feedback in Character)
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Write the feedback AS THE PERSONA — in their voice, with their frustrations. This is not a bug report. This is a real human venting.
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```
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[PERSONA NAME]'s Review of [PRODUCT]
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Overall: [Would they keep using it? Yes/No/Maybe with conditions]
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THE GOOD (grudging admission):
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- [things even they have to admit work]
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THE BAD (legitimate UX issues):
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- [real problems that would stop them from using the product]
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THE UGLY (showstoppers):
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- [things that would make them uninstall/cancel immediately]
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SPECIFIC COMPLAINTS:
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1. [Page/feature]: "[quote in persona voice]" — [what happened, expected]
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2. ...
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VERDICT: "[one-line persona quote summarizing their experience]"
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```
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## Step 4: The Pragmatism Filter (Critical — Do Not Skip)
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Step OUT of the persona. Evaluate each complaint as a product person:
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- **RED: REAL UX BUG** — Any user would have this problem, not just grumpy ones. Fix it.
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- **YELLOW: VALID BUT LOW PRIORITY** — Real issue but only for extreme users. Note it.
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- **WHITE: PERSONA NOISE** — "I hate computers" talking, not a product problem. Skip it.
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- **GREEN: FEATURE REQUEST** — Good idea hidden in the complaint. Consider it.
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### Filter Criteria
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1. Would a 35-year-old competent-but-busy user have the same complaint? → RED
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2. Is this a genuine accessibility issue (font size, contrast, click targets)? → RED
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3. Is this "I want it to work like paper" resistance to digital? → WHITE
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4. Is this a real workflow inefficiency the persona stumbled on? → YELLOW or RED
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5. Would fixing this add complexity for the 80% who are fine? → WHITE
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6. Does the complaint reveal a missing onboarding moment? → GREEN
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**This filter is MANDATORY.** Never ship raw persona complaints as tickets.
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## Step 5: Create Tickets
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For **RED** and **GREEN** items only:
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- Clear, actionable title
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- Include the persona's verbatim quote (entertaining + memorable)
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- The real UX issue underneath (objective)
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- A suggested fix (actionable)
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- Tag/label: "ux-review"
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For **YELLOW** items: one catch-all ticket with all notes.
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**WHITE** items appear in the report only. No tickets.
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**Max 10 tickets per session** — focus on the worst issues.
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## Step 6: Report
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Deliver:
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1. The persona rant (Step 3) — entertaining and visceral
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2. The filtered assessment (Step 4) — pragmatic and actionable
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3. Tickets created (Step 5) — with links
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4. Screenshots of key issues
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## Tips
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- **One persona per session.** Don't mix perspectives.
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- **Stay in character during Steps 2-3.** Break character only at Step 4.
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- **Test the CORE WORKFLOW first.** Don't get distracted by settings pages.
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- **Empty states are gold.** New user experience reveals the most friction.
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- **The best findings are RED items the persona found accidentally** while trying to do something else.
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- **If the persona has zero complaints, your persona is too tech-savvy.** Make them older, less patient, more set in their ways.
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- **Run this before demos, launches, or after shipping a batch of features.**
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- **Register as a NEW user when possible.** Don't use pre-seeded admin accounts — the cold start experience is where most friction lives.
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- **Zero WHITE items is a signal, not a failure.** If the pragmatism filter finds no noise, your product has real UX problems, not just a grumpy persona.
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- **Check known issues in project docs AFTER the test.** If the persona found a bug that's already in the known issues list, that's actually the most damning finding — it means the team knew about it but never felt the user's pain.
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- **Subscription/paywall testing is critical.** Test with expired accounts, not just active ones. The "what happens when you can't pay" experience reveals whether the product respects users or holds their data hostage.
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- **Count the clicks to accomplish the persona's ONE task.** If it's more than 5, that's almost always a RED finding regardless of persona tech level.
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## Example Personas by Industry
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These are starting points — customize for your specific product:
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| Product Type | Persona | Age | Key Trait |
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|-------------|---------|-----|-----------|
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| CRM | Retirement home director | 68 | Filing cabinet is the current CRM |
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| Photography SaaS | Rural wedding photographer | 62 | Books clients by phone, invoices on paper |
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| AI/ML Tool | Department store buyer | 55 | Burned by 3 failed tech startups |
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| Fitness App | Old-school gym coach | 58 | Paper notebook, thick fingers, bad eyes |
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| Accounting | Family bakery owner | 64 | Shoebox of receipts, hates subscriptions |
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| E-commerce | Market stall vendor | 60 | Cash only, smartphone is for calls |
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| Healthcare | Senior GP | 63 | Dictates notes, nurse handles the computer |
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| Education | Veteran teacher | 57 | Chalk and talk, worksheets in ring binders |
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## Rules
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- Stay in character during Steps 2-3
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- Be genuinely mean but fair — find real problems, not manufactured ones
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- The pragmatism filter (Step 4) is **MANDATORY**
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- Screenshots required for every complaint
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- Max 10 tickets per session
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- Test on staging/deployed app, not local dev
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- One persona, one session, one report
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@@ -1,199 +0,0 @@
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---
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name: maps
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description: >
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Location intelligence — geocode a place, reverse-geocode coordinates,
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find nearby places (46 POI categories), driving/walking/cycling
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distance + time, turn-by-turn directions, timezone lookup, bounding
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box + area for a named place, and POI search within a rectangle.
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Uses OpenStreetMap + Overpass + OSRM. Free, no API key.
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version: 1.2.0
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author: Mibayy
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license: MIT
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metadata:
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hermes:
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tags: [maps, geocoding, places, routing, distance, directions, nearby, location, openstreetmap, nominatim, overpass, osrm]
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category: productivity
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requires_toolsets: [terminal]
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supersedes: [find-nearby]
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---
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# Maps Skill
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Location intelligence using free, open data sources. 8 commands, 44 POI
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categories, zero dependencies (Python stdlib only), no API key required.
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Data sources: OpenStreetMap/Nominatim, Overpass API, OSRM, TimeAPI.io.
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This skill supersedes the old `find-nearby` skill — all of find-nearby's
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functionality is covered by the `nearby` command below, with the same
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`--near "<place>"` shortcut and multi-category support.
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## When to Use
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- User sends a Telegram location pin (latitude/longitude in the message) → `nearby`
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- User wants coordinates for a place name → `search`
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- User has coordinates and wants the address → `reverse`
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- User asks for nearby restaurants, hospitals, pharmacies, hotels, etc. → `nearby`
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- User wants driving/walking/cycling distance or travel time → `distance`
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- User wants turn-by-turn directions between two places → `directions`
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- User wants timezone information for a location → `timezone`
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- User wants to search for POIs within a geographic area → `area` + `bbox`
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## Prerequisites
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Python 3.8+ (stdlib only — no pip installs needed).
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Script path: `~/.hermes/skills/maps/scripts/maps_client.py`
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## Commands
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```bash
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MAPS=~/.hermes/skills/maps/scripts/maps_client.py
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```
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### search — Geocode a place name
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```bash
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python3 $MAPS search "Eiffel Tower"
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python3 $MAPS search "1600 Pennsylvania Ave, Washington DC"
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```
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Returns: lat, lon, display name, type, bounding box, importance score.
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### reverse — Coordinates to address
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```bash
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python3 $MAPS reverse 48.8584 2.2945
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```
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Returns: full address breakdown (street, city, state, country, postcode).
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### nearby — Find places by category
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```bash
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# By coordinates (from a Telegram location pin, for example)
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python3 $MAPS nearby 48.8584 2.2945 restaurant --limit 10
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python3 $MAPS nearby 40.7128 -74.0060 hospital --radius 2000
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# By address / city / zip / landmark — --near auto-geocodes
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python3 $MAPS nearby --near "Times Square, New York" --category cafe
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python3 $MAPS nearby --near "90210" --category pharmacy
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# Multiple categories merged into one query
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python3 $MAPS nearby --near "downtown austin" --category restaurant --category bar --limit 10
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```
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46 categories: restaurant, cafe, bar, hospital, pharmacy, hotel, guest_house,
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camp_site, supermarket, atm, gas_station, parking, museum, park, school,
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university, bank, police, fire_station, library, airport, train_station,
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bus_stop, church, mosque, synagogue, dentist, doctor, cinema, theatre, gym,
|
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swimming_pool, post_office, convenience_store, bakery, bookshop, laundry,
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car_wash, car_rental, bicycle_rental, taxi, veterinary, zoo, playground,
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stadium, nightclub.
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Each result includes: `name`, `address`, `lat`/`lon`, `distance_m`,
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`maps_url` (clickable Google Maps link), `directions_url` (Google Maps
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directions from the search point), and promoted tags when available —
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`cuisine`, `hours` (opening_hours), `phone`, `website`.
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### distance — Travel distance and time
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```bash
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python3 $MAPS distance "Paris" --to "Lyon"
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python3 $MAPS distance "New York" --to "Boston" --mode driving
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python3 $MAPS distance "Big Ben" --to "Tower Bridge" --mode walking
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```
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Modes: driving (default), walking, cycling. Returns road distance, duration,
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and straight-line distance for comparison.
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### directions — Turn-by-turn navigation
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|
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```bash
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python3 $MAPS directions "Eiffel Tower" --to "Louvre Museum" --mode walking
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python3 $MAPS directions "JFK Airport" --to "Times Square" --mode driving
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```
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Returns numbered steps with instruction, distance, duration, road name, and
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maneuver type (turn, depart, arrive, etc.).
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||||
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### timezone — Timezone for coordinates
|
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|
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```bash
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python3 $MAPS timezone 48.8584 2.2945
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python3 $MAPS timezone 35.6762 139.6503
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```
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|
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Returns timezone name, UTC offset, and current local time.
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### area — Bounding box and area for a place
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|
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```bash
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python3 $MAPS area "Manhattan, New York"
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python3 $MAPS area "London"
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```
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Returns bounding box coordinates, width/height in km, and approximate area.
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Useful as input for the bbox command.
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|
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### bbox — Search within a bounding box
|
||||
|
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```bash
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python3 $MAPS bbox 40.75 -74.00 40.77 -73.98 restaurant --limit 20
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```
|
||||
|
||||
Finds POIs within a geographic rectangle. Use `area` first to get the
|
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bounding box coordinates for a named place.
|
||||
|
||||
## Working With Telegram Location Pins
|
||||
|
||||
When a user sends a location pin, the message contains `latitude:` and
|
||||
`longitude:` fields. Extract those and pass them straight to `nearby`:
|
||||
|
||||
```bash
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||||
# User sent a pin at 36.17, -115.14 and asked "find cafes nearby"
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python3 $MAPS nearby 36.17 -115.14 cafe --radius 1500
|
||||
```
|
||||
|
||||
Present results as a numbered list with names, distances, and the
|
||||
`maps_url` field so the user gets a tap-to-open link in chat. For "open
|
||||
now?" questions, check the `hours` field; if missing or unclear, verify
|
||||
with `web_search` since OSM hours are community-maintained and not always
|
||||
current.
|
||||
|
||||
## Workflow Examples
|
||||
|
||||
**"Find Italian restaurants near the Colosseum":**
|
||||
1. `nearby --near "Colosseum Rome" --category restaurant --radius 500`
|
||||
— one command, auto-geocoded
|
||||
|
||||
**"What's near this location pin they sent?":**
|
||||
1. Extract lat/lon from the Telegram message
|
||||
2. `nearby LAT LON cafe --radius 1500`
|
||||
|
||||
**"How do I walk from hotel to conference center?":**
|
||||
1. `directions "Hotel Name" --to "Conference Center" --mode walking`
|
||||
|
||||
**"What restaurants are in downtown Seattle?":**
|
||||
1. `area "Downtown Seattle"` → get bounding box
|
||||
2. `bbox S W N E restaurant --limit 30`
|
||||
|
||||
## Pitfalls
|
||||
|
||||
- Nominatim ToS: max 1 req/s (handled automatically by the script)
|
||||
- `nearby` requires lat/lon OR `--near "<address>"` — one of the two is needed
|
||||
- OSRM routing coverage is best for Europe and North America
|
||||
- Overpass API can be slow during peak hours; the script automatically
|
||||
falls back between mirrors (overpass-api.de → overpass.kumi.systems)
|
||||
- `distance` and `directions` use `--to` flag for the destination (not positional)
|
||||
- If a zip code alone gives ambiguous results globally, include country/state
|
||||
|
||||
## Verification
|
||||
|
||||
```bash
|
||||
python3 ~/.hermes/skills/maps/scripts/maps_client.py search "Statue of Liberty"
|
||||
# Should return lat ~40.689, lon ~-74.044
|
||||
|
||||
python3 ~/.hermes/skills/maps/scripts/maps_client.py nearby --near "Times Square" --category restaurant --limit 3
|
||||
# Should return a list of restaurants within ~500m of Times Square
|
||||
```
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,135 +0,0 @@
|
||||
"""Regression tests for the bundled maps skill."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import importlib.util
|
||||
from pathlib import Path
|
||||
from types import SimpleNamespace
|
||||
|
||||
SCRIPT_PATH = (
|
||||
Path(__file__).resolve().parents[2]
|
||||
/ "skills/productivity/maps/scripts/maps_client.py"
|
||||
)
|
||||
SKILL_PATH = (
|
||||
Path(__file__).resolve().parents[2]
|
||||
/ "skills/productivity/maps/SKILL.md"
|
||||
)
|
||||
|
||||
|
||||
def load_module():
|
||||
assert SCRIPT_PATH.exists(), f"missing maps client script: {SCRIPT_PATH}"
|
||||
spec = importlib.util.spec_from_file_location("maps_client_test", SCRIPT_PATH)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
assert spec.loader is not None
|
||||
spec.loader.exec_module(module)
|
||||
return module
|
||||
|
||||
|
||||
def test_maps_skill_files_exist():
|
||||
assert SCRIPT_PATH.exists()
|
||||
assert SKILL_PATH.exists()
|
||||
|
||||
|
||||
def test_category_tags_cover_guest_house_camp_site_and_dual_key_bakery():
|
||||
module = load_module()
|
||||
|
||||
assert module.CATEGORY_TAGS["guest_house"] == ("tourism", "guest_house")
|
||||
assert module.CATEGORY_TAGS["camp_site"] == ("tourism", "camp_site")
|
||||
assert module.CATEGORY_TAGS["bakery"] == [
|
||||
("shop", "bakery"),
|
||||
("amenity", "bakery"),
|
||||
]
|
||||
assert module._tags_for("bakery") == [
|
||||
("shop", "bakery"),
|
||||
("amenity", "bakery"),
|
||||
]
|
||||
|
||||
|
||||
def test_build_overpass_queries_include_all_supported_tags():
|
||||
module = load_module()
|
||||
|
||||
bakery_query = module.build_overpass_nearby(
|
||||
None,
|
||||
None,
|
||||
40.0,
|
||||
-74.0,
|
||||
500,
|
||||
10,
|
||||
tag_pairs=module._tags_for("bakery"),
|
||||
)
|
||||
assert 'node["shop"="bakery"]' in bakery_query
|
||||
assert 'way["shop"="bakery"]' in bakery_query
|
||||
assert 'node["amenity"="bakery"]' in bakery_query
|
||||
assert 'way["amenity"="bakery"]' in bakery_query
|
||||
|
||||
guest_house_query = module.build_overpass_nearby(
|
||||
None,
|
||||
None,
|
||||
40.0,
|
||||
-74.0,
|
||||
500,
|
||||
10,
|
||||
tag_pairs=module._tags_for("guest_house"),
|
||||
)
|
||||
assert 'node["tourism"="guest_house"]' in guest_house_query
|
||||
assert 'way["tourism"="guest_house"]' in guest_house_query
|
||||
|
||||
camp_site_bbox = module.build_overpass_bbox(
|
||||
None,
|
||||
None,
|
||||
39.0,
|
||||
-75.0,
|
||||
41.0,
|
||||
-73.0,
|
||||
10,
|
||||
tag_pairs=module._tags_for("camp_site"),
|
||||
)
|
||||
assert 'node["tourism"="camp_site"]' in camp_site_bbox
|
||||
assert 'way["tourism"="camp_site"]' in camp_site_bbox
|
||||
|
||||
|
||||
def test_cmd_nearby_dedupes_dual_tag_bakery_results(monkeypatch, capsys):
|
||||
module = load_module()
|
||||
|
||||
duplicate_bakery = {
|
||||
"elements": [
|
||||
{
|
||||
"type": "node",
|
||||
"id": 101,
|
||||
"lat": 40.0,
|
||||
"lon": -74.0,
|
||||
"tags": {"name": "Wild Flour", "shop": "bakery"},
|
||||
},
|
||||
{
|
||||
"type": "node",
|
||||
"id": 101,
|
||||
"lat": 40.0,
|
||||
"lon": -74.0,
|
||||
"tags": {"name": "Wild Flour", "amenity": "bakery"},
|
||||
},
|
||||
]
|
||||
}
|
||||
|
||||
monkeypatch.setattr(module, "overpass_query", lambda query: duplicate_bakery)
|
||||
args = SimpleNamespace(
|
||||
lat="40.0",
|
||||
lon="-74.0",
|
||||
near=None,
|
||||
category="bakery",
|
||||
category_list=[],
|
||||
radius=500,
|
||||
limit=10,
|
||||
)
|
||||
|
||||
module.cmd_nearby(args)
|
||||
out = capsys.readouterr().out
|
||||
assert '"count": 1' in out
|
||||
assert '"Wild Flour"' in out
|
||||
|
||||
|
||||
def test_skill_doc_lists_new_categories_and_supersession():
|
||||
text = SKILL_PATH.read_text(encoding="utf-8")
|
||||
assert "guest_house" in text
|
||||
assert "camp_site" in text
|
||||
assert "bakery" in text
|
||||
assert "supersedes: [find-nearby]" in text
|
||||
25
tests/test_optional_adversarial_ux_skill_catalog.py
Normal file
25
tests/test_optional_adversarial_ux_skill_catalog.py
Normal file
@@ -0,0 +1,25 @@
|
||||
from pathlib import Path
|
||||
|
||||
from tools.skills_hub import OptionalSkillSource
|
||||
|
||||
|
||||
REPO_ROOT = Path(__file__).resolve().parents[1]
|
||||
|
||||
|
||||
def test_optional_skill_source_scans_adversarial_ux_test():
|
||||
source = OptionalSkillSource()
|
||||
metas = {meta.identifier: meta for meta in source._scan_all()}
|
||||
|
||||
assert "official/dogfood/adversarial-ux-test" in metas
|
||||
assert metas["official/dogfood/adversarial-ux-test"].name == "adversarial-ux-test"
|
||||
assert "tech-resistant user" in metas["official/dogfood/adversarial-ux-test"].description
|
||||
|
||||
|
||||
def test_optional_skill_catalog_docs_list_adversarial_ux_test():
|
||||
optional_catalog = (REPO_ROOT / "website" / "docs" / "reference" / "optional-skills-catalog.md").read_text(encoding="utf-8")
|
||||
bundled_catalog = (REPO_ROOT / "website" / "docs" / "reference" / "skills-catalog.md").read_text(encoding="utf-8")
|
||||
|
||||
assert "**adversarial-ux-test**" in optional_catalog
|
||||
assert "official/dogfood/adversarial-ux-test" in optional_catalog
|
||||
assert "`adversarial-ux-test`" in bundled_catalog
|
||||
assert "dogfood/adversarial-ux-test" in bundled_catalog
|
||||
@@ -16,6 +16,7 @@ For example:
|
||||
|
||||
```bash
|
||||
hermes skills install official/blockchain/solana
|
||||
hermes skills install official/dogfood/adversarial-ux-test
|
||||
hermes skills install official/mlops/flash-attention
|
||||
```
|
||||
|
||||
@@ -56,6 +57,12 @@ hermes skills uninstall <skill-name>
|
||||
| **blender-mcp** | Control Blender directly from Hermes via socket connection to the blender-mcp addon. Create 3D objects, materials, animations, and run arbitrary Blender Python (bpy) code. |
|
||||
| **meme-generation** | Generate real meme images by picking a template and overlaying text with Pillow. Produces actual `.png` meme files. |
|
||||
|
||||
## Dogfood
|
||||
|
||||
| Skill | Description |
|
||||
|-------|-------------|
|
||||
| **adversarial-ux-test** | Roleplay the most difficult, tech-resistant user for a product — browse in-persona, rant, then filter through a RED/YELLOW/WHITE/GREEN pragmatism layer so only real UX friction becomes tickets. |
|
||||
|
||||
## DevOps
|
||||
|
||||
| Skill | Description |
|
||||
|
||||
@@ -59,9 +59,12 @@ DevOps and infrastructure automation skills.
|
||||
|
||||
## dogfood
|
||||
|
||||
Internal dogfooding and QA skills used to test Hermes Agent itself.
|
||||
|
||||
| Skill | Description | Path |
|
||||
|-------|-------------|------|
|
||||
| `dogfood` | Systematic exploratory QA testing of web applications — find bugs, capture evidence, and generate structured reports. | `dogfood/dogfood` |
|
||||
| `adversarial-ux-test` | Roleplay the most difficult, tech-resistant user for a product — browse in-persona, rant, then filter through a RED/YELLOW/WHITE/GREEN pragmatism layer so only real UX friction becomes tickets. | `dogfood/adversarial-ux-test` |
|
||||
| `hermes-agent-setup` | Help users configure Hermes Agent — CLI usage, setup wizard, model/provider selection, tools, skills, voice/STT/TTS, gateway, and troubleshooting. | `dogfood/hermes-agent-setup` |
|
||||
|
||||
## email
|
||||
|
||||
Reference in New Issue
Block a user