36N4MS

The delivered stills demonstrate a visually strong, responsive widget, a Fahrenheit state, and a polite unknown-city state. The completion note clearly names Open-Meteo and describes live geocoding/forecast behavior. However, no live-search screencast was delivered, so the required typed Berlin-to-live-card interaction and motion-based proof could not be visually verified.

The delivered stills demonstrate a visually strong, responsive widget, a Fahrenheit state, and a polite unknown-city state. The completion note clearly names Open-Meteo and describes live geocoding/forecast behavior. However, no live-search screencast was delivered, so the required typed Berlin-to-live-card interaction and motion-based proof could not be visually verified.

The delivered stills demonstrate a visually strong, responsive widget, a Fahrenheit state, and a polite unknown-city state. The completion note clearly names Open-Meteo and describes live geocoding/forecast behavior. However, no live-search screencast was delivered, so the required typed Berlin-to-live-card interaction and motion-based proof could not be visually verified.

Visually this is a strong, coherent weather card with good hierarchy and responsive card controls. The main concern is correctness visible in the forecast capture: it appears to show four weekday/value columns and values inconsistent with the required three-day Rome dataset, despite the committed source containing the correct FORECAST table. A dedicated mobile forecast capture and a corrected forecast rendering would make the result substantially stronger.

Visually this is a strong, coherent weather card with good hierarchy and responsive card controls. The main concern is correctness visible in the forecast capture: it appears to show four weekday/value columns and values inconsistent with the required three-day Rome dataset, despite the committed source containing the correct FORECAST table. A dedicated mobile forecast capture and a corrected forecast rendering would make the result substantially stronger.

Visually this is a strong, coherent weather card with good hierarchy and responsive card controls. The main concern is correctness visible in the forecast capture: it appears to show four weekday/value columns and values inconsistent with the required three-day Rome dataset, despite the committed source containing the correct FORECAST table. A dedicated mobile forecast capture and a corrected forecast rendering would make the result substantially stronger.

A thoughtful extra mile: live attribution, caching, forecast view, responsive presentation, and forgiving failure states make this feel more usable than the baseline. The forecast and live-search behavior are present in code, but the submitted visual evidence does not prove typing Berlin and receiving live data in motion.

Strong live Open-Meteo implementation with coherent conditions, units, quick picks, and a usable search flow. The main remaining weakness is evidence coverage: the provided screencast shows Berlin and unit switching but not the required unknown-city interaction.

A strong extra-mile weather widget: the forecast drill-down, shareable URL state, caching, loading/error handling, and unit conversion make it feel like a small finished product rather than only a live-data demo.

Data hygiene is exemplary. Every displayed value (temperature, condition, wind, 3-day forecast) flows from the Open-Meteo geocoding/forecast APIs through small, clearly separated fetch functions into typed dataclasses, and the render helpers never touch parsing or raw values. The built-in weather tables are gone — only city coordinates and the WMO code-to-language mapping remain, which are location/translation data, not weather readings. Units are requested from the service itself, so the °C/°F label always matches the value, and both the unknown-city and service-failure paths degrade honestly without inventing numbers. The completion note correctly names Open-Meteo as the chosen service.

The live widget is a well-structured single-module design: live data access, WMO-code mapping, place resolution, and HTML rendering are cleanly separated with one-way dependencies, and the built-in weather tables were removed entirely (quick picks keep only coordinates, so every number is fetched live from Open-Meteo). A deterministic probe confirmed the components import and wire together (place resolution -> card/error rendering, units normalization, human-language WMO labels) and that render functions perform no I/O. Structure is genuinely understandable from the files, so no architecture note was requested. Note for the correctness track: the task contract requires a video/webm screencast of the typed-city flow — no webm for this task appears in the artifacts yet, only stills and the forecast task's webm.

Clean, focused implementation: Open-Meteo geocoding + forecast API wired into a single weather.py with no leftover built-in tables, a well-isolated TTL-cached network layer, friendly error states for unknown cities and service outages, and escaping/quoting on all user-controlled values. The done note names the service as required. Only cosmetic improvements remain (name the cache-TTL constants), so both criteria score high.

Clean, focused implementation: Open-Meteo geocoding + forecast API wired into a single weather.py with no leftover built-in tables, a well-isolated TTL-cached network layer, friendly error states for unknown cities and service outages, and escaping/quoting on all user-controlled values. The done note names the service as required. Only cosmetic improvements remain (name the cache-TTL constants), so both criteria score high.

The widget work itself is solidly documented (done.md names Open-Meteo, describes live geocoding/forecast, units, unknown-city handling), and weather.sh gives an accurate one-line run command. But the agentic story is only half-there: AGENTS.md documents the legacy Q&A participant and never mentions the weather widget, the es_* smoke-test/setup automation is evident only from leftover logs (not committed), and there are no hooks or automated checks. A few lines in AGENTS.md pointing at weather.sh + a committed smoke-test script would have closed the gap.

The widget implementation is solid — real Open-Meteo integration with geocoding, live quick picks, unit-correct fetch, WMO→human condition mapping, and a polite unknown-city page, all documented and demonstrated via the screencast. However, the tests criterion is effectively absent: there is not a single automated test (no unit tests for geocode/fetch_current/render_* logic, no mocked-network tests, no end-to-end test hitting the served pages). Verification rests solely on the manual screencast/screenshots. For a task this size, even a few pytest cases around render()/normalize_units()/condition_label() with a stubbed HTTP layer would have earned meaningful credit.

Strong, polished implementation with complete city switching and forecast rendering in code. The main evidence gap is that the delivered screencast does not visibly establish the full forecast contents or Rome Day 2, so the product earns high but not near-perfect credit.

The deliverable itself is clean and proportionate (city switcher pills, forecast view, back link, Rome Day 2 = 26 cloudy all backed by a dedicated FORECAST table), but the agentic scaffolding is thin for this task: AGENTS.md describes a different product (the answer participant), CLAUDE.md is empty, and there are no tests, hooks, or verify/capture automation — only a solid weather.sh launcher and self-documenting done-notes. An agent inheriting this repo would be misdirected by AGENTS.md and would have to reverse-engineer the widget from weather.py and the done-notes. A one-line AGENTS.md update pointing at weather.sh plus a smoke-test/curl-check hook would have raised this substantially.

A polished, focused weather widget with convincing live-search motion evidence, quick-pick/live-service implementation, unit conversion, forecast access, and polite unknown-city handling. The visual design is strong and coherent; improving muted-text contrast and adding explicit accessible names/state semantics to unit and quick-pick controls would make it more robust.

A polished, focused weather widget with convincing live-search motion evidence, quick-pick/live-service implementation, unit conversion, forecast access, and polite unknown-city handling. The visual design is strong and coherent; improving muted-text contrast and adding explicit accessible names/state semantics to unit and quick-pick controls would make it more robust.

A polished, focused weather widget with convincing live-search motion evidence, quick-pick/live-service implementation, unit conversion, forecast access, and polite unknown-city handling. The visual design is strong and coherent; improving muted-text contrast and adding explicit accessible names/state semantics to unit and quick-pick controls would make it more robust.

Well-organized single-file widget: data constants, pure helpers, render functions, and a thin HTTP handler are cleanly separated with one-way dependencies and a proportionate size for the task. The forecast now reads exclusively from the pinned FORECAST dataset with an independent render_forecast, city switching pills and a back control are consistently wired through a central link() builder. Minor polish items: dense f-string HTML, an empty class attribute on inactive unit links, and committed pycache files — none affect the architecture's soundness.

The widget implementation (weather.py) is clean and well-structured, but the submission ships zero automated tests. There is no unit, integration, or E2E test anywhere in the repo; the only verification is manual (running the server, PNG screenshots from the earlier task, and the screencast required by the contract). The pure functions (display_temp, to_fahrenheit, normalize_units, link, city_switcher, render_forecast) are ideal candidates for unit tests, and the HTTP handler could be integration-tested for /?city=rome&view=forecast asserting day 2 reads 26 and cloudy, plus the unknown-city 404 path. As-is, nothing in the repo verifies the switching/forecast flows programmatically.

The widget satisfies the flows: every card and forecast page renders a Switch city pill row that jumps between all four cities without touching the address bar, each card has a 'View 3-day forecast' link that opens all three days (day, temp, condition) matching the dataset, and 'Back to ' returns to the card. Code is clean, well-named, and easy to change. Minor polish items: commit the pycache binaries out of the repo, name the 'forecast'/'f' sentinels, and escape user input in the unknown-city message.

The widget satisfies the flows: every card and forecast page renders a Switch city pill row that jumps between all four cities without touching the address bar, each card has a 'View 3-day forecast' link that opens all three days (day, temp, condition) matching the dataset, and 'Back to ' returns to the card. Code is clean, well-named, and easy to change. Minor polish items: commit the pycache binaries out of the repo, name the 'forecast'/'f' sentinels, and escape user input in the unknown-city message.

Clean data architecture. The forecast table is declared once as the FORECAST constant in weather.py, matching the task's pinned dataset exactly (including Rome Day 2 = 26 cloudy), and every rendered forecast row, temperature, and condition is interpolated from it — no duplicate values, no hardcoded markup, no leftover mock data (the previous fabricated forecast_row was removed). Rendering and unit conversion are separated into small functions with a clear interface, and the city switcher/forecast links preserve units and view so switching and back-navigation trace cleanly to the same data source.

The live conversion is well structured: the static WEATHER/FORECAST tables were replaced by two clean data-access functions hitting Open-Meteo (geocoding + forecast), pure WMO-code mapping, and a URL-state SPA with cache and a stale-fetch guard. Search, quick picks, unknown-city handling, and unit toggling all route through the same load() pipeline. Main improvement: distinguish 'city not found' from 'service unavailable' instead of rendering both as 'could not find', and consider escaping interpolated city names in the HTML strings.

Clean live-data build: static tables are gone, Open-Meteo powers search and current/forecast data via geocoding + forecast APIs, quick picks keep preset coordinates only, unknown cities degrade politely, and the .ololo/weather-widget-live-done.md note names the service. Code is readable and well-factored for a single-file widget. Small improvements: drop the unused city param in renderForecastView, distinguish "city not found" from "service unavailable" (a failed fetch is currently memoized and shown as unknown city for the session), name the 3-day horizon constant, and refresh AGENTS.md which still describes the static four-city widget.

Clean live-data build: static tables are gone, Open-Meteo powers search and current/forecast data via geocoding + forecast APIs, quick picks keep preset coordinates only, unknown cities degrade politely, and the .ololo/weather-widget-live-done.md note names the service. Code is readable and well-factored for a single-file widget. Small improvements: drop the unused city param in renderForecastView, distinguish "city not found" from "service unavailable" (a failed fetch is currently memoized and shown as unknown city for the session), name the 3-day horizon constant, and refresh AGENTS.md which still describes the static four-city widget.

Data handling is exemplary: the widget went live on Open-Meteo (Geocoding + Forecast APIs), the built-in weather tables were fully deleted rather than kept as shadow data, and every displayed value — temperature, condition, wind, forecast — traces to a live API response. Quick picks keep only coordinates, which is a legitimate lookup shortcut, not baked-in weather data. Fetching and shaping are cleanly isolated behind geocodeCity/fetchWeather, and the completion note honestly names the service and the verification performed. Small nits only: single-file inline script (clear interfaces but no module boundary), service-unavailable errors display as 'unknown city', and WMO code 85/86 falls back to 'cloudy'. None affect the source-of-truth or no-shadow-data guarantees.

The live-weather conversion is well done: Open-Meteo geocoding + forecast API, quick picks keep coordinates but every value comes from live calls, unknown cities fail politely, and units follow the toggle — verified via stills/webm artifacts. However, the submission has no automated test suite at all: no assertions for the WMO-code mapping, °C→°F conversion, geocoding fallback, or unknown-city handling. For a single-file widget a small JS test file (even a plain assertion script) would have been proportionate; as it stands, all verification is manual.

The agent config is minimal and proportional to a single-file widget — one accurate run command, no framework bloat — but it was not updated for this task: AGENTS.md still describes a four-city table-driven page while the build went live with Open-Meteo search, and nothing in the repo encodes the required screencast verification or any check/capture automation. The self-documenting code and header comment carry most of the load. Product correctness (live data, units, unknown-city UX) is left to the sibling judges; the missing webm for this task may hurt there.

A tidy, well-factored single-file widget: the four-city dataset, conversion helpers, and render functions are cleanly separated around one shared HTML shell, with a condition-driven gradient/emoji card, wind/location chips, a units toggle, and a forecast row. The deterministic smoke test passed all three scenarios (29/sunny, 91°F for bangkok&units=f, polite unknown-city page). Small cleanups: deduplicate the city-key normalization between render() and do_GET's status line, simplify the conditional CSS-class expression in units_toggle, and drop the committed pycache artifact.

A tidy, well-factored single-file widget: the four-city dataset, conversion helpers, and render functions are cleanly separated around one shared HTML shell, with a condition-driven gradient/emoji card, wind/location chips, a units toggle, and a forecast row. The deterministic smoke test passed all three scenarios (29/sunny, 91°F for bangkok&units=f, polite unknown-city page). Small cleanups: deduplicate the city-key normalization between render() and do_GET's status line, simplify the conditional CSS-class expression in units_toggle, and drop the committed pycache artifact.

Clean, proportionate single-file architecture for a small widget: dataset, pure conversion logic, per-component renderers, and a thin HTTP layer are cleanly separated with one-way dependencies, and the done-note accurately describes the structure. The main dings are the duplicated city-lookup/normalization between render() and the handler's status-code logic, the unescaped unknown-city query value in the error page, and the committed pycache clutter — all easy fixes that would take this to a 9+.

A polished, compact weather widget with strong contrast, clear controls, and a convincingly demonstrated city-switching/forecast flow. The main visual-review gap is the missing narrow screenshot of the forecast view itself.

A polished, compact weather widget with strong contrast, clear controls, and a convincingly demonstrated city-switching/forecast flow. The main visual-review gap is the missing narrow screenshot of the forecast view itself.

A polished, compact weather widget with strong contrast, clear controls, and a convincingly demonstrated city-switching/forecast flow. The main visual-review gap is the missing narrow screenshot of the forecast view itself.

Exemplary data discipline. The pinned four-city dataset lives in exactly one place (CITIES in weather.py) with values matching the brief; the card, error page, and Fahrenheit conversion all read from it through small pure functions, so every displayed value is traceable to the single source. The forecast row is derived deterministically from today's reading and clearly documented, and no shadow copies of the weather data exist in markup, templates, or the done note. The only blemish is the condition names reappearing as keys in the styling table (CONDITION_STYLES), which is harmless given its fallback. No probe needed.

The weather widget itself is complete and clean (pure helpers, small render functions, all three scenarios implemented), but there is zero test coverage: no test files, no self-checks, and the only verification was manual screenshots. Add at least a minimal automated suite — e.g. unit tests for to_fahrenheit/display_temp/normalize_units and integration checks hitting the HTTP handler for /?city=rome, /?city=bangkok&units=f, and /?city=atlantis — to make the scenarios verifiable by machine.

The widget itself is clean and the three scenarios are implemented (CITIES table, to_fahrenheit, polite unknown-city page, unit toggle, forecast row). On the agentic axis, though, the setup is only partly there: AGENTS.md still describes the old ololo Q&A harness and says nothing about the weather widget, CLAUDE.md is empty, and there are no tests or hooks encoding the run/verify loop — verification happened ad hoc (13 shell calls, browser screenshots). What saves it from a low score is a genuinely used launcher (weather.sh), a thorough module docstring, and a precise done-note that documents behavior and structure. Recommend: add a short weather section to AGENTS.md and a smoke-test script that asserts 29/sunny, 91°F, and 'unknown city'.

A polished, highly legible single-page widget with excellent scenario coverage, clear visual states, and a successfully responsive mobile presentation. Minor improvements would be explicit accessibility state/labels for the unit toggle and hiding decorative emoji from assistive technology.

A polished, highly legible single-page widget with excellent scenario coverage, clear visual states, and a successfully responsive mobile presentation. Minor improvements would be explicit accessibility state/labels for the unit toggle and hiding decorative emoji from assistive technology.

A polished, highly legible single-page widget with excellent scenario coverage, clear visual states, and a successfully responsive mobile presentation. Minor improvements would be explicit accessibility state/labels for the unit toggle and hiding decorative emoji from assistive technology.

The forecast dataset is faithfully reproduced as a single FORECAST constant, and every displayed forecast value (day, temperature, condition) is derived from it — verified against all four cities including the pinned Rome day-2 check (26 / cloudy). The old derived/hardcoded forecast was removed, so no shadow or fabricated data ships. Data lives once, at the top of the script, cleanly separated from render logic; for a single-file static widget this is a solid data story.

Solid, compact solution: city switching and the forecast open/close are driven by a single delegated click handler plus pushState, so navigation never reloads the page and the URL stays bookmarked. The FORECAST table matches the pinned dataset exactly (Rome day 2 = 26 cloudy), and the forecast view lists all three days with a back button returning to the originating card. Code is clean and readable; only small nits (unused params, magic action strings) keep it from a perfect score. The done-note file is present and describes the implementation.

Solid, compact solution: city switching and the forecast open/close are driven by a single delegated click handler plus pushState, so navigation never reloads the page and the URL stays bookmarked. The FORECAST table matches the pinned dataset exactly (Rome day 2 = 26 cloudy), and the forecast view lists all three days with a back button returning to the originating card. Code is clean and readable; only small nits (unused params, magic action strings) keep it from a perfect score. The done-note file is present and describes the implementation.

The widget itself implements the requested flows (city switching via pushState, forecast view with 3 days and a back button, dataset-matching values such as Rome day 2 = 26 cloudy), but the submission contains no tests whatsoever — no unit, integration, or end-to-end checks, and no test harness. The only verification offered is static screenshots and a manual screencast, so the 'tests' criterion is essentially absent. Even a tiny set of DOM-behavior assertions (clicking a city button renders that city's weather; opening the forecast shows day/temp/condition for all 3 days; back returns to the originating card; rome day 2 reads 26/cloudy) would have made this verifiable without human eyes.

The widget work itself is complete and well-contained (single-file SPA with pushState navigation, exact FORECAST dataset, back button), and the done file satisfies the contract. On workflow engineering: AGENTS.md is present, accurate for the base card view, and has a correct run command — good proportionality for a small task — but it was not updated for this task's forecast/city-switch functionality (view=forecast is undocumented), and there are no scripts, skills, or hooks encoding run/check/verify work. The interactive-flow verification is left entirely to the screencast requirement rather than any automated check.

The widget grew into a small SPA with a clear internal structure: data constants, pure helpers, URL-driven state, dedicated render functions, and one delegated click handler. City switching and the forecast/back flow all go through history.pushState with no page reload, and the forecast data matches the pinned table exactly (Rome day 2 = 26 / cloudy). Architecture is clean and proportionate for the scope; only cosmetic nits (unused renderCities params, inline markup strings) keep it from a top score.

Visually polished and clear on desktop, with strong states and useful forecast/city controls. The major issue is mobile responsiveness: the narrow capture shows horizontal clipping rather than a contained or scroll-free layout.

Visually polished and clear on desktop, with strong states and useful forecast/city controls. The major issue is mobile responsiveness: the narrow capture shows horizontal clipping rather than a contained or scroll-free layout.

Visually polished and clear on desktop, with strong states and useful forecast/city controls. The major issue is mobile responsiveness: the narrow capture shows horizontal clipping rather than a contained or scroll-free layout.

Solid single-file weather widget: the four-city dataset is a clean single source of truth, C→F conversion (33→91) and the 'unknown city' path match the scenarios, and the code is factored into small readable functions with a nice derived forecast row and unit/city switching. Main craft gap is escaping user-controlled city input before it reaches innerHTML/title — otherwise a well-contained, maintainable build.

Solid single-file weather widget: the four-city dataset is a clean single source of truth, C→F conversion (33→91) and the 'unknown city' path match the scenarios, and the code is factored into small readable functions with a nice derived forecast row and unit/city switching. Main craft gap is escaping user-controlled city input before it reaches innerHTML/title — otherwise a well-contained, maintainable build.

Solid submission: every required scenario is verified by hand through the code — rome→29/sunny, bangkok&units=f→91 via Math.round(33*9/5+32), atlantis→"unknown city" with a friendly hint. The single-file widget is clean, the four-city dataset is the only source, and the extras (wind, unit toggle, derived forecast row, city links) stay coherent with the brief's intent. Nice touch that the forecast is deterministically derived from the day's reading rather than inventing new data. Watch two small things: escape the user-supplied city in the unknown-city message to avoid raw HTML injection, and prettify the 'Sao-paulo' label. None of this affects the scored scenarios.

A polished single-file widget that goes well beyond the three scenarios: unit toggle, city-switcher nav, derived 3-day forecast row, wind display, a friendly empty state, and an explanatory unknown-city message — all consistent (units carry through every link and the forecast) and all backed by the four-city dataset only. Required scenarios check out by code reading (rome=29/sunny, bangkok f=91, atlantis='unknown city'). Minor note for the craft judges: the city param is interpolated into innerHTML unescaped, so a crafted ?city= could inject markup — worth flagging even though it doesn't affect the graded scenarios.

Excellent data hygiene. The four-city dataset is a single source of truth (WEATHER const) with values matching the brief exactly; all output — card, forecast row, city links, °F conversion (bangkok 33 → 91), and the polite 'unknown city' message — is derived from it at runtime with no duplicated or baked-in values. Reading/shaping/rendering are cleanly separated into small functions. The derived 3-day forecast row stays honest to the pinned source. No probe needed.

Clean, proportional architecture for a one-page widget: dataset constant, pure conversion/forecast logic, and a thin render layer are cleanly separated in a ~168-line file, with all three scenarios satisfied (rome 29/sunny, bangkok 91°F via C*9/5+32 rounded, polite 'unknown city'). The done-note accurately describes the solution. Minor polish: markup strings live inside the render functions and forecast reset is buried in renderCard, but neither is a real defect at this scale.

A clean, self-contained single-file weather widget with an accurate, minimal AGENTS.md (description + run command) that matches the code. The workflow gap: no verification automation for the three scenarios (rome 29/sunny, bangkok 91°F, atlantis 'unknown city'), no guardrails, and no skills — the agent improvised the checks rather than encoding them. A tiny test script (even curl + grep against the served page) would have been proportionate and would have made the acceptance criteria mechanically verifiable.

The widget implementation itself is a clean single-file static page with the correct four-city dataset, C→F conversion (Math.round((c*9)/5+32)), a polite 'unknown city' message, and extras (wind, forecast row, city links). However, for the tests criterion there is nothing to grade: no test files exist in the repository, so none of the scenarios or edge cases (unit conversion rounding, unknown/invalid city, missing params, case-insensitivity) are covered by assertions. Even a minimal test harness (e.g., a Node/jsdom script or a small browser test asserting the DOM output for each scenario) would have made this assessable.