[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-sentry-replay-ux-research":3,"mdc--c8awl2-key":39,"related-org-sentry-replay-ux-research":944,"related-repo-sentry-replay-ux-research":1122},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":27,"repoUrl":28,"updatedAt":29,"license":30,"forks":31,"topics":32,"repo":34,"sourceUrl":37,"mdContent":38},"replay-ux-research","analyze Sentry session replays for UX research","Analyze Sentry session replays to surface UX patterns, pain points, and user journeys for a given product area. Use when asked to \"show me how users use\", \"day in the life\", \"UX research\", \"replay research\", \"how do customers use\", \"what's the user experience like for\", \"watch replays of\", \"analyze replays for\", \"user behavior on\", or \"replay UX audit\" for any Sentry product surface.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"sentry","Sentry","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fsentry.png","getsentry",[13,17,20,23,24],{"name":14,"slug":15,"type":16},"Observability","observability","tag",{"name":18,"slug":19,"type":16},"User Research","user-research",{"name":21,"slug":22,"type":16},"Analytics","analytics",{"name":9,"slug":8,"type":16},{"name":25,"slug":26,"type":16},"Frontend","frontend",861,"https:\u002F\u002Fgithub.com\u002Fgetsentry\u002Fskills","2026-04-27T05:38:53.375233",null,45,[33],"tag-production",{"repoUrl":28,"stars":27,"forks":31,"topics":35,"description":36},[33],"Agent Skills used by the Sentry team for development.","https:\u002F\u002Fgithub.com\u002Fgetsentry\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Freplay-ux-research","---\nname: replay-ux-research\ndescription: Analyze Sentry session replays to surface UX patterns, pain points, and user journeys for a given product area. Use when asked to \"show me how users use\", \"day in the life\", \"UX research\", \"replay research\", \"how do customers use\", \"what's the user experience like for\", \"watch replays of\", \"analyze replays for\", \"user behavior on\", or \"replay UX audit\" for any Sentry product surface.\nargument-hint: '\u003Cproduct-area>'\n---\n\n# Replay UX Research\n\nAnalyze session replays from real external users of sentry.io to surface UX patterns, pain points, and representative journeys for a given product area. This uses Sentry's own dogfooding org.\n\n## Inputs\n\n`$ARGUMENTS` is the product area to research (e.g., \"issues\", \"traces\", \"dashboards\", \"replays\", \"monitors\", \"releases\", \"alerts\").\n\nIf `$ARGUMENTS` is empty, ask the user which product area to research.\n\n## Prerequisites\n\nThis skill requires the Sentry MCP server to be connected. The following tools are used:\n- `search_events` — Search for session replays\n- `get_replay_details` — Get detailed replay information\n- `search_issues` — Look up error issues\n- `get_sentry_resource` — Fetch issue details from URLs\n\nIf these tools are not available, ask the user to connect the Sentry MCP server before proceeding.\n\n## Step 1: Map product area to URL patterns\n\nRead `references\u002Fproduct-areas.md` and find the URL patterns for the requested area.\n\nIf the product area is not listed, infer a URL pattern from the area name. Most Sentry product areas follow the pattern `\u002F\u003Carea-name>\u002F` in the URL path. The reference file may not cover newer product areas — confirm your assumption with the user if unclear.\n\n## Step 2: Search for replays\n\nSearch for replays from external (non-Sentry-employee) users. 25 replays is a good starting point — go deeper if the product area is complex, if early patterns are ambiguous, or if the user wants a more comprehensive picture.\n\nStart with last 24 hours — extend to 48h or 7d if needed to reach your target count. Run multiple `search_events` calls if needed. Use `limit: 50` per call.\n\n**If you can't find enough replays** (fewer than 10 even at 7 days), tell the user what you found and ask them to help iterate — they may suggest broader URL patterns, a different time range, or a related product area to include.\n\n**Query construction:**\n\nUse natural language queries like:\n\n```\nreplays from the last 24 hours where url contains \"\u002F\u003Carea-path>\" excluding user emails ending in @sentry.io and @getsentry.com\n```\n\nKey filters:\n\n- **Time range**: last 24 hours (extend if needed)\n- **URL pattern**: match the product area paths from Step 1\n- **Exclude employees**: `-user.email:*@sentry.io -user.email:*@getsentry.com`\n- **Environment**: prod\n\nDo NOT pass a `projectSlug` filter — replays span the whole org.\n\n## Step 3: Get replay details\n\nCall `get_replay_details` for each replay found in Step 2. Run these calls in parallel batches for speed.\n\nFor each replay, capture:\n\n- **User**: email domain only (the API returns full emails — never include these in output)\n- **Journey**: ordered list of pages visited (from URLs and activity breadcrumbs)\n- **Duration**: total session length\n- **Replay type**: `session` (randomly sampled from normal browsing) vs `buffer` (triggered by an event — error, manual flush, or specific user action like submitting feedback or going through checkout). Note this distinction in your analysis since buffer replays are biased toward error\u002Faction moments, not typical browsing.\n- **Entry context**: first URL tells you how they arrived — look for referrer signals like `referrer=slack`, `notification_uuid`, `alert_rule_id` in query params (Slack notification), email link patterns, or bare URLs (bookmark\u002Fdirect nav)\n- **Engagement signals**: error count, rage clicks, dead clicks, warning count\n- **Browser\u002FOS\u002FDevice**: for device distribution context\n- **Activity breadcrumbs**: page views, navigation patterns, key interactions\n\n## Step 4: Investigate significant errors\n\nAfter collecting replay details, identify errors that appear in multiple replays or seem likely to affect the user experience. For each significant error:\n\n1. **Triage by frequency**: If the same issue ID (e.g., `JAVASCRIPT-33RM`) appears in 3+ replays, it's worth investigating.\n2. **Check the issue in Sentry**: Use `search_issues` to find the issue, or `get_sentry_resource` with the issue URL from the replay details. Understand:\n   - What is the error? (message, stack trace context)\n   - How many total users\u002Fevents does it affect? (beyond just this replay sample)\n   - Is it assigned or being worked on?\n3. **Infer user-facing impact from behavioral signals**: We cannot see the rendered page content through replay metadata — only by watching the replay in-browser. Instead, infer impact from what users did _after_ the error:\n   - **Retried the same action** → they likely saw a failure and tried again\n   - **Navigated away immediately** → they were blocked or gave up\n   - **Continued their flow normally** → the error may be silent\u002Fcosmetic\n   - **Rage-clicked or dead-clicked after** → the UI may have become unresponsive\n   - **Spent a long time on the page after** → they may be reading an error message or confused\n   - **No behavioral change at all** → error was likely invisible to the user\n4. **Classify each error** based on this evidence:\n   - **Likely blocking**: Error + user retried\u002Fleft\u002Fcouldn't continue. High confidence of user impact.\n   - **Likely degrading**: Error + user continued but with unusual behavior. Moderate confidence.\n   - **Likely silent**: Error fired but user behavior was unaffected. Low confidence of user impact.\n   - **Unclear**: Not enough behavioral signal to judge. Flag for manual replay review.\n\n   Always note the confidence level and recommend watching specific replays to confirm. Link directly to the replay URL for each classified error.\n\nInclude this classification in the Friction & Pain Points section. Don't report likely-silent errors as pain points — list them in a separate \"Background Errors (likely silent)\" subsection for completeness.\n\n## Step 5: Analyze patterns\n\nLook at the replays through these UX research lenses:\n\n### Behavioral patterns\n\n1. **Common journeys**: What navigation paths do users take? What's the typical flow?\n2. **Entry points**: How do users arrive? Categorize: alert notification (Slack\u002Femail), direct bookmark, organic navigation from another page. The first URL's query params reveal this.\n3. **Task completion**: Did the user appear to accomplish a goal, or did they wander\u002Fabandon? Signs of completion: navigating to a detail view then leaving. Signs of abandonment: short session, back-and-forth navigation, leaving from the same page they entered.\n4. **Time on task**: How long do users spend on key pages before acting?\n\n### Friction & discovery\n\n1. **Friction signals**: Rage clicks, dead clicks, errors — but also _hesitation_ (visiting the same page repeatedly), _thrashing_ (rapid back-and-forth between pages), and _retry loops_ (repeating the same action sequence).\n2. **Feature discovery**: Are users finding sub-features (filters, search, sort, bulk actions) or only using the primary view? Look at URL query params and breadcrumbs for evidence of feature use.\n3. **User intent signals**: Mine URL query params for search terms, filter values, sort orders, and date ranges users set. These are the closest thing to verbatim user \"quotes\" — they reveal what users are looking for in their own words. (e.g., `query=is%3Aunresolved+assigned%3Ame` tells you the user is triaging their own assigned issues.)\n4. **Workarounds**: Any unexpected navigation patterns that suggest a missing feature or confusing flow? (e.g., going to settings mid-task, opening multiple pages in sequence that could be one view)\n5. **Error recovery**: When users encounter errors, do they recover and continue or abandon?\n\n### Context\n\n1. **Replay trigger mix**: What proportion are `session` (random sample) vs `buffer` (event-triggered)? Buffer replays show moments where something notable happened (error, feedback submission, checkout, etc.) — they're valuable for friction analysis but aren't representative of typical browsing. Call out this bias when drawing conclusions.\n2. **Return visitors**: Do any email domains appear in multiple replays? Repeat visitors suggest habitual usage — their journeys may reveal power-user patterns or persistent pain points they've learned to work around.\n3. **User diversity**: Are replays from many different orgs\u002Fcompanies or concentrated? Are there differences in behavior by org?\n4. **Device\u002Fbrowser distribution**: What are users primarily using?\n5. **Drop-off points**: Where do users leave or navigate away?\n\n## Step 6: Write the report\n\nUse the template in `references\u002Foutput-template.md`. Be specific — cite individual replays as evidence for each pattern. Link to replay URLs so the reader can watch the replay themselves.\n\n**Privacy**: Never include full user email addresses in the report. Use anonymized identifiers like \"user from [company domain]\" or \"User A, B, C.\"\n",{"data":40,"body":42},{"name":4,"description":6,"argument-hint":41},"\u003Cproduct-area>",{"type":43,"children":44},"root",[45,53,59,66,78,90,96,101,150,155,161,174,187,193,198,218,229,237,242,254,259,308,321,327,339,344,465,471,476,680,685,691,696,703,746,752,834,840,907,913,926],{"type":46,"tag":47,"props":48,"children":49},"element","h1",{"id":4},[50],{"type":51,"value":52},"text","Replay UX Research",{"type":46,"tag":54,"props":55,"children":56},"p",{},[57],{"type":51,"value":58},"Analyze session replays from real external users of sentry.io to surface UX patterns, pain points, and representative journeys for a given product area. This uses Sentry's own dogfooding org.",{"type":46,"tag":60,"props":61,"children":63},"h2",{"id":62},"inputs",[64],{"type":51,"value":65},"Inputs",{"type":46,"tag":54,"props":67,"children":68},{},[69,76],{"type":46,"tag":70,"props":71,"children":73},"code",{"className":72},[],[74],{"type":51,"value":75},"$ARGUMENTS",{"type":51,"value":77}," is the product area to research (e.g., \"issues\", \"traces\", \"dashboards\", \"replays\", \"monitors\", \"releases\", \"alerts\").",{"type":46,"tag":54,"props":79,"children":80},{},[81,83,88],{"type":51,"value":82},"If ",{"type":46,"tag":70,"props":84,"children":86},{"className":85},[],[87],{"type":51,"value":75},{"type":51,"value":89}," is empty, ask the user which product area to research.",{"type":46,"tag":60,"props":91,"children":93},{"id":92},"prerequisites",[94],{"type":51,"value":95},"Prerequisites",{"type":46,"tag":54,"props":97,"children":98},{},[99],{"type":51,"value":100},"This skill requires the Sentry MCP server to be connected. The following tools are used:",{"type":46,"tag":102,"props":103,"children":104},"ul",{},[105,117,128,139],{"type":46,"tag":106,"props":107,"children":108},"li",{},[109,115],{"type":46,"tag":70,"props":110,"children":112},{"className":111},[],[113],{"type":51,"value":114},"search_events",{"type":51,"value":116}," — Search for session replays",{"type":46,"tag":106,"props":118,"children":119},{},[120,126],{"type":46,"tag":70,"props":121,"children":123},{"className":122},[],[124],{"type":51,"value":125},"get_replay_details",{"type":51,"value":127}," — Get detailed replay information",{"type":46,"tag":106,"props":129,"children":130},{},[131,137],{"type":46,"tag":70,"props":132,"children":134},{"className":133},[],[135],{"type":51,"value":136},"search_issues",{"type":51,"value":138}," — Look up error issues",{"type":46,"tag":106,"props":140,"children":141},{},[142,148],{"type":46,"tag":70,"props":143,"children":145},{"className":144},[],[146],{"type":51,"value":147},"get_sentry_resource",{"type":51,"value":149}," — Fetch issue details from URLs",{"type":46,"tag":54,"props":151,"children":152},{},[153],{"type":51,"value":154},"If these tools are not available, ask the user to connect the Sentry MCP server before proceeding.",{"type":46,"tag":60,"props":156,"children":158},{"id":157},"step-1-map-product-area-to-url-patterns",[159],{"type":51,"value":160},"Step 1: Map product area to URL patterns",{"type":46,"tag":54,"props":162,"children":163},{},[164,166,172],{"type":51,"value":165},"Read ",{"type":46,"tag":70,"props":167,"children":169},{"className":168},[],[170],{"type":51,"value":171},"references\u002Fproduct-areas.md",{"type":51,"value":173}," and find the URL patterns for the requested area.",{"type":46,"tag":54,"props":175,"children":176},{},[177,179,185],{"type":51,"value":178},"If the product area is not listed, infer a URL pattern from the area name. Most Sentry product areas follow the pattern ",{"type":46,"tag":70,"props":180,"children":182},{"className":181},[],[183],{"type":51,"value":184},"\u002F\u003Carea-name>\u002F",{"type":51,"value":186}," in the URL path. The reference file may not cover newer product areas — confirm your assumption with the user if unclear.",{"type":46,"tag":60,"props":188,"children":190},{"id":189},"step-2-search-for-replays",[191],{"type":51,"value":192},"Step 2: Search for replays",{"type":46,"tag":54,"props":194,"children":195},{},[196],{"type":51,"value":197},"Search for replays from external (non-Sentry-employee) users. 25 replays is a good starting point — go deeper if the product area is complex, if early patterns are ambiguous, or if the user wants a more comprehensive picture.",{"type":46,"tag":54,"props":199,"children":200},{},[201,203,208,210,216],{"type":51,"value":202},"Start with last 24 hours — extend to 48h or 7d if needed to reach your target count. Run multiple ",{"type":46,"tag":70,"props":204,"children":206},{"className":205},[],[207],{"type":51,"value":114},{"type":51,"value":209}," calls if needed. Use ",{"type":46,"tag":70,"props":211,"children":213},{"className":212},[],[214],{"type":51,"value":215},"limit: 50",{"type":51,"value":217}," per call.",{"type":46,"tag":54,"props":219,"children":220},{},[221,227],{"type":46,"tag":222,"props":223,"children":224},"strong",{},[225],{"type":51,"value":226},"If you can't find enough replays",{"type":51,"value":228}," (fewer than 10 even at 7 days), tell the user what you found and ask them to help iterate — they may suggest broader URL patterns, a different time range, or a related product area to include.",{"type":46,"tag":54,"props":230,"children":231},{},[232],{"type":46,"tag":222,"props":233,"children":234},{},[235],{"type":51,"value":236},"Query construction:",{"type":46,"tag":54,"props":238,"children":239},{},[240],{"type":51,"value":241},"Use natural language queries like:",{"type":46,"tag":243,"props":244,"children":248},"pre",{"className":245,"code":247,"language":51},[246],"language-text","replays from the last 24 hours where url contains \"\u002F\u003Carea-path>\" excluding user emails ending in @sentry.io and @getsentry.com\n",[249],{"type":46,"tag":70,"props":250,"children":252},{"__ignoreMap":251},"",[253],{"type":51,"value":247},{"type":46,"tag":54,"props":255,"children":256},{},[257],{"type":51,"value":258},"Key filters:",{"type":46,"tag":102,"props":260,"children":261},{},[262,272,282,298],{"type":46,"tag":106,"props":263,"children":264},{},[265,270],{"type":46,"tag":222,"props":266,"children":267},{},[268],{"type":51,"value":269},"Time range",{"type":51,"value":271},": last 24 hours (extend if needed)",{"type":46,"tag":106,"props":273,"children":274},{},[275,280],{"type":46,"tag":222,"props":276,"children":277},{},[278],{"type":51,"value":279},"URL pattern",{"type":51,"value":281},": match the product area paths from Step 1",{"type":46,"tag":106,"props":283,"children":284},{},[285,290,292],{"type":46,"tag":222,"props":286,"children":287},{},[288],{"type":51,"value":289},"Exclude employees",{"type":51,"value":291},": ",{"type":46,"tag":70,"props":293,"children":295},{"className":294},[],[296],{"type":51,"value":297},"-user.email:*@sentry.io -user.email:*@getsentry.com",{"type":46,"tag":106,"props":299,"children":300},{},[301,306],{"type":46,"tag":222,"props":302,"children":303},{},[304],{"type":51,"value":305},"Environment",{"type":51,"value":307},": prod",{"type":46,"tag":54,"props":309,"children":310},{},[311,313,319],{"type":51,"value":312},"Do NOT pass a ",{"type":46,"tag":70,"props":314,"children":316},{"className":315},[],[317],{"type":51,"value":318},"projectSlug",{"type":51,"value":320}," filter — replays span the whole org.",{"type":46,"tag":60,"props":322,"children":324},{"id":323},"step-3-get-replay-details",[325],{"type":51,"value":326},"Step 3: Get replay details",{"type":46,"tag":54,"props":328,"children":329},{},[330,332,337],{"type":51,"value":331},"Call ",{"type":46,"tag":70,"props":333,"children":335},{"className":334},[],[336],{"type":51,"value":125},{"type":51,"value":338}," for each replay found in Step 2. Run these calls in parallel batches for speed.",{"type":46,"tag":54,"props":340,"children":341},{},[342],{"type":51,"value":343},"For each replay, capture:",{"type":46,"tag":102,"props":345,"children":346},{},[347,357,367,377,402,435,445,455],{"type":46,"tag":106,"props":348,"children":349},{},[350,355],{"type":46,"tag":222,"props":351,"children":352},{},[353],{"type":51,"value":354},"User",{"type":51,"value":356},": email domain only (the API returns full emails — never include these in output)",{"type":46,"tag":106,"props":358,"children":359},{},[360,365],{"type":46,"tag":222,"props":361,"children":362},{},[363],{"type":51,"value":364},"Journey",{"type":51,"value":366},": ordered list of pages visited (from URLs and activity breadcrumbs)",{"type":46,"tag":106,"props":368,"children":369},{},[370,375],{"type":46,"tag":222,"props":371,"children":372},{},[373],{"type":51,"value":374},"Duration",{"type":51,"value":376},": total session length",{"type":46,"tag":106,"props":378,"children":379},{},[380,385,386,392,394,400],{"type":46,"tag":222,"props":381,"children":382},{},[383],{"type":51,"value":384},"Replay type",{"type":51,"value":291},{"type":46,"tag":70,"props":387,"children":389},{"className":388},[],[390],{"type":51,"value":391},"session",{"type":51,"value":393}," (randomly sampled from normal browsing) vs ",{"type":46,"tag":70,"props":395,"children":397},{"className":396},[],[398],{"type":51,"value":399},"buffer",{"type":51,"value":401}," (triggered by an event — error, manual flush, or specific user action like submitting feedback or going through checkout). Note this distinction in your analysis since buffer replays are biased toward error\u002Faction moments, not typical browsing.",{"type":46,"tag":106,"props":403,"children":404},{},[405,410,412,418,420,426,427,433],{"type":46,"tag":222,"props":406,"children":407},{},[408],{"type":51,"value":409},"Entry context",{"type":51,"value":411},": first URL tells you how they arrived — look for referrer signals like ",{"type":46,"tag":70,"props":413,"children":415},{"className":414},[],[416],{"type":51,"value":417},"referrer=slack",{"type":51,"value":419},", ",{"type":46,"tag":70,"props":421,"children":423},{"className":422},[],[424],{"type":51,"value":425},"notification_uuid",{"type":51,"value":419},{"type":46,"tag":70,"props":428,"children":430},{"className":429},[],[431],{"type":51,"value":432},"alert_rule_id",{"type":51,"value":434}," in query params (Slack notification), email link patterns, or bare URLs (bookmark\u002Fdirect nav)",{"type":46,"tag":106,"props":436,"children":437},{},[438,443],{"type":46,"tag":222,"props":439,"children":440},{},[441],{"type":51,"value":442},"Engagement signals",{"type":51,"value":444},": error count, rage clicks, dead clicks, warning count",{"type":46,"tag":106,"props":446,"children":447},{},[448,453],{"type":46,"tag":222,"props":449,"children":450},{},[451],{"type":51,"value":452},"Browser\u002FOS\u002FDevice",{"type":51,"value":454},": for device distribution context",{"type":46,"tag":106,"props":456,"children":457},{},[458,463],{"type":46,"tag":222,"props":459,"children":460},{},[461],{"type":51,"value":462},"Activity breadcrumbs",{"type":51,"value":464},": page views, navigation patterns, key interactions",{"type":46,"tag":60,"props":466,"children":468},{"id":467},"step-4-investigate-significant-errors",[469],{"type":51,"value":470},"Step 4: Investigate significant errors",{"type":46,"tag":54,"props":472,"children":473},{},[474],{"type":51,"value":475},"After collecting replay details, identify errors that appear in multiple replays or seem likely to affect the user experience. For each significant error:",{"type":46,"tag":477,"props":478,"children":479},"ol",{},[480,498,540,621],{"type":46,"tag":106,"props":481,"children":482},{},[483,488,490,496],{"type":46,"tag":222,"props":484,"children":485},{},[486],{"type":51,"value":487},"Triage by frequency",{"type":51,"value":489},": If the same issue ID (e.g., ",{"type":46,"tag":70,"props":491,"children":493},{"className":492},[],[494],{"type":51,"value":495},"JAVASCRIPT-33RM",{"type":51,"value":497},") appears in 3+ replays, it's worth investigating.",{"type":46,"tag":106,"props":499,"children":500},{},[501,506,508,513,515,520,522],{"type":46,"tag":222,"props":502,"children":503},{},[504],{"type":51,"value":505},"Check the issue in Sentry",{"type":51,"value":507},": Use ",{"type":46,"tag":70,"props":509,"children":511},{"className":510},[],[512],{"type":51,"value":136},{"type":51,"value":514}," to find the issue, or ",{"type":46,"tag":70,"props":516,"children":518},{"className":517},[],[519],{"type":51,"value":147},{"type":51,"value":521}," with the issue URL from the replay details. Understand:",{"type":46,"tag":102,"props":523,"children":524},{},[525,530,535],{"type":46,"tag":106,"props":526,"children":527},{},[528],{"type":51,"value":529},"What is the error? (message, stack trace context)",{"type":46,"tag":106,"props":531,"children":532},{},[533],{"type":51,"value":534},"How many total users\u002Fevents does it affect? (beyond just this replay sample)",{"type":46,"tag":106,"props":536,"children":537},{},[538],{"type":51,"value":539},"Is it assigned or being worked on?",{"type":46,"tag":106,"props":541,"children":542},{},[543,548,550,556,558],{"type":46,"tag":222,"props":544,"children":545},{},[546],{"type":51,"value":547},"Infer user-facing impact from behavioral signals",{"type":51,"value":549},": We cannot see the rendered page content through replay metadata — only by watching the replay in-browser. Instead, infer impact from what users did ",{"type":46,"tag":551,"props":552,"children":553},"em",{},[554],{"type":51,"value":555},"after",{"type":51,"value":557}," the error:",{"type":46,"tag":102,"props":559,"children":560},{},[561,571,581,591,601,611],{"type":46,"tag":106,"props":562,"children":563},{},[564,569],{"type":46,"tag":222,"props":565,"children":566},{},[567],{"type":51,"value":568},"Retried the same action",{"type":51,"value":570}," → they likely saw a failure and tried again",{"type":46,"tag":106,"props":572,"children":573},{},[574,579],{"type":46,"tag":222,"props":575,"children":576},{},[577],{"type":51,"value":578},"Navigated away immediately",{"type":51,"value":580}," → they were blocked or gave up",{"type":46,"tag":106,"props":582,"children":583},{},[584,589],{"type":46,"tag":222,"props":585,"children":586},{},[587],{"type":51,"value":588},"Continued their flow normally",{"type":51,"value":590}," → the error may be silent\u002Fcosmetic",{"type":46,"tag":106,"props":592,"children":593},{},[594,599],{"type":46,"tag":222,"props":595,"children":596},{},[597],{"type":51,"value":598},"Rage-clicked or dead-clicked after",{"type":51,"value":600}," → the UI may have become unresponsive",{"type":46,"tag":106,"props":602,"children":603},{},[604,609],{"type":46,"tag":222,"props":605,"children":606},{},[607],{"type":51,"value":608},"Spent a long time on the page after",{"type":51,"value":610}," → they may be reading an error message or confused",{"type":46,"tag":106,"props":612,"children":613},{},[614,619],{"type":46,"tag":222,"props":615,"children":616},{},[617],{"type":51,"value":618},"No behavioral change at all",{"type":51,"value":620}," → error was likely invisible to the user",{"type":46,"tag":106,"props":622,"children":623},{},[624,629,631,674,678],{"type":46,"tag":222,"props":625,"children":626},{},[627],{"type":51,"value":628},"Classify each error",{"type":51,"value":630}," based on this evidence:",{"type":46,"tag":102,"props":632,"children":633},{},[634,644,654,664],{"type":46,"tag":106,"props":635,"children":636},{},[637,642],{"type":46,"tag":222,"props":638,"children":639},{},[640],{"type":51,"value":641},"Likely blocking",{"type":51,"value":643},": Error + user retried\u002Fleft\u002Fcouldn't continue. High confidence of user impact.",{"type":46,"tag":106,"props":645,"children":646},{},[647,652],{"type":46,"tag":222,"props":648,"children":649},{},[650],{"type":51,"value":651},"Likely degrading",{"type":51,"value":653},": Error + user continued but with unusual behavior. Moderate confidence.",{"type":46,"tag":106,"props":655,"children":656},{},[657,662],{"type":46,"tag":222,"props":658,"children":659},{},[660],{"type":51,"value":661},"Likely silent",{"type":51,"value":663},": Error fired but user behavior was unaffected. Low confidence of user impact.",{"type":46,"tag":106,"props":665,"children":666},{},[667,672],{"type":46,"tag":222,"props":668,"children":669},{},[670],{"type":51,"value":671},"Unclear",{"type":51,"value":673},": Not enough behavioral signal to judge. Flag for manual replay review.",{"type":46,"tag":675,"props":676,"children":677},"br",{},[],{"type":51,"value":679},"Always note the confidence level and recommend watching specific replays to confirm. Link directly to the replay URL for each classified error.",{"type":46,"tag":54,"props":681,"children":682},{},[683],{"type":51,"value":684},"Include this classification in the Friction & Pain Points section. Don't report likely-silent errors as pain points — list them in a separate \"Background Errors (likely silent)\" subsection for completeness.",{"type":46,"tag":60,"props":686,"children":688},{"id":687},"step-5-analyze-patterns",[689],{"type":51,"value":690},"Step 5: Analyze patterns",{"type":46,"tag":54,"props":692,"children":693},{},[694],{"type":51,"value":695},"Look at the replays through these UX research lenses:",{"type":46,"tag":697,"props":698,"children":700},"h3",{"id":699},"behavioral-patterns",[701],{"type":51,"value":702},"Behavioral patterns",{"type":46,"tag":477,"props":704,"children":705},{},[706,716,726,736],{"type":46,"tag":106,"props":707,"children":708},{},[709,714],{"type":46,"tag":222,"props":710,"children":711},{},[712],{"type":51,"value":713},"Common journeys",{"type":51,"value":715},": What navigation paths do users take? What's the typical flow?",{"type":46,"tag":106,"props":717,"children":718},{},[719,724],{"type":46,"tag":222,"props":720,"children":721},{},[722],{"type":51,"value":723},"Entry points",{"type":51,"value":725},": How do users arrive? Categorize: alert notification (Slack\u002Femail), direct bookmark, organic navigation from another page. The first URL's query params reveal this.",{"type":46,"tag":106,"props":727,"children":728},{},[729,734],{"type":46,"tag":222,"props":730,"children":731},{},[732],{"type":51,"value":733},"Task completion",{"type":51,"value":735},": Did the user appear to accomplish a goal, or did they wander\u002Fabandon? Signs of completion: navigating to a detail view then leaving. Signs of abandonment: short session, back-and-forth navigation, leaving from the same page they entered.",{"type":46,"tag":106,"props":737,"children":738},{},[739,744],{"type":46,"tag":222,"props":740,"children":741},{},[742],{"type":51,"value":743},"Time on task",{"type":51,"value":745},": How long do users spend on key pages before acting?",{"type":46,"tag":697,"props":747,"children":749},{"id":748},"friction-discovery",[750],{"type":51,"value":751},"Friction & discovery",{"type":46,"tag":477,"props":753,"children":754},{},[755,786,796,814,824],{"type":46,"tag":106,"props":756,"children":757},{},[758,763,765,770,772,777,779,784],{"type":46,"tag":222,"props":759,"children":760},{},[761],{"type":51,"value":762},"Friction signals",{"type":51,"value":764},": Rage clicks, dead clicks, errors — but also ",{"type":46,"tag":551,"props":766,"children":767},{},[768],{"type":51,"value":769},"hesitation",{"type":51,"value":771}," (visiting the same page repeatedly), ",{"type":46,"tag":551,"props":773,"children":774},{},[775],{"type":51,"value":776},"thrashing",{"type":51,"value":778}," (rapid back-and-forth between pages), and ",{"type":46,"tag":551,"props":780,"children":781},{},[782],{"type":51,"value":783},"retry loops",{"type":51,"value":785}," (repeating the same action sequence).",{"type":46,"tag":106,"props":787,"children":788},{},[789,794],{"type":46,"tag":222,"props":790,"children":791},{},[792],{"type":51,"value":793},"Feature discovery",{"type":51,"value":795},": Are users finding sub-features (filters, search, sort, bulk actions) or only using the primary view? Look at URL query params and breadcrumbs for evidence of feature use.",{"type":46,"tag":106,"props":797,"children":798},{},[799,804,806,812],{"type":46,"tag":222,"props":800,"children":801},{},[802],{"type":51,"value":803},"User intent signals",{"type":51,"value":805},": Mine URL query params for search terms, filter values, sort orders, and date ranges users set. These are the closest thing to verbatim user \"quotes\" — they reveal what users are looking for in their own words. (e.g., ",{"type":46,"tag":70,"props":807,"children":809},{"className":808},[],[810],{"type":51,"value":811},"query=is%3Aunresolved+assigned%3Ame",{"type":51,"value":813}," tells you the user is triaging their own assigned issues.)",{"type":46,"tag":106,"props":815,"children":816},{},[817,822],{"type":46,"tag":222,"props":818,"children":819},{},[820],{"type":51,"value":821},"Workarounds",{"type":51,"value":823},": Any unexpected navigation patterns that suggest a missing feature or confusing flow? (e.g., going to settings mid-task, opening multiple pages in sequence that could be one view)",{"type":46,"tag":106,"props":825,"children":826},{},[827,832],{"type":46,"tag":222,"props":828,"children":829},{},[830],{"type":51,"value":831},"Error recovery",{"type":51,"value":833},": When users encounter errors, do they recover and continue or abandon?",{"type":46,"tag":697,"props":835,"children":837},{"id":836},"context",[838],{"type":51,"value":839},"Context",{"type":46,"tag":477,"props":841,"children":842},{},[843,867,877,887,897],{"type":46,"tag":106,"props":844,"children":845},{},[846,851,853,858,860,865],{"type":46,"tag":222,"props":847,"children":848},{},[849],{"type":51,"value":850},"Replay trigger mix",{"type":51,"value":852},": What proportion are ",{"type":46,"tag":70,"props":854,"children":856},{"className":855},[],[857],{"type":51,"value":391},{"type":51,"value":859}," (random sample) vs ",{"type":46,"tag":70,"props":861,"children":863},{"className":862},[],[864],{"type":51,"value":399},{"type":51,"value":866}," (event-triggered)? Buffer replays show moments where something notable happened (error, feedback submission, checkout, etc.) — they're valuable for friction analysis but aren't representative of typical browsing. Call out this bias when drawing conclusions.",{"type":46,"tag":106,"props":868,"children":869},{},[870,875],{"type":46,"tag":222,"props":871,"children":872},{},[873],{"type":51,"value":874},"Return visitors",{"type":51,"value":876},": Do any email domains appear in multiple replays? Repeat visitors suggest habitual usage — their journeys may reveal power-user patterns or persistent pain points they've learned to work around.",{"type":46,"tag":106,"props":878,"children":879},{},[880,885],{"type":46,"tag":222,"props":881,"children":882},{},[883],{"type":51,"value":884},"User diversity",{"type":51,"value":886},": Are replays from many different orgs\u002Fcompanies or concentrated? Are there differences in behavior by org?",{"type":46,"tag":106,"props":888,"children":889},{},[890,895],{"type":46,"tag":222,"props":891,"children":892},{},[893],{"type":51,"value":894},"Device\u002Fbrowser distribution",{"type":51,"value":896},": What are users primarily using?",{"type":46,"tag":106,"props":898,"children":899},{},[900,905],{"type":46,"tag":222,"props":901,"children":902},{},[903],{"type":51,"value":904},"Drop-off points",{"type":51,"value":906},": Where do users leave or navigate away?",{"type":46,"tag":60,"props":908,"children":910},{"id":909},"step-6-write-the-report",[911],{"type":51,"value":912},"Step 6: Write the report",{"type":46,"tag":54,"props":914,"children":915},{},[916,918,924],{"type":51,"value":917},"Use the template in ",{"type":46,"tag":70,"props":919,"children":921},{"className":920},[],[922],{"type":51,"value":923},"references\u002Foutput-template.md",{"type":51,"value":925},". Be specific — cite individual replays as evidence for each pattern. Link to replay URLs so the reader can watch the replay themselves.",{"type":46,"tag":54,"props":927,"children":928},{},[929,934,936,942],{"type":46,"tag":222,"props":930,"children":931},{},[932],{"type":51,"value":933},"Privacy",{"type":51,"value":935},": Never include full user email addresses in the report. Use anonymized identifiers like \"user from ",{"type":46,"tag":937,"props":938,"children":939},"span",{},[940],{"type":51,"value":941},"company domain",{"type":51,"value":943},"\" or \"User A, B, C.\"",{"items":945,"total":1121},[946,971,985,998,1012,1029,1045,1059,1069,1080,1090,1108],{"slug":947,"name":947,"fn":948,"description":949,"org":950,"tags":951,"stars":968,"repoUrl":969,"updatedAt":970},"xcodebuildmcp","build and test Apple apps with XcodeBuildMCP","Official skill for XcodeBuildMCP. Use when doing iOS\u002FmacOS\u002FwatchOS\u002FtvOS\u002FvisionOS work (build, test, run, debug, log, UI automation).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[952,955,958,961,962,965],{"name":953,"slug":954,"type":16},"Debugging","debugging",{"name":956,"slug":957,"type":16},"iOS","ios",{"name":959,"slug":960,"type":16},"macOS","macos",{"name":9,"slug":8,"type":16},{"name":963,"slug":964,"type":16},"Testing","testing",{"name":966,"slug":967,"type":16},"Xcode","xcode",6176,"https:\u002F\u002Fgithub.com\u002Fgetsentry\u002FXcodeBuildMCP","2026-04-06T18:13:34.8719",{"slug":972,"name":972,"fn":973,"description":974,"org":975,"tags":976,"stars":968,"repoUrl":969,"updatedAt":984},"xcodebuildmcp-cli","build and test Apple apps via CLI","Official skill for the XcodeBuildMCP CLI. Use when doing iOS\u002FmacOS\u002FwatchOS\u002FtvOS\u002FvisionOS work (build, test, run, debug, log, UI automation).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[977,980,981,982,983],{"name":978,"slug":979,"type":16},"CLI","cli",{"name":956,"slug":957,"type":16},{"name":959,"slug":960,"type":16},{"name":963,"slug":964,"type":16},{"name":966,"slug":967,"type":16},"2026-04-06T18:13:36.13414",{"slug":986,"name":986,"fn":987,"description":988,"org":989,"tags":990,"stars":27,"repoUrl":28,"updatedAt":997},"agents-md","maintain project instruction files","Creates and maintains concise AGENTS.md and CLAUDE.md project instruction files. Use when asked to create AGENTS.md, update AGENTS.md, maintain agent docs, set up CLAUDE.md, document repository agent conventions, or keep coding-agent instructions minimal and reference-backed.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[991,994],{"name":992,"slug":993,"type":16},"Documentation","documentation",{"name":995,"slug":996,"type":16},"Engineering","engineering","2026-05-15T06:16:29.695991",{"slug":999,"name":999,"fn":1000,"description":1001,"org":1002,"tags":1003,"stars":27,"repoUrl":28,"updatedAt":1011},"blog-writing-guide","write and review engineering blog posts","Write, review, and improve blog posts for the Sentry engineering blog following Sentry's specific writing standards, voice, and quality bar. Use this skill whenever someone asks to write a blog post, draft a technical article, review blog content, improve a draft, write a product announcement, create an engineering deep-dive, or produce any written content destined for the Sentry blog or developer audience. Also trigger when the user mentions \"blog post,\" \"blog draft,\" \"write-up,\" \"announcement post,\" \"engineering post,\" \"deep dive,\" \"postmortem,\" or asks for help with technical writing for Sentry. Even if the user just says \"help me write about [feature\u002Ftopic]\" — if it sounds like it could become a Sentry blog post, use this skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1004,1007,1008],{"name":1005,"slug":1006,"type":16},"Communications","communications",{"name":9,"slug":8,"type":16},{"name":1009,"slug":1010,"type":16},"Technical Writing","technical-writing","2026-05-15T06:16:33.38217",{"slug":1013,"name":1013,"fn":1014,"description":1015,"org":1016,"tags":1017,"stars":27,"repoUrl":28,"updatedAt":1028},"brand-guidelines","write copy following Sentry brand guidelines","Write copy following Sentry brand guidelines. Use when writing UI text, error messages, empty states, onboarding flows, 404 pages, documentation, marketing copy, or any user-facing content. Covers both Plain Speech (default) and Sentry Voice tones.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1018,1021,1024,1025],{"name":1019,"slug":1020,"type":16},"Branding","branding",{"name":1022,"slug":1023,"type":16},"Content Creation","content-creation",{"name":9,"slug":8,"type":16},{"name":1026,"slug":1027,"type":16},"UX Copy","ux-copy","2026-05-15T06:16:22.395707",{"slug":1030,"name":1030,"fn":1031,"description":1032,"org":1033,"tags":1034,"stars":27,"repoUrl":28,"updatedAt":1044},"claude-settings-audit","generate Claude Code settings permissions","Analyze a repository to generate recommended Claude Code settings.json permissions. Use when setting up a new project, auditing existing settings, or determining which read-only bash commands to allow. Detects tech stack, build tools, and monorepo structure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1035,1038,1041],{"name":1036,"slug":1037,"type":16},"Claude Code","claude-code",{"name":1039,"slug":1040,"type":16},"Configuration","configuration",{"name":1042,"slug":1043,"type":16},"Security","security","2026-05-15T06:16:44.335977",{"slug":1046,"name":1046,"fn":1047,"description":1048,"org":1049,"tags":1050,"stars":27,"repoUrl":28,"updatedAt":1058},"code-review","perform code reviews for Sentry projects","Perform code reviews following Sentry engineering practices. Use when reviewing pull requests, examining code changes, or providing feedback on code quality. Covers security, performance, testing, and design review.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1051,1053,1054,1057],{"name":1052,"slug":1046,"type":16},"Code Review",{"name":995,"slug":996,"type":16},{"name":1055,"slug":1056,"type":16},"Performance","performance",{"name":1042,"slug":1043,"type":16},"2026-05-15T06:16:35.824864",{"slug":1060,"name":1060,"fn":1061,"description":1062,"org":1063,"tags":1064,"stars":27,"repoUrl":28,"updatedAt":1068},"code-simplifier","simplify and refine source code","Simplifies and refines code for clarity, consistency, and maintainability while preserving all functionality. Use when asked to \"simplify code\", \"clean up code\", \"refactor for clarity\", \"improve readability\", or review recently modified code for elegance. Focuses on project-specific best practices.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1065],{"name":1066,"slug":1067,"type":16},"Code Analysis","code-analysis","2026-05-15T06:16:32.127981",{"slug":1070,"name":1070,"fn":1071,"description":1072,"org":1073,"tags":1074,"stars":27,"repoUrl":28,"updatedAt":1079},"commit","create commits with Sentry conventions","Use for every request to commit changes or draft a commit message. Creates Sentry-style conventional commits with issue references.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1075,1078],{"name":1076,"slug":1077,"type":16},"Git","git",{"name":9,"slug":8,"type":16},"2026-07-18T05:15:10.723937",{"slug":1081,"name":1081,"fn":1082,"description":1083,"org":1084,"tags":1085,"stars":27,"repoUrl":28,"updatedAt":1089},"create-branch","create git branches for Sentry workflows","Create a git branch following Sentry naming conventions. Use when asked to \"create a branch\", \"new branch\", \"start a branch\", \"make a branch\", \"switch to a new branch\", or when starting new work on the default branch.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1086,1087,1088],{"name":995,"slug":996,"type":16},{"name":1076,"slug":1077,"type":16},{"name":9,"slug":8,"type":16},"2026-05-15T06:16:39.458431",{"slug":1091,"name":1091,"fn":1092,"description":1093,"org":1094,"tags":1095,"stars":27,"repoUrl":28,"updatedAt":1107},"django-access-review","review Django access control and IDOR","Django access control and IDOR security review. Use when reviewing Django views, DRF viewsets, ORM queries, or any Python\u002FDjango code handling user authorization. Trigger keywords: \"IDOR\", \"access control\", \"authorization\", \"Django permissions\", \"object permissions\", \"tenant isolation\", \"broken access\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1096,1099,1100,1103,1106],{"name":1097,"slug":1098,"type":16},"Access Control","access-control",{"name":1066,"slug":1067,"type":16},{"name":1101,"slug":1102,"type":16},"Django","django",{"name":1104,"slug":1105,"type":16},"Python","python",{"name":1042,"slug":1043,"type":16},"2026-05-15T06:16:43.098698",{"slug":1109,"name":1109,"fn":1110,"description":1111,"org":1112,"tags":1113,"stars":27,"repoUrl":28,"updatedAt":1120},"django-perf-review","review and optimize Django performance","Django performance code review. Use when asked to \"review Django performance\", \"find N+1 queries\", \"optimize Django\", \"check queryset performance\", \"database performance\", \"Django ORM issues\", or audit Django code for performance problems.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1114,1115,1118,1119],{"name":1052,"slug":1046,"type":16},{"name":1116,"slug":1117,"type":16},"Database","database",{"name":1101,"slug":1102,"type":16},{"name":1055,"slug":1056,"type":16},"2026-05-15T06:16:24.832813",88,{"items":1123,"total":1164},[1124,1129,1135,1142,1148,1155,1159],{"slug":986,"name":986,"fn":987,"description":988,"org":1125,"tags":1126,"stars":27,"repoUrl":28,"updatedAt":997},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1127,1128],{"name":992,"slug":993,"type":16},{"name":995,"slug":996,"type":16},{"slug":999,"name":999,"fn":1000,"description":1001,"org":1130,"tags":1131,"stars":27,"repoUrl":28,"updatedAt":1011},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1132,1133,1134],{"name":1005,"slug":1006,"type":16},{"name":9,"slug":8,"type":16},{"name":1009,"slug":1010,"type":16},{"slug":1013,"name":1013,"fn":1014,"description":1015,"org":1136,"tags":1137,"stars":27,"repoUrl":28,"updatedAt":1028},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1138,1139,1140,1141],{"name":1019,"slug":1020,"type":16},{"name":1022,"slug":1023,"type":16},{"name":9,"slug":8,"type":16},{"name":1026,"slug":1027,"type":16},{"slug":1030,"name":1030,"fn":1031,"description":1032,"org":1143,"tags":1144,"stars":27,"repoUrl":28,"updatedAt":1044},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1145,1146,1147],{"name":1036,"slug":1037,"type":16},{"name":1039,"slug":1040,"type":16},{"name":1042,"slug":1043,"type":16},{"slug":1046,"name":1046,"fn":1047,"description":1048,"org":1149,"tags":1150,"stars":27,"repoUrl":28,"updatedAt":1058},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1151,1152,1153,1154],{"name":1052,"slug":1046,"type":16},{"name":995,"slug":996,"type":16},{"name":1055,"slug":1056,"type":16},{"name":1042,"slug":1043,"type":16},{"slug":1060,"name":1060,"fn":1061,"description":1062,"org":1156,"tags":1157,"stars":27,"repoUrl":28,"updatedAt":1068},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1158],{"name":1066,"slug":1067,"type":16},{"slug":1070,"name":1070,"fn":1071,"description":1072,"org":1160,"tags":1161,"stars":27,"repoUrl":28,"updatedAt":1079},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1162,1163],{"name":1076,"slug":1077,"type":16},{"name":9,"slug":8,"type":16},28]