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Update to Fullstory

Snapshot Oct 30, 2026 · 22:33 UTC · version 3.1.0

WHAT CHANGED

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Full description — Now says: See what actually happened behind every number. Fullstory MCP gives ChatGPT and Codex both halves of product investigation: analytics to size a problem and...

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Full description

Before

Oct 24, 2026 · 12:40 UTC

Ask about your product analytics, and get answers you can verify. Fullstory MCP connects Codex and ChatGPT to your product's behavioral data. Ask how many users adopted a feature, where checkout is leaking, or whether yesterday's release...

After

Oct 30, 2026 · 22:33 UTC

See what actually happened behind every number. Fullstory MCP gives ChatGPT and Codex both halves of product investigation: analytics to size a problem and session replay to see why it happened. Ask where checkout is leaking, why a metri...

Long description

Before

Oct 24, 2026 · 12:40 UTC

Ask about your product analytics, and get answers you can verify. Fullstory MCP connects Codex and ChatGPT to your product's behavioral data. Ask how many users adopted a feature, where checkout is leaking, or whether yesterday's release...

After

Oct 30, 2026 · 22:33 UTC

See what actually happened behind every number. Fullstory MCP gives ChatGPT and Codex both halves of product investigation: analytics to size a problem and session replay to see why it happened. Ask where checkout is leaking, why a metri...

Declared skills

Before

Oct 24, 2026 · 12:40 UTC

[{"description":"How to structure A vs B comparisons in Fullstory — when to use dimensionality (event/session properties) vs separate segments (user-level properties), and why the distinction matters for correctness.","interface":{"brand...

After

Oct 30, 2026 · 22:33 UTC

[{"description":"How to structure A vs B comparisons in Fullstory — when to use dimensionality (event/session properties) vs separate segments (user-level properties), and why the distinction matters for correctness.","interface":{"brand...

App-6a9a1ebb11fc81919ef2aa7231b9b068

Before

Oct 24, 2026 · 12:40 UTC

{"id":"asdk_app_6a9a1ebb11fc81919ef2aa7231b9b068","required":true}

After

Oct 30, 2026 · 22:33 UTC

Not present

Compare saved observations

Download comparison JSON
Full technical diff · 13 changed fields

removed /release/app_manifest/apps/app-6a9a1ebb11fc81919ef2aa7231b9b068

BEFORE
{
  "id": "asdk_app_6a9a1ebb11fc81919ef2aa7231b9b068",
  "required": true
}
AFTER
Field is absent

added /release/app_manifest/apps/fullstory

BEFORE
Field was absent
AFTER
{
  "id": "asdk_app_6a9a1ebb11fc81919ef2aa7231b9b068",
  "required": true
}

changed /release/description

BEFORE
"Ask about your product analytics, and get answers you can verify.\n\nFullstory MCP connects Codex and ChatGPT to your product's behavioral data. Ask how many users adopted a feature, where checkout is leaking, or whether yesterday's release broke something, and Codex and ChatGPT build the segment, computes the metric, and explains the result with a link back to Fullstory.\n\nAnalytics connectors can give you numbers. Fullstory shows you what happened. Metrics unpack into real session replay, so \"conversion dropped 12%\" becomes \"the coupon field errors for logged-out users.\"\n\nAnd because Fullstory autocaptures clicks, page views, errors, and frustration signals, many questions need no instrumentation at all. Ask about rage clicks on pricing or errors in checkout without shipping a single event.\n\nWhat Codex and ChatGPT can do:\n(1) Build and save segments and metrics.\nDescribe the users or behavior you care about in your own words; Codex and ChatGPT create segments or metrics in Fullstory where your whole team can reuse it.\n\n(2) Compute on demand.\n Counts, rates, trends, and breakdowns by device, browser, geography, or user property, over any time window.\n\n(3) Speak your product's language. \nCodex and ChatGPT discover your pages, named elements, and defined events, so questions map to your real product vocabulary instead of guessed selectors.\n\n(4) Analyze conversion.\nRead your saved funnels and measure where users drop and why.\n\n(5) Back every number with sessions.\nRetrieve the sessions and events behind any metric, including frustration signals like rage clicks and dead clicks.\n\n(6) Read sessions at scale.\nAsk Codex and ChatGPT to review dozens of sessions behind a drop-off or an alert and report the patterns, including failures that never reached your error tracker.\n\nTeams use it to let PMs, support, and engineers self-serve answers that used to queue behind an analyst, and to settle \"why did this number move?\" with evidence instead of theories."
AFTER
"See what actually happened behind every number.\n\nFullstory MCP gives ChatGPT and Codex both halves of product investigation: analytics to size a problem and session replay to see why it happened. Ask where checkout is leaking, why a metric moved, or how to reproduce a bug. It discovers your pages and elements, computes the metric or funnel, and opens the sessions behind the drop. Every answer links to Fullstory, so you can check the work in one click.\n\nFullstory shows what users experienced - including silent failures that never threw an error - so \"conversion dropped 12%\" becomes \"the coupon field fails 5x more for logged-out users.\"\n\nBecause every answer links to the proof, you can give ChatGPT or Codex a standing job. Set up a scheduled task once, and it reports back with the sessions attached: an hourly watch for silent failures, automatic bug scoping on new tickets, or a Monday ranking of the fixes that would recover the most users.\n\nWhat ChatGPT and Codex can do:\n\n(1) Map a session, then zoom to the moment. Read a compact timeline first, then jump to the timestamp where it broke to pull the screenshot and page DOM from before and after - keeping context light.\n\n(2) Review sessions at scale. Because sessions load as compact structured data, read dozens of replays in one run and surface why users gave up.\n\n(3) Build segments, metrics, and funnels in your own words. Discover your pages, named elements, and events so questions map to your product.\n\n(4) Compute on demand. Run counts, rates, trends, funnels, journeys, and friction breakdowns by device, browser, geography, or user property.\n\nFullstory autocaptures clicks, page views, console and network errors, and frustration signals (rage and dead clicks), so many questions need no instrumentation at all."

changed /release/id

BEFORE
"pluginrel_acbb19f0b47c81919932e8bd6c646c5e"
AFTER
"pluginrel_98053c07633081918daf25836cead2e5"

changed /release/interface/composer_icon_dark_url

BEFORE
"https://files.openai.com/content?id=file_0000000050e881f5919e8ac6f7238843"
AFTER
null

changed /release/interface/composer_icon_url

BEFORE
"https://files.openai.com/content?id=file_0000000050e881f5919e8ac6f7238843"
AFTER
"https://files.openai.com/content?id=file_00000000e24c81f585368607ebb445dd"

changed /release/interface/default_prompt

BEFORE
"Find the page with the highest rage-click rate in the past 7 days. Pull 3 sessions and walk me through what happened."
AFTER
"Every hour, find checkout sessions with frustration but no logged error, and flag anything new with a session link."

changed /release/interface/default_prompts

BEFORE
[
  "Find the page with the highest rage-click rate in the past 7 days. Pull 3 sessions and walk me through what happened.",
  "Build a segment of users who hit a console error this week, then pull two of their sessions and summarize what happened.",
  "Which users were highly active 60 days ago but haven't logged in for 30 days? Pull 5 sessions for their last week of activity."
]
AFTER
[
  "Every hour, find checkout sessions with frustration but no logged error, and flag anything new with a session link.",
  "Find a session with a console error this week. Show me the screen where it broke, then count who else hit it.",
  "Find the page with the most rage clicks this week. Open 3 sessions and show me the screen where users got stuck."
]

changed /release/interface/logo_url

BEFORE
"https://files.openai.com/content?id=file_000000009d688243b5c71611d1c2f576"
AFTER
"https://files.openai.com/content?id=file_00000000aa5481f5a7ccbddac6f3a897"

changed /release/interface/logo_url_dark

BEFORE
"https://files.openai.com/content?id=file_000000009d688243b5c71611d1c2f576"
AFTER
null

changed /release/interface/long_description

BEFORE
"Ask about your product analytics, and get answers you can verify.\n\nFullstory MCP connects Codex and ChatGPT to your product's behavioral data. Ask how many users adopted a feature, where checkout is leaking, or whether yesterday's release broke something, and Codex and ChatGPT build the segment, computes the metric, and explains the result with a link back to Fullstory.\n\nAnalytics connectors can give you numbers. Fullstory shows you what happened. Metrics unpack into real session replay, so \"conversion dropped 12%\" becomes \"the coupon field errors for logged-out users.\"\n\nAnd because Fullstory autocaptures clicks, page views, errors, and frustration signals, many questions need no instrumentation at all. Ask about rage clicks on pricing or errors in checkout without shipping a single event.\n\nWhat Codex and ChatGPT can do:\n(1) Build and save segments and metrics.\nDescribe the users or behavior you care about in your own words; Codex and ChatGPT create segments or metrics in Fullstory where your whole team can reuse it.\n\n(2) Compute on demand.\n Counts, rates, trends, and breakdowns by device, browser, geography, or user property, over any time window.\n\n(3) Speak your product's language. \nCodex and ChatGPT discover your pages, named elements, and defined events, so questions map to your real product vocabulary instead of guessed selectors.\n\n(4) Analyze conversion.\nRead your saved funnels and measure where users drop and why.\n\n(5) Back every number with sessions.\nRetrieve the sessions and events behind any metric, including frustration signals like rage clicks and dead clicks.\n\n(6) Read sessions at scale.\nAsk Codex and ChatGPT to review dozens of sessions behind a drop-off or an alert and report the patterns, including failures that never reached your error tracker.\n\nTeams use it to let PMs, support, and engineers self-serve answers that used to queue behind an analyst, and to settle \"why did this number move?\" with evidence instead of theories."
AFTER
"See what actually happened behind every number.\n\nFullstory MCP gives ChatGPT and Codex both halves of product investigation: analytics to size a problem and session replay to see why it happened. Ask where checkout is leaking, why a metric moved, or how to reproduce a bug. It discovers your pages and elements, computes the metric or funnel, and opens the sessions behind the drop. Every answer links to Fullstory, so you can check the work in one click.\n\nFullstory shows what users experienced - including silent failures that never threw an error - so \"conversion dropped 12%\" becomes \"the coupon field fails 5x more for logged-out users.\"\n\nBecause every answer links to the proof, you can give ChatGPT or Codex a standing job. Set up a scheduled task once, and it reports back with the sessions attached: an hourly watch for silent failures, automatic bug scoping on new tickets, or a Monday ranking of the fixes that would recover the most users.\n\nWhat ChatGPT and Codex can do:\n\n(1) Map a session, then zoom to the moment. Read a compact timeline first, then jump to the timestamp where it broke to pull the screenshot and page DOM from before and after - keeping context light.\n\n(2) Review sessions at scale. Because sessions load as compact structured data, read dozens of replays in one run and surface why users gave up.\n\n(3) Build segments, metrics, and funnels in your own words. Discover your pages, named elements, and events so questions map to your product.\n\n(4) Compute on demand. Run counts, rates, trends, funnels, journeys, and friction breakdowns by device, browser, geography, or user property.\n\nFullstory autocaptures clicks, page views, console and network errors, and frustration signals (rage and dead clicks), so many questions need no instrumentation at all."

changed /release/skills

BEFORE
[
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    "description": "How to structure A vs B comparisons in Fullstory — when to use dimensionality (event/session properties) vs separate segments (user-level properties), and why the distinction matters for correctness.",
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      "display_name": "comparisons",
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      "short_description": "How to structure A vs B comparisons in Fullstory — when to use dimensionality (event/session properties) vs separate segments (user-level properties), and why the distinction matters for correctness."
    },
    "name": "comparisons",
    "plugin_release_skill_id": "pluginrsk_6ab55227f4508191b04edc40a28973af"
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  {
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      "short_description": "Fullstory analytics workflow. Use when answering a question that requires measuring user behavior — counts, rates, trends, breakdowns, or cohort comparisons. Builds segments and metrics, computes results, then investigates sessions to explain what the numbers mean."
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    "interface": {
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      "default_prompt": null,
      "display_name": "session-review",
      "icon_large_url": null,
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      "short_description": "Use when diagnosing user-reported issues, investigating bugs, analyzing user behavior, or validating UI correctness using Fullstory session recordings."
    },
    "name": "session-review",
    "plugin_release_skill_id": "pluginrsk_6ab550eebc18819197ef5b621494242a"
  },
  {
    "description": "Finds product improvement ideas a team has not already considered. Searches the metrics, segments and funnels the team already built, builds the ones that are missing, measures how many users each idea affects against a baseline, and pulls session replays as evidence. Returns 3 to 5 ranked proposals, each with what is new about it, a user count with its baseline, and replay links. Use when someone asks what to build next, how to improve a product area or flow, what the team is missing, for data-backed product ideas or proposals, or what changed after a release. If the user only wants the list of Fullstory Opportunities, call the Opportunities tools directly instead. For counts, rates and trends use general-analysis, for A vs B mechanics use comparisons, and for one session use session-review.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "idea-generation",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "heart",
      "short_description": "Finds product improvement ideas a team has not already considered. Searches the metrics, segments and funnels the team already built, builds the ones that are missing, measures how many users each idea affects against a baseline, and pulls session replays as evidence. Returns 3 to 5 ranked proposals, each with what is new about it, a user count with its baseline, and replay links. Use when someone asks what to build next, how to improve a product area or flow, what the team is missing, for data-backed product ideas or proposals, or what changed after a release. If the user only wants the list of Fullstory Opportunities, call the Opportunities tools directly instead. For counts, rates and trends use general-analysis, for A vs B mechanics use comparisons, and for one session use session-review."
    },
    "name": "idea-generation",
    "plugin_release_skill_id": "pluginrsk_6ab55215e45c8191b167b75af0c2aab0"
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AFTER
[
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    "description": "How to structure A vs B comparisons in Fullstory — when to use dimensionality (event/session properties) vs separate segments (user-level properties), and why the distinction matters for correctness.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "comparisons",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "chart",
      "short_description": "How to structure A vs B comparisons in Fullstory — when to use dimensionality (event/session properties) vs separate segments (user-level properties), and why the distinction matters for correctness."
    },
    "name": "comparisons",
    "plugin_release_skill_id": "pluginrsk_6ae49e67d5188191a102cedfeb2bb031"
  },
  {
    "description": "Finds where users struggle on a page or flow and whether a suspected problem is real. Ranks Fullstory's frustration and error signals, separates real problems from buttons people are meant to click many times, measures how many users each problem affects, and uses session replays to show what happened. Use when someone asks where users are struggling, what is broken on a page or flow, for top pain points or a friction audit, why users give up at a step, whether a rage click is real, whether pages feel slow, whether people keep retrying, re-typing or going back and forth, whether a mobile app keeps crashing, or whether a problem from support tickets is widespread or affects only a few people.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "friction-analysis",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "radar",
      "short_description": "Finds where users struggle on a page or flow and whether a suspected problem is real. Ranks Fullstory's frustration and error signals, separates real problems from buttons people are meant to click many times, measures how many users each problem affects, and uses session replays to show what happened. Use when someone asks where users are struggling, what is broken on a page or flow, for top pain points or a friction audit, why users give up at a step, whether a rage click is real, whether pages feel slow, whether people keep retrying, re-typing or going back and forth, whether a mobile app keeps crashing, or whether a problem from support tickets is widespread or affects only a few people."
    },
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    "plugin_release_skill_id": "pluginrsk_6ae49e686ef88191850c713cb6c7cec8"
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  {
    "description": "Fullstory analytics workflow — the default way to answer any question about user behavior. Use whenever someone asks how many, what percentage, which pages, whether something is trending, where users drop off, what users did before or after an action, or how two cohorts compare — and also when they describe a business question that needs numbers behind it without ever saying \"metric\", \"funnel\", or \"segment\". Covers building and computing metrics, segments, funnels, and journeys, verifying that what got built is what was actually asked for, and pulling sessions to explain why the numbers look the way they do. Not for reviewing one specific session, or for setting up capture and instrumentation.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "general-analysis",
      "icon_large_url": null,
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      "short_description": "Fullstory analytics workflow — the default way to answer any question about user behavior. Use whenever someone asks how many, what percentage, which pages, whether something is trending, where users drop off, what users did before or after an action, or how two cohorts compare — and also when they describe a business question that needs numbers behind it without ever saying \"metric\", \"funnel\", or \"segment\". Covers building and computing metrics, segments, funnels, and journeys, verifying that what got built is what was actually asked for, and pulling sessions to explain why the numbers look the way they do. Not for reviewing one specific session, or for setting up capture and instrumentation."
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      "display_name": "idea-generation",
      "icon_large_url": null,
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      "iconography": "chart",
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    },
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changed /release/version

BEFORE
"3.0.0"
AFTER
"3.1.0"
Full snapshot data
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  "connector_id": "asdk_app_6a9a1ebb11fc81919ef2aa7231b9b068",
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    "description": "See what actually happened behind every number.\n\nFullstory MCP gives ChatGPT and Codex both halves of product investigation: analytics to size a problem and session replay to see why it happened. Ask where checkout is leaking, why a metric moved, or how to reproduce a bug. It discovers your pages and elements, computes the metric or funnel, and opens the sessions behind the drop. Every answer links to Fullstory, so you can check the work in one click.\n\nFullstory shows what users experienced - including silent failures that never threw an error - so \"conversion dropped 12%\" becomes \"the coupon field fails 5x more for logged-out users.\"\n\nBecause every answer links to the proof, you can give ChatGPT or Codex a standing job. Set up a scheduled task once, and it reports back with the sessions attached: an hourly watch for silent failures, automatic bug scoping on new tickets, or a Monday ranking of the fixes that would recover the most users.\n\nWhat ChatGPT and Codex can do:\n\n(1) Map a session, then zoom to the moment. Read a compact timeline first, then jump to the timestamp where it broke to pull the screenshot and page DOM from before and after - keeping context light.\n\n(2) Review sessions at scale. Because sessions load as compact structured data, read dozens of replays in one run and surface why users gave up.\n\n(3) Build segments, metrics, and funnels in your own words. Discover your pages, named elements, and events so questions map to your product.\n\n(4) Compute on demand. Run counts, rates, trends, funnels, journeys, and friction breakdowns by device, browser, geography, or user property.\n\nFullstory autocaptures clicks, page views, console and network errors, and frustration signals (rage and dead clicks), so many questions need no instrumentation at all.",
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      "screenshot_urls": [],
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          "default_prompt": null,
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        "plugin_release_skill_id": "pluginrsk_6ae49e67d5188191a102cedfeb2bb031"
      },
      {
        "description": "Finds where users struggle on a page or flow and whether a suspected problem is real. Ranks Fullstory's frustration and error signals, separates real problems from buttons people are meant to click many times, measures how many users each problem affects, and uses session replays to show what happened. Use when someone asks where users are struggling, what is broken on a page or flow, for top pain points or a friction audit, why users give up at a step, whether a rage click is real, whether pages feel slow, whether people keep retrying, re-typing or going back and forth, whether a mobile app keeps crashing, or whether a problem from support tickets is widespread or affects only a few people.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "friction-analysis",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "radar",
          "short_description": "Finds where users struggle on a page or flow and whether a suspected problem is real. Ranks Fullstory's frustration and error signals, separates real problems from buttons people are meant to click many times, measures how many users each problem affects, and uses session replays to show what happened. Use when someone asks where users are struggling, what is broken on a page or flow, for top pain points or a friction audit, why users give up at a step, whether a rage click is real, whether pages feel slow, whether people keep retrying, re-typing or going back and forth, whether a mobile app keeps crashing, or whether a problem from support tickets is widespread or affects only a few people."
        },
        "name": "friction-analysis",
        "plugin_release_skill_id": "pluginrsk_6ae49e686ef88191850c713cb6c7cec8"
      },
      {
        "description": "Fullstory analytics workflow — the default way to answer any question about user behavior. Use whenever someone asks how many, what percentage, which pages, whether something is trending, where users drop off, what users did before or after an action, or how two cohorts compare — and also when they describe a business question that needs numbers behind it without ever saying \"metric\", \"funnel\", or \"segment\". Covers building and computing metrics, segments, funnels, and journeys, verifying that what got built is what was actually asked for, and pulling sessions to explain why the numbers look the way they do. Not for reviewing one specific session, or for setting up capture and instrumentation.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "general-analysis",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "chart",
          "short_description": "Fullstory analytics workflow — the default way to answer any question about user behavior. Use whenever someone asks how many, what percentage, which pages, whether something is trending, where users drop off, what users did before or after an action, or how two cohorts compare — and also when they describe a business question that needs numbers behind it without ever saying \"metric\", \"funnel\", or \"segment\". Covers building and computing metrics, segments, funnels, and journeys, verifying that what got built is what was actually asked for, and pulling sessions to explain why the numbers look the way they do. Not for reviewing one specific session, or for setting up capture and instrumentation."
        },
        "name": "general-analysis",
        "plugin_release_skill_id": "pluginrsk_6ae49e67f6dc8191bcced53771183675"
      },
      {
        "description": "Finds product improvement ideas a team has not already considered. Searches the metrics, segments and funnels the team already built, builds the ones that are missing, measures how many users each idea affects against a baseline, and pulls session replays as evidence. Returns 3 to 5 ranked proposals, each with what is new about it, a user count with its baseline, and replay links. Use when someone asks what to build next, how to improve a product area or flow, what the team is missing, for data-backed product ideas or proposals, or what changed after a release. If the user only wants the list of Fullstory Opportunities, call the Opportunities tools directly instead. For counts, rates and trends use general-analysis, for A vs B mechanics use comparisons, and for one session use session-review.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "idea-generation",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "chart",
          "short_description": "Finds product improvement ideas a team has not already considered. Searches the metrics, segments and funnels the team already built, builds the ones that are missing, measures how many users each idea affects against a baseline, and pulls session replays as evidence. Returns 3 to 5 ranked proposals, each with what is new about it, a user count with its baseline, and replay links. Use when someone asks what to build next, how to improve a product area or flow, what the team is missing, for data-backed product ideas or proposals, or what changed after a release. If the user only wants the list of Fullstory Opportunities, call the Opportunities tools directly instead. For counts, rates and trends use general-analysis, for A vs B mechanics use comparisons, and for one session use session-review."
        },
        "name": "idea-generation",
        "plugin_release_skill_id": "pluginrsk_6ae49e67ae688191b92db14d347c1124"
      },
      {
        "description": "Use when diagnosing user-reported issues, investigating bugs, analyzing user behavior, or validating UI correctness using Fullstory session recordings.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "session-review",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "radar",
          "short_description": "Use when diagnosing user-reported issues, investigating bugs, analyzing user behavior, or validating UI correctness using Fullstory session recordings."
        },
        "name": "session-review",
        "plugin_release_skill_id": "pluginrsk_6ae49e689fe08191b396f27e99831ded"
      }
    ],
    "version": "3.1.0"
  },
  "scope": "GLOBAL",
  "status": "ENABLED"
}

SHA-256 of public snapshot: de8b5b3ab334d6e5f562620a9d9b5afc16bbafa7426bb495399808542a92e246