Phase 4: Nightly extraction -- automated workflow crystallisation via ADF #696

Description

@AlexMikhalev

Parent Epic

Part of #692 (Operational Skill Store)

Summary

Add a new Core-tier ADF agent that runs nightly to automatically discover and crystallise successful multi-step workflows from session history into reusable procedures. This closes the loop -- instead of manual capture-success, the system learns from its own history.

What Changes

New ADF agent: procedure-crystalliser

Tier: Core (cron-scheduled, runs nightly)
CLI: terraphim-agent (uses sessions + learn subcommands)

Nightly workflow:

  1. terraphim-agent sessions import -- refresh session cache
  2. Scan recent sessions for successful multi-step sequences (exit code 0 chains)
  3. Classify sequences by task type using KG concept matching
  4. Deduplicate against existing procedures (Aho-Corasick)
  5. Store novel sequences as new CapturedProcedure entries
  6. Run learn health across all procedures
  7. Generate daily report: new procedures, degraded procedures, statistics

Orchestrator config addition

[[agents]]
name = "procedure-crystalliser"tier = "Core"cli = "terraphim-agent"schedule = "0 2 * * *"# 02:00 dailyworking_dir = "/home/alex/terraphim-ai"

Session extraction API in terraphim_sessions

pubasyncfnextract_successful_sequences(since:DateTime<Utc>,min_steps:usize,) -> Vec<CommandSequence>;

Affected Crates

  • terraphim_orchestrator (new agent config)
  • terraphim_sessions (extraction API)
  • terraphim_agent (orchestration entry point)

Dependencies

Acceptance Criteria

  • procedure-crystalliser agent defined in orchestrator config
  • Session extraction API returns successful command sequences
  • KG-based task type classification for discovered sequences
  • Aho-Corasick deduplication prevents storing known procedures
  • Daily report generated with new/degraded procedure counts
  • Integration test with sample session data

Activity

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      , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all
       blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
      }
      } catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
      })();
      (function(){
      try {
      var __m = "github.com";
      var __re = new RegExp('^' + "github\\.com" + '
      
      Skip to content

      Phase 4: Nightly extraction -- automated workflow crystallisation via ADF #696

      Description

      @AlexMikhalev

      Parent Epic

      Part of #692 (Operational Skill Store)

      Summary

      Add a new Core-tier ADF agent that runs nightly to automatically discover and crystallise successful multi-step workflows from session history into reusable procedures. This closes the loop -- instead of manual capture-success, the system learns from its own history.

      What Changes

      New ADF agent: procedure-crystalliser

      Tier: Core (cron-scheduled, runs nightly)
      CLI: terraphim-agent (uses sessions + learn subcommands)

      Nightly workflow:

      1. terraphim-agent sessions import -- refresh session cache
      2. Scan recent sessions for successful multi-step sequences (exit code 0 chains)
      3. Classify sequences by task type using KG concept matching
      4. Deduplicate against existing procedures (Aho-Corasick)
      5. Store novel sequences as new CapturedProcedure entries
      6. Run learn health across all procedures
      7. Generate daily report: new procedures, degraded procedures, statistics

      Orchestrator config addition

      [[agents]]
      name = "procedure-crystalliser"tier = "Core"cli = "terraphim-agent"schedule = "0 2 * * *"# 02:00 dailyworking_dir = "/home/alex/terraphim-ai"

      Session extraction API in terraphim_sessions

      pubasyncfnextract_successful_sequences(since:DateTime<Utc>,min_steps:usize,) -> Vec<CommandSequence>;

      Affected Crates

      • terraphim_orchestrator (new agent config)
      • terraphim_sessions (extraction API)
      • terraphim_agent (orchestration entry point)

      Dependencies

      Acceptance Criteria

      • procedure-crystalliser agent defined in orchestrator config
      • Session extraction API returns successful command sequences
      • KG-based task type classification for discovered sequences
      • Aho-Corasick deduplication prevents storing known procedures
      • Daily report generated with new/degraded procedure counts
      • Integration test with sample session data

      Activity

      Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

      Metadata

      Metadata

      Assignees

      No one assigned

        Labels

        enhancementNew feature or request

        Type

        No type

        Projects

        No projects

          Milestone

          No milestone

          Relationships

          None yet

          Development

          No branches or pull requests

          Issue actions

          , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
          Skip to content

          Phase 4: Nightly extraction -- automated workflow crystallisation via ADF #696

          Description

          @AlexMikhalev

          Parent Epic

          Part of #692 (Operational Skill Store)

          Summary

          Add a new Core-tier ADF agent that runs nightly to automatically discover and crystallise successful multi-step workflows from session history into reusable procedures. This closes the loop -- instead of manual capture-success, the system learns from its own history.

          What Changes

          New ADF agent: procedure-crystalliser

          Tier: Core (cron-scheduled, runs nightly)
          CLI: terraphim-agent (uses sessions + learn subcommands)

          Nightly workflow:

          1. terraphim-agent sessions import -- refresh session cache
          2. Scan recent sessions for successful multi-step sequences (exit code 0 chains)
          3. Classify sequences by task type using KG concept matching
          4. Deduplicate against existing procedures (Aho-Corasick)
          5. Store novel sequences as new CapturedProcedure entries
          6. Run learn health across all procedures
          7. Generate daily report: new procedures, degraded procedures, statistics

          Orchestrator config addition

          [[agents]]
          name = "procedure-crystalliser"tier = "Core"cli = "terraphim-agent"schedule = "0 2 * * *"# 02:00 dailyworking_dir = "/home/alex/terraphim-ai"

          Session extraction API in terraphim_sessions

          pubasyncfnextract_successful_sequences(since:DateTime<Utc>,min_steps:usize,) -> Vec<CommandSequence>;

          Affected Crates

          • terraphim_orchestrator (new agent config)
          • terraphim_sessions (extraction API)
          • terraphim_agent (orchestration entry point)

          Dependencies

          Acceptance Criteria

          • procedure-crystalliser agent defined in orchestrator config
          • Session extraction API returns successful command sequences
          • KG-based task type classification for discovered sequences
          • Aho-Corasick deduplication prevents storing known procedures
          • Daily report generated with new/degraded procedure counts
          • Integration test with sample session data

          Activity

          Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

          Metadata

          Metadata

          Assignees

          No one assigned

            Labels

            enhancementNew feature or request

            Type

            No type

            Projects

            No projects

              Milestone

              No milestone

              Relationships

              None yet

              Development

              No branches or pull requests

              Issue actions

              , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
              Skip to content

              Phase 4: Nightly extraction -- automated workflow crystallisation via ADF #696

              Description

              @AlexMikhalev

              Parent Epic

              Part of #692 (Operational Skill Store)

              Summary

              Add a new Core-tier ADF agent that runs nightly to automatically discover and crystallise successful multi-step workflows from session history into reusable procedures. This closes the loop -- instead of manual capture-success, the system learns from its own history.

              What Changes

              New ADF agent: procedure-crystalliser

              Tier: Core (cron-scheduled, runs nightly)
              CLI: terraphim-agent (uses sessions + learn subcommands)

              Nightly workflow:

              1. terraphim-agent sessions import -- refresh session cache
              2. Scan recent sessions for successful multi-step sequences (exit code 0 chains)
              3. Classify sequences by task type using KG concept matching
              4. Deduplicate against existing procedures (Aho-Corasick)
              5. Store novel sequences as new CapturedProcedure entries
              6. Run learn health across all procedures
              7. Generate daily report: new procedures, degraded procedures, statistics

              Orchestrator config addition

              [[agents]]
              name = "procedure-crystalliser"tier = "Core"cli = "terraphim-agent"schedule = "0 2 * * *"# 02:00 dailyworking_dir = "/home/alex/terraphim-ai"

              Session extraction API in terraphim_sessions

              pubasyncfnextract_successful_sequences(since:DateTime<Utc>,min_steps:usize,) -> Vec<CommandSequence>;

              Affected Crates

              • terraphim_orchestrator (new agent config)
              • terraphim_sessions (extraction API)
              • terraphim_agent (orchestration entry point)

              Dependencies

              Acceptance Criteria

              • procedure-crystalliser agent defined in orchestrator config
              • Session extraction API returns successful command sequences
              • KG-based task type classification for discovered sequences
              • Aho-Corasick deduplication prevents storing known procedures
              • Daily report generated with new/degraded procedure counts
              • Integration test with sample session data

              Activity

              Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

              Metadata

              Metadata

              Assignees

              No one assigned

                Labels

                enhancementNew feature or request

                Type

                No type

                Projects

                No projects

                  Milestone

                  No milestone

                  Relationships

                  None yet

                  Development

                  No branches or pull requests

                  Issue actions

                  , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
                  Skip to content

                  Phase 4: Nightly extraction -- automated workflow crystallisation via ADF #696

                  Description

                  @AlexMikhalev

                  Parent Epic

                  Part of #692 (Operational Skill Store)

                  Summary

                  Add a new Core-tier ADF agent that runs nightly to automatically discover and crystallise successful multi-step workflows from session history into reusable procedures. This closes the loop -- instead of manual capture-success, the system learns from its own history.

                  What Changes

                  New ADF agent: procedure-crystalliser

                  Tier: Core (cron-scheduled, runs nightly)
                  CLI: terraphim-agent (uses sessions + learn subcommands)

                  Nightly workflow:

                  1. terraphim-agent sessions import -- refresh session cache
                  2. Scan recent sessions for successful multi-step sequences (exit code 0 chains)
                  3. Classify sequences by task type using KG concept matching
                  4. Deduplicate against existing procedures (Aho-Corasick)
                  5. Store novel sequences as new CapturedProcedure entries
                  6. Run learn health across all procedures
                  7. Generate daily report: new procedures, degraded procedures, statistics

                  Orchestrator config addition

                  [[agents]]
                  name = "procedure-crystalliser"tier = "Core"cli = "terraphim-agent"schedule = "0 2 * * *"# 02:00 dailyworking_dir = "/home/alex/terraphim-ai"

                  Session extraction API in terraphim_sessions

                  pubasyncfnextract_successful_sequences(since:DateTime<Utc>,min_steps:usize,) -> Vec<CommandSequence>;

                  Affected Crates

                  • terraphim_orchestrator (new agent config)
                  • terraphim_sessions (extraction API)
                  • terraphim_agent (orchestration entry point)

                  Dependencies

                  Acceptance Criteria

                  • procedure-crystalliser agent defined in orchestrator config
                  • Session extraction API returns successful command sequences
                  • KG-based task type classification for discovered sequences
                  • Aho-Corasick deduplication prevents storing known procedures
                  • Daily report generated with new/degraded procedure counts
                  • Integration test with sample session data

                  Activity

                  Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

                  Metadata

                  Metadata

                  Assignees

                  No one assigned

                    Labels

                    enhancementNew feature or request

                    Type

                    No type

                    Projects

                    No projects

                      Milestone

                      No milestone

                      Relationships

                      None yet

                      Development

                      No branches or pull requests

                      Issue actions

                      , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
                      Skip to content

                      Phase 4: Nightly extraction -- automated workflow crystallisation via ADF #696

                      Description

                      @AlexMikhalev

                      Parent Epic

                      Part of #692 (Operational Skill Store)

                      Summary

                      Add a new Core-tier ADF agent that runs nightly to automatically discover and crystallise successful multi-step workflows from session history into reusable procedures. This closes the loop -- instead of manual capture-success, the system learns from its own history.

                      What Changes

                      New ADF agent: procedure-crystalliser

                      Tier: Core (cron-scheduled, runs nightly)
                      CLI: terraphim-agent (uses sessions + learn subcommands)

                      Nightly workflow:

                      1. terraphim-agent sessions import -- refresh session cache
                      2. Scan recent sessions for successful multi-step sequences (exit code 0 chains)
                      3. Classify sequences by task type using KG concept matching
                      4. Deduplicate against existing procedures (Aho-Corasick)
                      5. Store novel sequences as new CapturedProcedure entries
                      6. Run learn health across all procedures
                      7. Generate daily report: new procedures, degraded procedures, statistics

                      Orchestrator config addition

                      [[agents]]
                      name = "procedure-crystalliser"tier = "Core"cli = "terraphim-agent"schedule = "0 2 * * *"# 02:00 dailyworking_dir = "/home/alex/terraphim-ai"

                      Session extraction API in terraphim_sessions

                      pubasyncfnextract_successful_sequences(since:DateTime<Utc>,min_steps:usize,) -> Vec<CommandSequence>;

                      Affected Crates

                      • terraphim_orchestrator (new agent config)
                      • terraphim_sessions (extraction API)
                      • terraphim_agent (orchestration entry point)

                      Dependencies

                      Acceptance Criteria

                      • procedure-crystalliser agent defined in orchestrator config
                      • Session extraction API returns successful command sequences
                      • KG-based task type classification for discovered sequences
                      • Aho-Corasick deduplication prevents storing known procedures
                      • Daily report generated with new/degraded procedure counts
                      • Integration test with sample session data

                      Activity

                      Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

                      Metadata

                      Metadata

                      Assignees

                      No one assigned

                        Labels

                        enhancementNew feature or request

                        Type

                        No type

                        Projects

                        No projects

                          Milestone

                          No milestone

                          Relationships

                          None yet

                          Development

                          No branches or pull requests

                          Issue actions

                          , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
                          Skip to content

                          Phase 4: Nightly extraction -- automated workflow crystallisation via ADF #696

                          Description

                          @AlexMikhalev

                          Parent Epic

                          Part of #692 (Operational Skill Store)

                          Summary

                          Add a new Core-tier ADF agent that runs nightly to automatically discover and crystallise successful multi-step workflows from session history into reusable procedures. This closes the loop -- instead of manual capture-success, the system learns from its own history.

                          What Changes

                          New ADF agent: procedure-crystalliser

                          Tier: Core (cron-scheduled, runs nightly)
                          CLI: terraphim-agent (uses sessions + learn subcommands)

                          Nightly workflow:

                          1. terraphim-agent sessions import -- refresh session cache
                          2. Scan recent sessions for successful multi-step sequences (exit code 0 chains)
                          3. Classify sequences by task type using KG concept matching
                          4. Deduplicate against existing procedures (Aho-Corasick)
                          5. Store novel sequences as new CapturedProcedure entries
                          6. Run learn health across all procedures
                          7. Generate daily report: new procedures, degraded procedures, statistics

                          Orchestrator config addition

                          [[agents]]
                          name = "procedure-crystalliser"tier = "Core"cli = "terraphim-agent"schedule = "0 2 * * *"# 02:00 dailyworking_dir = "/home/alex/terraphim-ai"

                          Session extraction API in terraphim_sessions

                          pubasyncfnextract_successful_sequences(since:DateTime<Utc>,min_steps:usize,) -> Vec<CommandSequence>;

                          Affected Crates

                          • terraphim_orchestrator (new agent config)
                          • terraphim_sessions (extraction API)
                          • terraphim_agent (orchestration entry point)

                          Dependencies

                          Acceptance Criteria

                          • procedure-crystalliser agent defined in orchestrator config
                          • Session extraction API returns successful command sequences
                          • KG-based task type classification for discovered sequences
                          • Aho-Corasick deduplication prevents storing known procedures
                          • Daily report generated with new/degraded procedure counts
                          • Integration test with sample session data

                          Activity

                          Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

                          Metadata

                          Metadata

                          Assignees

                          No one assigned

                            Labels

                            enhancementNew feature or request

                            Type

                            No type

                            Projects

                            No projects

                              Milestone

                              No milestone

                              Relationships

                              None yet

                              Development

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                              , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
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                              Phase 4: Nightly extraction -- automated workflow crystallisation via ADF #696

                              Description

                              @AlexMikhalev

                              Parent Epic

                              Part of #692 (Operational Skill Store)

                              Summary

                              Add a new Core-tier ADF agent that runs nightly to automatically discover and crystallise successful multi-step workflows from session history into reusable procedures. This closes the loop -- instead of manual capture-success, the system learns from its own history.

                              What Changes

                              New ADF agent: procedure-crystalliser

                              Tier: Core (cron-scheduled, runs nightly)
                              CLI: terraphim-agent (uses sessions + learn subcommands)

                              Nightly workflow:

                              1. terraphim-agent sessions import -- refresh session cache
                              2. Scan recent sessions for successful multi-step sequences (exit code 0 chains)
                              3. Classify sequences by task type using KG concept matching
                              4. Deduplicate against existing procedures (Aho-Corasick)
                              5. Store novel sequences as new CapturedProcedure entries
                              6. Run learn health across all procedures
                              7. Generate daily report: new procedures, degraded procedures, statistics

                              Orchestrator config addition

                              [[agents]]
                              name = "procedure-crystalliser"tier = "Core"cli = "terraphim-agent"schedule = "0 2 * * *"# 02:00 dailyworking_dir = "/home/alex/terraphim-ai"

                              Session extraction API in terraphim_sessions

                              pubasyncfnextract_successful_sequences(since:DateTime<Utc>,min_steps:usize,) -> Vec<CommandSequence>;

                              Affected Crates

                              • terraphim_orchestrator (new agent config)
                              • terraphim_sessions (extraction API)
                              • terraphim_agent (orchestration entry point)

                              Dependencies

                              Acceptance Criteria

                              • procedure-crystalliser agent defined in orchestrator config
                              • Session extraction API returns successful command sequences
                              • KG-based task type classification for discovered sequences
                              • Aho-Corasick deduplication prevents storing known procedures
                              • Daily report generated with new/degraded procedure counts
                              • Integration test with sample session data

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