From 9fa065908d21cc65b1f1f9b96a2905c63ddd394a Mon Sep 17 00:00:00 2001 From: Hitonabi Date: Wed, 24 Jun 2026 18:08:31 +0200 Subject: [PATCH] =?UTF-8?q?feat(v9):=20Phase=2010=20Schritt=201=20?= =?UTF-8?q?=E2=80=94=20Modell-Capabilities=20aus=20GGUF-Metadaten?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - model_caps.py: dependency-freier GGUF-Header-Reader (architecture, expert_count, context_length, parameter_count) — offline/authoritativ, bricht vor dem Tokenizer-Array ab. Leitet Tag-Set ab: MoE(+aktive B), Tools (yes via --jinja/Template | likely via Familie | no), Vision, Coder, Reasoning, Embedding, native ctx/params. Familien-Liste nur Fallback. - models.py status(): meta.capabilities ergänzt. - ModelsPanel: "KANN"-Spalte zeigt jetzt Capability-Chips statt Text/Code/Bild. Co-Authored-By: Claude Opus 4.8 --- frontend/src/panels/ModelsPanel.svelte | 21 ++-- model_caps.py | 151 +++++++++++++++++++++++++ routers/models.py | 6 + static/dist/main.js | 50 ++++---- 4 files changed, 195 insertions(+), 33 deletions(-) create mode 100644 model_caps.py diff --git a/frontend/src/panels/ModelsPanel.svelte b/frontend/src/panels/ModelsPanel.svelte index d2077ce..2885ab1 100644 --- a/frontend/src/panels/ModelsPanel.svelte +++ b/frontend/src/panels/ModelsPanel.svelte @@ -94,13 +94,18 @@ } catch (e: any) { toast(e.message, true) } } - function capTags(caps: string[]) { - if (!caps?.length) return '–' - return caps.map((c: string) => - c === 'Code' ? 'Code' - : c === 'Bild' ? 'Bild' - : 'Text' - ).join(' ') + function capChips(cap: any) { + if (!cap) return '–' + const chips: string[] = [] + if (cap.coder) chips.push('Code') + if (cap.vision) chips.push('Bild') + if (cap.reasoning) chips.push('🧠 Reason') + if (cap.moe) chips.push(`🧩 MoE${cap.active_b ? ' ·' + cap.active_b + 'B' : ''}`) + if (cap.tools === 'yes') chips.push('🛠 Tools') + else if (cap.tools === 'likely') chips.push('🛠 Tools?') + if (cap.embedding) chips.push('🔢 Embed') + if (!cap.coder && !cap.vision && !cap.embedding) chips.unshift('Text') + return chips.length ? chips.join(' ') : '–' } function details(meta: any) { @@ -183,7 +188,7 @@ {/each} - {@html capTags(m.meta?.caps)} + {@html capChips(m.meta?.capabilities)} {@html details(m.meta)} {@html badge(m.state, m.download_progress)} {m.port ?? 'auto'} diff --git a/model_caps.py b/model_caps.py new file mode 100644 index 0000000..155491a --- /dev/null +++ b/model_caps.py @@ -0,0 +1,151 @@ +""" +Modell-Capabilities (Phase 10) — EINE Quelle der Wahrheit fuer Modell-Eigenschaften. + +Statt Einzel-Label ("Code"/"Text") ein Satz unabhaengiger Tags, die ein Modell +gleichzeitig tragen kann (MoE + Tools + Reasoning + Coder + Long-Context …). + +Quellen, geschichtet (sicher → Fallback): + 1) GGUF-Metadaten der lokalen Datei (architecture, expert_count, context_length, + parameter_count) — authoritativ & **offline** (kein Netz, passt zu 100%-lokal). + 2) cmd-Flags der llama-swap-Config: `--jinja` = Tool-Template aktiv, `--mmproj` = Vision. + 3) duenner Namens-/Familien-Fallback, wenn Metadaten fehlen. + 4) optional `hf`-Block (HF-API: tags + gguf.chat_template) fuer die Profi-Suche (Schritt 2). + +Tool-Faehigkeit kommt NICHT aus dem Dateinamen, sondern aus `--jinja` (bestaetigt) / +dem Chat-Template (HF) / der Familie (vermutet) → drei Stufen: yes | likely | no. +""" + +import re +import struct + +from hw_math import extract_params_b, extract_active_params_b + +# GGUF-Skalar-Typen → Bytebreite (fuer Skip) +_GGUF_FIXED = {0: 1, 1: 1, 2: 2, 3: 2, 4: 4, 5: 4, 6: 4, 7: 1, 10: 8, 11: 8, 12: 8} + + +def _read_gguf_meta(path: str) -> dict: + """Liest nur den GGUF-Metadaten-Header (architecture/context_length/expert_count/ + parameter_count). Bricht vor dem riesigen Tokenizer-Array ab → schnell, laedt NICHT + das Modell. Robust: gibt {} bei jedem Fehler.""" + out: dict = {} + try: + with open(path, "rb") as f: + if f.read(4) != b"GGUF": + return {} + struct.unpack(" int: return struct.unpack(" int: return struct.unpack(" str: return f.read(ru64()).decode("utf-8", "replace") + + def rval(t: int): + if t == 8: return rstr() + if t == 0: return struct.unpack(" dict: + """Capability-Tag-Set fuer ein Modell. Alle Quellen optional — nutzt, was da ist.""" + low = (name or "").lower() + cmdl = (cmd or "").lower() + hf = hf or {} + + meta = _read_gguf_meta(gguf_path) if gguf_path else {} + arch = str(meta.get("architecture") or hf.get("architecture") or "").lower() + tags = [str(t).lower() for t in (hf.get("tags") or [])] + chat_tpl = str(hf.get("chat_template") or "") + + # --- MoE --- + expert_count = int(meta.get("expert_count") or 0) + moe = ( + expert_count > 1 + or any(a in arch for a in _MOE_ARCH) + or bool(re.search(r"\d+x\d+\.?\d*b", low)) # 8x7B + or bool(re.search(r"a\d+\.?\d*b", low)) # 30B-A3B + ) + active_b = extract_active_params_b(name) + + # --- Params / Kontext --- + pcount = int(meta.get("parameter_count") or 0) + params_b = round(pcount / 1e9, 1) if pcount else extract_params_b(name) + ctx = meta.get("context_length") + if not ctx: + m = re.search(r"-(?:c|-ctx-size)\s+(\d+)", cmdl) + ctx = int(m.group(1)) if m else None + + # --- Tools: yes (bestaetigt) | likely (Familie) | no --- + tool_confirmed = "--jinja" in cmdl or "tool_call" in chat_tpl or "" in chat_tpl + tool_family = any(fam in low for fam in _TOOL_FAMILIES) or "function-calling" in tags + tools = "yes" if tool_confirmed else ("likely" if tool_family else "no") + + vision = "--mmproj" in cmdl or "vl" in arch or "clip" in arch or any(k in low for k in _VISION_KW) + coder = any(k in low for k in _CODE_KW) + reasoning = any(k in low for k in _REASON_KW) or "reasoning" in tags + embedding = "bert" in arch or any(k in low for k in _EMBED_KW) + + return { + "moe": moe, + "active_b": active_b, + "tools": tools, # yes | likely | no + "vision": vision, + "coder": coder, + "reasoning": reasoning, + "embedding": embedding, + "ctx": ctx, + "params_b": params_b or None, + "arch": arch or None, + } diff --git a/routers/models.py b/routers/models.py index ea16e22..6082c80 100644 --- a/routers/models.py +++ b/routers/models.py @@ -20,6 +20,7 @@ from routers.cookbook import hf_file_size from llamaswap import (_swap_get, read_config, write_config, model_id_from_path, set_role_alias, ROLE_IDS) from hw_math import extract_params_b, max_ctx_for, estimate_memory_gb +from model_caps import capabilities import re import os import shutil @@ -90,6 +91,7 @@ def status(): size_bytes = None quant = "" filename = "" + path = "" m_path = re.search(r'-(?:m|-model)\s+([^\s]+)', cmd) if m_path: path = m_path.group(1).replace("'", "").replace('"', '') @@ -137,6 +139,10 @@ def status(): "optimal_ctx": (_oc := (max_ctx_for(_pb, quant or "Q4_K_M", ram_gb) if ram_gb else None)), "peak_ram_gb": round(estimate_memory_gb(_pb, quant or "Q4_K_M", ctx), 1), "peak_ram_optimal_gb": (round(estimate_memory_gb(_pb, quant or "Q4_K_M", _oc), 1) if _oc else None), + "capabilities": capabilities( + name=filename or name, cmd=cmd, + gguf_path=(path if (path and os.path.exists(path)) else ""), + ), } } # Laufende Download-Jobs erkennnen: Modell bekommt state "downloading" + Fortschritt. diff --git a/static/dist/main.js 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Für dich: Upgrades

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Für dich: Upgrades

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  1. Modell holen: Tab Cookbook → Anwendungsfall wählen (z.B. „Coden & Programmieren") → Komplettes Setup installieren. Der Download läuft im Tab Aktivität — einfach warten bis der Job grün wird.
    2. @@ -649,17 +649,17 @@ src/
    Strg+Shift+R (Hard Reload). Wenn das nicht hilft: Tab Server → Mission Control → Neu starten.
    - `]]}],dv=[["LLM / Modell","Die KI selbst — versteht und schreibt Text (wie ChatGPT, nur lokal auf deinem PC). Ein Modell ist eine konkrete Datei, oft auf etwas spezialisiert (Code, Bilder, Chat)."],["GGUF","Das lokal lauffähige Dateiformat. llama-swap lädt nur GGUF — Repos ohne GGUF kann die Engine nicht nutzen."],["Quantisierung (Q4_K_M)","Verkleinert ein Modell (16-Bit → 4-Bit), damit es in den Speicher passt — bei kaum Qualitätsverlust. Q4_K_M ist der bewährte Sweetspot. Für Coding/Reasoning: Q8_0 (nahezu verlustfrei). Wenn Q4_K_M 1 GB zu groß ist: IQ4_XS als Alternative (ähnliche Qualität, etwas kleiner)."],["Kontextfenster","Das Kurzzeitgedächtnis: wie viel Text sich das Modell gleichzeitig merken kann. Größer = mehr Überblick, mehr Speicher, etwas langsamer."],["Token","Die kleinste Einheit die ein Modell verarbeitet — meist 3–4 Zeichen oder ein kurzes Wort. 1 000 Tokens ≈ 750 Wörter. Kontextfenster, Geschwindigkeit und Kosten werden in Tokens gemessen."],["Auto-Swap","llama-swap lädt/entlädt Modelle automatisch — immer nur eins gleichzeitig. Du managst nichts."],["TTL (Time to Live)","Wie lange ein Modell nach der letzten Anfrage im RAM bleibt bevor es entladen wird. Standard: 5 Minuten. ∞ = bleibt dauerhaft geladen (sinnvoll für den Scout)."],["Agent","Ein Programm (OpenCode, Cline, Continue), das das Modell in einer Handlungsschleife laufen lässt — Dateien lesen/ändern, Befehle ausführen, planen, bis die Aufgabe erledigt ist."],["MCP","Model Context Protocol — der Stecker, über den ein Agent zusätzliche Werkzeuge bekommt (Web, Datenbank, GitHub …). MCP = neue Fähigkeiten."],["Skills / Rules","Kurze Textdatei (AGENTS.md, .clinerules …) mit Anweisungen wie der Agent in deinem Projekt arbeiten soll — einmal geschrieben, immer befolgt. Seit 2026 plattformübergreifender Standard: AGENTS.md wird von über 10 Tools nativ gelesen."],["Memory Bank","Clines Konzept für dauerhaften Projekt-Kontext: projectbrief.md (Ziel & Scope), activeContext.md (aktueller Fokus), progress.md (Stand). Wird zu Beginn jeder Aufgabe automatisch gelesen — ähnlich wie AGENTS.md, aber aufgeteilt nach Zweck."],["System-Prompt","Eine unsichtbare Anweisung die vor deiner Nachricht steht und das Verhalten des Modells grundlegend prägt (Sprache, Ton, Rolle). Skills nutzen oft den System-Prompt."],["Vision","Modelle mit Vision-Fähigkeit können Bilder verstehen — Screenshots, Diagramme, Fehlerfotos. Erkennbar am mmproj-Projektor der mitgeladen wird."]];var cv=g('

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    Guide

    Schritt für Schritt erklärt — von der ersten Einrichtung bis zu echten Agenten-Workflows.

    Begriffe kurz erklärt

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    Import aus Claude / Gemini / ChatGPT

    Füge Erkenntnisse aus anderen KI-Sessions ein — jede Zeile wird ein eigener Eintrag.
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    Neuer Eintrag

    Strg+Enter zum Speichern
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    ',1),Sv=g('
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    Gedächtnis

    Fakten, Entscheidungen und Kontext — geteilt von allen KI-Tools via MCP.
    Filter:

    Wie KI-Tools dieses Gedächtnis nutzen

    Cline, OpenCode und Claude Code greifen über den Memory MCP-Server auf diese Einträge zu. +
    `]]}],dv=[["LLM / Modell","Die KI selbst — versteht und schreibt Text (wie ChatGPT, nur lokal auf deinem PC). Ein Modell ist eine konkrete Datei, oft auf etwas spezialisiert (Code, Bilder, Chat)."],["GGUF","Das lokal lauffähige Dateiformat. llama-swap lädt nur GGUF — Repos ohne GGUF kann die Engine nicht nutzen."],["Quantisierung (Q4_K_M)","Verkleinert ein Modell (16-Bit → 4-Bit), damit es in den Speicher passt — bei kaum Qualitätsverlust. Q4_K_M ist der bewährte Sweetspot. Für Coding/Reasoning: Q8_0 (nahezu verlustfrei). Wenn Q4_K_M 1 GB zu groß ist: IQ4_XS als Alternative (ähnliche Qualität, etwas kleiner)."],["Kontextfenster","Das Kurzzeitgedächtnis: wie viel Text sich das Modell gleichzeitig merken kann. Größer = mehr Überblick, mehr Speicher, etwas langsamer."],["Token","Die kleinste Einheit die ein Modell verarbeitet — meist 3–4 Zeichen oder ein kurzes Wort. 1 000 Tokens ≈ 750 Wörter. Kontextfenster, Geschwindigkeit und Kosten werden in Tokens gemessen."],["Auto-Swap","llama-swap lädt/entlädt Modelle automatisch — immer nur eins gleichzeitig. Du managst nichts."],["TTL (Time to Live)","Wie lange ein Modell nach der letzten Anfrage im RAM bleibt bevor es entladen wird. Standard: 5 Minuten. ∞ = bleibt dauerhaft geladen (sinnvoll für den Scout)."],["Agent","Ein Programm (OpenCode, Cline, Continue), das das Modell in einer Handlungsschleife laufen lässt — Dateien lesen/ändern, Befehle ausführen, planen, bis die Aufgabe erledigt ist."],["MCP","Model Context Protocol — der Stecker, über den ein Agent zusätzliche Werkzeuge bekommt (Web, Datenbank, GitHub …). MCP = neue Fähigkeiten."],["Skills / Rules","Kurze Textdatei (AGENTS.md, .clinerules …) mit Anweisungen wie der Agent in deinem Projekt arbeiten soll — einmal geschrieben, immer befolgt. Seit 2026 plattformübergreifender Standard: AGENTS.md wird von über 10 Tools nativ gelesen."],["Memory Bank","Clines Konzept für dauerhaften Projekt-Kontext: projectbrief.md (Ziel & Scope), activeContext.md (aktueller Fokus), progress.md (Stand). Wird zu Beginn jeder Aufgabe automatisch gelesen — ähnlich wie AGENTS.md, aber aufgeteilt nach Zweck."],["System-Prompt","Eine unsichtbare Anweisung die vor deiner Nachricht steht und das Verhalten des Modells grundlegend prägt (Sprache, Ton, Rolle). Skills nutzen oft den System-Prompt."],["Vision","Modelle mit Vision-Fähigkeit können Bilder verstehen — Screenshots, Diagramme, Fehlerfotos. Erkennbar am mmproj-Projektor der mitgeladen wird."]];var cv=g('

    '),vv=g(""),uv=g('
    '),pv=g('
    '),fv=g('

    Guide

    Schritt für Schritt erklärt — von der ersten Einrichtung bis zu echten Agenten-Workflows.

    Begriffe kurz erklärt

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    Import aus Claude / Gemini / ChatGPT

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    Neuer Eintrag

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    Gedächtnis

    Fakten, Entscheidungen und Kontext — geteilt von allen KI-Tools via MCP.
    Filter:

    Wie KI-Tools dieses Gedächtnis nutzen

    Cline, OpenCode und Claude Code greifen über den Memory MCP-Server auf diese Einträge zu. Beim Start einer Session lädt der Agent automatisch alle Einträge — er kennt dann deinen Stack, - deine Präferenzen und frühere Entscheidungen. Den Setup-Guide findest du unter Verbinden → Gedächtnis-MCP.
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      Was Hermes über dich gelernt hat (automatisches Profil)
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      Keine geplanten Jobs. Hermes kann selbst welche anlegen — sag ihm z. B. +...`);var Y=i(E,2),ke=r(Y),Pe=i(r(ke),2),C=r(Pe);C.value=C.__value="user";var R=i(C);R.value=R.__value="instruction";var se=i(R);se.value=se.__value="stable";var ie=i(se);ie.value=ie.__value="versioned";var ee=i(ie);ee.value=ee.__value="ephemeral";var Se=i(ke,2),de=i(r(Se),2),A=r(de);A.value=A.__value="import";var U=i(A);U.value=U.__value="claude";var oe=i(U);oe.value=oe.__value="gemini";var Me=i(oe);Me.value=Me.__value="chatgpt";var H=i(Me);H.value=H.__value="manual";var le=i(Y,2);{var Te=W=>{var xe=gv(),Ie=r(xe);z(()=>h(Ie,`${t(M).length??""} Eintrag/Einträge erkannt`)),p(W,xe)};O(le,W=>{t(M).length>0&&W(Te)})}var ze=i(le,2),x=r(ze),J=r(x),he=i(x,2);z(()=>{x.disabled=t(M).length===0||t(B),h(J,t(B)?"Importiere…":`${t(M).length} Einträge speichern`)}),dn(E,()=>t(_),W=>u(_,W)),Jn(Pe,()=>t(y),W=>u(y,W)),Jn(de,()=>t(D),W=>u(D,W)),S("click",x,ut),S("click",he,()=>{u(T,!1),u(_,"")}),p($,G)};O(nt,$=>{t(T)&&$(it)})}var et=i(nt,2);{var bt=$=>{var G=bv(),E=i(r(G),4),Y=i(E,4),ke=r(Y);ke.value=ke.__value="user";var Pe=i(ke);Pe.value=Pe.__value="instruction";var C=i(Pe);C.value=C.__value="stable";var R=i(C);R.value=R.__value="versioned";var se=i(R);se.value=se.__value="ephemeral";var ie=i(Y,2),ee=r(ie),Se=i(ee,2);z(de=>ee.disabled=de,[()=>!t(d).trim()]),S("keydown",E,de=>{de.key==="Enter"&&(de.ctrlKey||de.metaKey)&&te()}),dn(E,()=>t(d),de=>u(d,de)),Jn(Y,()=>t(c),de=>u(c,de)),S("click",ee,te),S("click",Se,()=>{u(o,!1),u(d,"")}),p($,G)};O(et,$=>{t(o)&&$(bt)})}var wt=i(et,2),Ct=r(wt),Kt=i(r(Ct),2);Je(Kt,17,()=>[["all","Alle","",t(a).length],["user","Über mich","#a78bfa",Ce("user")],["instruction","Verhalten","#fb923c",Ce("instruction")],["stable","Stabil","var(--accent)",Ce("stable")],["versioned","Versioniert","#f59e0b",Ce("versioned")],["ephemeral","Temporär","var(--mut)",Ce("ephemeral")]],Xe,($,G)=>{var E=q(()=>Hn(t(G),4));let Y=()=>t(E)[0],ke=()=>t(E)[1],Pe=()=>t(E)[2],C=()=>t(E)[3];var R=_v(),se=r(R),ie=i(se),ee=r(ie);z(()=>{At(R,`cursor:pointer;${t(l)===Y()?`background:rgba(45,212,191,.12);border-color:${Pe()||"var(--accent)"};color:${Pe()||"var(--accent)"}`:""}`),h(se,`${ke()??""} `),h(ee,C())}),S("click",R,()=>u(l,Y(),!0)),S("keydown",R,Se=>Se.key==="Enter"&&u(l,Y(),!0)),p($,R)});var Pt=i(wt,2);{var Dt=$=>{var G=yv();p($,G)},Lt=$=>{var G=kv(),E=r(G),Y=r(E),ke=r(Y),Pe=i(Y,2),C=r(Pe),R=i(Pe,2);{var se=ie=>{var ee=xv();S("click",ee,()=>u(o,!0)),p(ie,ee)};O(R,ie=>{t(a).length===0&&ie(se)})}z(()=>{h(ke,t(a).length===0?"Noch keine Einträge":"Keine Treffer"),h(C,t(a).length===0?'Klicke „+ Eintrag" um manuell etwas zu speichern, oder verbinde ein KI-Tool via MCP.':"Andere Kategorie wählen oder Eintrag anlegen.")}),p($,G)},It=$=>{var G=Ev();Je(G,23,()=>t(K),E=>E.id,(E,Y,ke)=>{var Pe=Mv(),C=r(Pe);{var R=ie=>{var ee=wv(),Se=tt(ee),de=i(Se,2),A=r(de);A.value=A.__value="user";var U=i(A);U.value=U.__value="instruction";var oe=i(U);oe.value=oe.__value="stable";var Me=i(oe);Me.value=Me.__value="versioned";var H=i(Me);H.value=H.__value="ephemeral";var le=i(de,2),Te=r(le),ze=i(Te,2);dn(Se,()=>t(f),x=>u(f,x)),Jn(de,()=>t(b),x=>u(b,x)),S("click",Te,()=>X(t(Y))),S("click",ze,()=>u(v,null)),p(ie,ee)},se=ie=>{var ee=Sv(),Se=r(ee),de=r(Se),A=r(de),U=i(de,2),oe=r(U),Me=r(oe),H=i(oe,2),le=r(H),Te=i(H,2),ze=r(Te),x=i(Se,2),J=r(x),he=i(J,2);z(W=>{var xe,Ie,Fe;h(A,t(Y).content),At(oe,`border-color:${(((xe=Oe[t(Y).category])==null?void 0:xe.color)||"var(--mut)")??""};color:${(((Ie=Oe[t(Y).category])==null?void 0:Ie.color)||"var(--mut)")??""}`),h(Me,((Fe=Oe[t(Y).category])==null?void 0:Fe.label)||t(Y).category),h(le,t(Y).source),h(ze,W)},[()=>Ee(t(Y).updated_at)]),S("click",J,()=>Be(t(Y))),S("click",he,()=>be(t(Y))),p(ie,ee)};O(C,ie=>{t(v)===t(Y).id?ie(R):ie(se,-1)})}z(()=>At(Pe,`padding:14px 16px;${t(ke){t(s)?$(Dt):t(K).length===0?$(Lt,1):$(It,-1)})}var Tt=i(Pt,2),P=i(r(Tt),2),V=i(r(P),3);z(()=>{He.disabled=t(Ne),h(N,t(Ne)?"…":"🧹 Aufräumen"),h(pe,t(T)?"Abbrechen":"⇩ Import"),h(Ve,t(o)?"Abbrechen":"+ Eintrag")}),S("click",He,Ze),S("click",ve,()=>{u(T,!t(T)),u(o,!1),u(_,"")}),S("click",je,()=>{u(o,!t(o)),u(T,!1),u(d,"")}),S("click",V,$=>{var G;$.preventDefault(),(G=document.querySelector(".nav-item[data-view='connect']"))==null||G.click()}),p(e,I),Pn()}qn(["click","keydown"]);var ts=g(' '),Tv=g(' Sessions'),zv=g(' Nachrichten'),$v=g(' Tool-Calls'),Pv=g(' Tokens'),Dv=g(' aktiv'),Iv=g('
      '),Lv=g('
      Meistgenutzte Tools
      ',1),Ov=g('
      Aktivität (letzte 30 Tage)
      '),Bv=g('
      • '),Rv=g('
        '),Fv=g('
        '),Kv=g('
        Was Hermes über dich gelernt hat (automatisches Profil)
        '),Nv=g(`
        Keine geplanten Jobs. Hermes kann selbst welche anlegen — sag ihm z. B. „Erinnere mich jeden Morgen um 9 Uhr an die offenen Modell-Upgrades".
        `),qv=g(' '),Gv=g(`
        `),Vv=g('
        '),Wv=g('
        Keine Skills geladen (oder Agent offline).
        '),jv=g(' '),Uv=g(' '),Hv=g('
        '),Jv=g('
        '),Qv=g('

        Agent-Cockpit

        Status
        Hermes-Gateway-Dienst (:8642) auf dem Bosgame
        Geplante Aufgaben
        Skills
        '),Zv=g('
        pip install faster-whisper
        '),Yv=g('
        '),Xv=g('
        4. Bosgame-Key eintragen (in Windows PowerShell):
        '),eu=g('
        SSH-Key nicht gefunden — bitte auf dem Bosgame generieren: ssh-keygen -t ed25519 -C "hermes-agent@bosgame" -f ~/.ssh/id_ed25519_hermes_agent -N ""
        '),tu=g('
        Einrichtung (einmalig, ~3 Minuten):
        1. PowerShell als Admin — OpenSSH Server installieren:
        Add-WindowsCapability -Online -Name OpenSSH.Server~~~~0.0.1.0
        2. Dienst starten und dauerhaft aktivieren:
        3. SSH-Ordner erstellen:
        5. Windows-IP in Mission Control Env-Var setzen:
        HERMES_WINDOWS_HOST=192.168.178.XXX
        '),nu=g('

        Hermes Setup-Wizard

        Whisper (Sprache → Text)
        faster-whisper muss auf dem Bosgame installiert sein
        Piper TTS (Text → Sprache)
        Piper-Binary + deutsche Stimme
        Windows SSH-Verbindung
        Damit Hermes Dateien auf deinem Windows-PC ändern kann
        '),au=g(''),iu=g(`
        Hermes-Agent offline
        Der Hermes-Gateway (:8642) bzw. das Dashboard (:9119) ist nicht erreichbar. - Status im Cockpit prüfen oder den Dienst neu starten.
        `),ru=g('

        Hermes

        ',1);function su(e,n){$n(n,!0);let a=I(null),s=I(!1),l=I(!1),o=I(!1),d=I(null),c=I(jt([])),v=I(jt([])),f=I(null),b=I(null),T=I("");async function _(){try{u(a,await we("/api/hermes/status"),!0)}catch{u(a,null)}}async function y(){try{const N=await we("/api/hermes/pubkey");u(T,N.pubkey,!0)}catch{u(T,"")}}async function P(){u(o,!0);try{const[N,ve,pe,je,Ve]=await Promise.all([we("/api/hermes/agent"),we("/api/hermes/cron"),we("/api/hermes/skills"),we("/api/hermes/learned"),we("/api/hermes/insights")]);u(d,N,!0),u(c,ve.jobs||[],!0),u(v,pe.skills||[],!0),u(f,je,!0),u(b,Ve,!0)}catch{u(d,null)}u(o,!1)}let O=q(()=>[...t(v)].sort((N,ve)=>(ve.use_count||0)-(N.use_count||0)||(N.name||"").localeCompare(ve.name||""))),M=q(()=>t(v).filter(N=>(N.status||"").includes("enabled")).length);function K(){var N;(N=navigator.clipboard)==null||N.writeText(t(T)),ce("SSH-Key kopiert")}bi(()=>{_()});var Oe=ru(),Ee=tt(Oe),Ce=i(r(Ee),2),Re=r(Ce),te=r(Re),X=i(Re,2),be=i(X,2),Be=i(Ee,2);{var Ne=N=>{var ve=Qv(),pe=r(ve),je=i(r(pe),2),Ve=r(je),nt=i(pe,2),it=r(nt),et=i(r(it),2),bt=r(et),wt=r(bt),Ct=i(bt,2),Kt=r(Ct),Pt=i(Ct,2);{var Dt=U=>{var le=ts(),Te=r(le);z(()=>h(Te,`Ticker vor ${t(d).heartbeat_age_s??""}s`)),p(U,le)};L(Pt,U=>{var le;((le=t(d))==null?void 0:le.heartbeat_age_s)!=null&&U(Dt)})}var Lt=i(Pt,2);{var It=U=>{var le=ts(),Te=r(le);z(ze=>{Aa(le,"title",t(d).version),h(Te,ze)},[()=>t(d).version.split(" · ")[0]]),p(U,le)};L(Lt,U=>{var le;(le=t(d))!=null&&le.version&&U(It)})}var Tt=i(nt,2);{var F=U=>{var le=Ov(),Te=i(r(le),2),ze=r(Te);{var x=R=>{var ne=Tv(),_e=r(ne),ge=r(_e);z(()=>h(ge,t(b).sessions)),p(R,ne)};L(ze,R=>{t(b).sessions&&R(x)})}var H=i(ze,2);{var he=R=>{var ne=zv(),_e=r(ne),ge=r(_e);z(()=>h(ge,t(b).messages)),p(R,ne)};L(H,R=>{t(b).messages&&R(he)})}var V=i(H,2);{var xe=R=>{var ne=$v(),_e=r(ne),ge=r(_e);z(()=>h(ge,t(b).tool_calls)),p(R,ne)};L(V,R=>{t(b).tool_calls&&R(xe)})}var Ie=i(V,2);{var Fe=R=>{var ne=Pv(),_e=r(ne),ge=r(_e);z(()=>h(ge,t(b).total_tokens)),p(R,ne)};L(Ie,R=>{t(b).total_tokens&&R(Fe)})}var De=i(Ie,2);{var W=R=>{var ne=Dv(),_e=r(ne),ge=r(_e);z(()=>h(ge,t(b).active_time)),p(R,ne)};L(De,R=>{t(b).active_time&&R(W)})}var J=i(Te,2);{var Q=R=>{var ne=Lv(),_e=i(tt(ne),2);Je(_e,21,()=>t(b).top_tools.slice(0,6),Xe,(ge,fe)=>{var me=Iv(),_t=r(me),zt=r(_t),Ot=i(_t,2),gn=r(Ot),da=i(Ot,2),k=r(da);z(()=>{h(zt,t(fe).name),At(gn,`display:block;height:100%;width:${t(fe).pct??""}%;background:var(--accent)`),h(k,`${t(fe).calls??""}× · ${t(fe).pct??""}%`)}),p(ge,me)}),p(R,ne)};L(J,R=>{var ne;(ne=t(b).top_tools)!=null&&ne.length&&R(Q)})}p(U,le)};L(Tt,U=>{t(b)&&(t(b).sessions||t(b).total_tokens)&&U(F)})}var Y=i(Tt,2);{var $=U=>{var le=Kv(),Te=i(r(le),2);{var ze=he=>{var V=Rv();Je(V,21,()=>t(f).user_facts,Xe,(xe,Ie)=>{var Fe=Bv(),De=r(Fe);z(()=>h(De,t(Ie))),p(xe,Fe)}),p(he,V)};L(Te,he=>{var V;(V=t(f).user_facts)!=null&&V.length&&he(ze)})}var x=i(Te,2);{var H=he=>{var V=Fv(),xe=r(V);z(()=>h(xe,t(f).memory)),p(he,V)};L(x,he=>{t(f).memory&&he(H)})}p(U,le)};L(Y,U=>{var le,Te,ze;((Te=(le=t(f))==null?void 0:le.user_facts)!=null&&Te.length||(ze=t(f))!=null&&ze.memory)&&U($)})}var G=i(Y,2),E=r(G),Z=r(E),ke=i(r(Z)),Pe=r(ke),C=i(E,2);{var B=U=>{var le=Nv();p(U,le)},se=U=>{var le=Vv();Je(le,21,()=>t(c),Xe,(Te,ze)=>{var x=Gv();Je(x,21,()=>Object.entries(t(ze)),Xe,(H,he)=>{var V=q(()=>Hn(t(he),2));let xe=()=>t(V)[0],Ie=()=>t(V)[1];var Fe=Ua(),De=tt(Fe);{var W=J=>{var Q=qv(),R=r(Q),ne=r(R),_e=i(R);z(()=>{h(ne,`${xe()??""}:`),h(_e,` ${Ie()??""}`)}),p(J,Q)};L(De,J=>{Ie()&&J(W)})}p(H,Fe)}),p(Te,x)}),p(U,le)};L(C,U=>{t(c).length===0?U(B):U(se,-1)})}var ie=i(G,2),ee=r(ie),Se=r(ee),de=i(r(Se)),A=r(de),j=i(ee,2);{var oe=U=>{var le=Wv();p(U,le)},Me=U=>{var le=Jv();Je(le,21,()=>t(O),Xe,(Te,ze)=>{var x=Hv(),H=r(x),he=i(H,2),V=r(he),xe=i(he,2);{var Ie=Q=>{var R=jv(),ne=r(R);z(()=>h(ne,t(ze).category)),p(Q,R)};L(xe,Q=>{t(ze).category&&Q(Ie)})}var Fe=i(xe,4);{var De=Q=>{var R=Uv(),ne=r(R);z(()=>h(ne,`${t(ze).use_count??""}× genutzt`)),p(Q,R)};L(Fe,Q=>{t(ze).use_count>0&&Q(De)})}var W=i(Fe,2),J=r(W);z((Q,R)=>{At(x,`display:flex;align-items:center;gap:8px;font-size:12.5px;padding:4px 8px; 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