feat(m14): AI assistant — explain diagnostics, opt-in (Ollama or Claude) — 0.27.0

New optional module (D24): explains the collected findings in plain language,
contacted ONLY on an explicit user action (never automatic).

- core/ai.py: provider chosen explicitly (no default) — ollama (local) or claude
  (Anthropic Messages API via stdlib urllib; key in keyring). Grounded prompt;
  HTTP error parsing; one-shot (no thinking/caching — snappy).
- core/ai_knowledge.py: curated reference KB (Xid/SMART/Proton/tunables),
  exact keyword/code match ("RAG-lite", no embeddings) injected into the prompt —
  lifts local models, sharpens Claude. No fine-tuning.
- config: ai_provider/ai_model/ai_endpoint + keyring-backed AI key (generalized
  the token keyring helpers).
- GUI: Settings → AI assistant (provider radios, model/endpoint/key, Save/Test);
  "Explain with AI" button on the diagnostic dialog (consent prompt for cloud).
- CLI: `rigdoctor ai status|test|explain`.
- Docs: D24, SPEC/MODULES/ROADMAP (Phase 7); tests for providers/grounding/parse.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

CHANGELOG.md

@@ -5,6 +5,19 @@ All notable changes to RigDoctor are recorded here. Format follows
 (`MAJOR.MINOR.PATCH`, pre-1.0). `__version__` and `pyproject.toml` must match the git
 release tag (so the auto-updater, D18, can compare versions).
 
+## [0.27.0] - 2026-05-22
+### Added
+- **AI assistant (M14, D24)** — optional, **strictly opt-in, never automatic**. Explains your
+  diagnostics in plain language only when you press **"Explain with AI"** on the diagnostic
+  dialog (or run `rigdoctor ai explain`). You choose a provider explicitly (no default):
+  **Ollama** (local, private, no key) or **Claude** (Anthropic; key stored in the keyring, with
+  a consent prompt before any data is sent). Configure in **Settings → AI assistant**.
+- Answers are **grounded**: RigDoctor passes the actual findings plus matched reference facts
+  from a curated knowledge base (`core/ai_knowledge.py` — exact keyword/code match, no
+  embeddings, stdlib only), so even a small local model gets the domain facts it needs. Stdlib
+  `urllib` only — no new core dependency. Output is advisory (D9).
+- CLI: `rigdoctor ai status|test|explain`.
+
 ## [0.26.1] - 2026-05-22
 ### Fixed
 - **Setup wizard contrast.** The **radio buttons** (Recording trigger) were unstyled, so the

docs/DECISIONS.md

@@ -249,9 +249,24 @@ duplicated what the GUI already shows and added surface area. Concretely:
   (preserves fish/ls/git theming), full-screen-able, with the guest read-only unless the host
   ticks "Allow the guest to type" (the D9 consent exception). Account-gated by the Gitea token.
 
+### D24 — AI assistant module (M14) — *DECIDED 2026-05-22; adds to D14*
+A new optional module that **explains the collected diagnostics in plain language** (likely
+root cause + suggested next steps). Adds M14 to the D14 set.
+- **Strictly opt-in, never automatic.** The model is contacted **only** on an explicit user
+  action (an "Explain with AI" button / `rigdoctor ai explain`) — never on launch, after a
+  diagnostic, in the sample/record loop, or in the background. **Configuring** a provider does
+  not trigger any call.
+- **Local-first.** Defaults to a local **Ollama** server (data never leaves the machine, no
+  key, stdlib `urllib`). An **OpenAI-compatible** endpoint (cloud or local) can be used with a
+  key (stored in the keyring like the update token). Cloud use shows a "this sends your data to
+  X" consent before the first call.
+- **Grounded & advisory.** The prompt carries only the findings we collected; output is framed
+  as suggestions (consistent with D9 — it explains/recommends, applying fixes stays
+  consent-gated). No new runtime dependency (HTTP via stdlib).
+
 ## Open
 
-None currently — all tracked decisions (D1–D23) are resolved. New questions will be added
+None currently — all tracked decisions (D1–D24) are resolved. New questions will be added
 here as they arise. Remaining detail to flesh out during build: the tray's supporting-action
 set (D13), per-module apt package names, M12's tunnel/token specifics, and M13's
 update mechanism (APT repo vs. self-installed `.deb`).

docs/MODULES.md

@@ -20,6 +20,7 @@ Status: ⬜ not started · 🟦 designing · 🟨 in progress · ✅ done
 | M9 | Installer | (meta) | none | all | P1 | 🟨 |
 | M12 | Session sharing (shared terminal) | Sharing | none (relay) | all | P3 | ✅ |
 | M13 | Auto-update | (core) | none (stdlib; user-local file swap) | all | P3 | ✅ |
+| M14 | AI assistant (explain diagnostics) | (optional) | none (stdlib urllib; Ollama or Claude) | all | P3 | ✅ |
 | ~~M7~~ | ~~Stress / repro~~ | — | — | — | — | ❌ dropped (D7) |
 
 ## Notes per module
@@ -117,6 +118,15 @@ Status: ⬜ not started · 🟦 designing · 🟨 in progress · ✅ done
   atomic symlink swap → restart, incl. the daemon). HTTPS-only, version-check-only (no
   telemetry), opt-out-able. Surfaced in the GUI; `rigdoctor update` in the CLI. (`.deb` users
   update via apt instead.)
+- **M14 AI assistant** (D24) — optional, **strictly opt-in, never automatic**: explains the
+  collected diagnostics in plain language only when the user presses **"Explain with AI"**
+  (`core/ai.py`, GUI button on the diagnostic dialog, `rigdoctor ai explain`). The user picks a
+  provider explicitly (no default): **Ollama** (local, private, no key) or **Claude** (Anthropic
+  Messages API, key in the keyring; consent prompt before sending). Answers are **grounded** —
+  we pass the actual findings plus matched reference facts from a curated knowledge base
+  (`core/ai_knowledge.py`, "RAG-lite": exact keyword/code match, no embeddings, stdlib only),
+  which lifts a small local model and sharpens Claude. Stdlib `urllib` (no pip deps); output is
+  advisory (D9). Configure in **Settings → AI assistant**.
 
 ## Bundles (final — D14)
 - **Essential:** M1 + M3 + M4  *(the MVP, NVIDIA-only — D5)*
@@ -124,6 +134,7 @@ Status: ⬜ not started · 🟦 designing · 🟨 in progress · ✅ done
 - **Diagnostics:** M5 + M6
 - **Desktop UI:** M10 + M11  *(adds PySide6)*
 - **Sharing:** M12  *(session sharing / remote assist — D16)*
+- **AI:** M14  *(optional AI explanations — D24)*
 
 ## MVP candidate — *confirmed (D5)*
 **M1 + M3 + M4 (Essential), NVIDIA-only, CLI-first.** Gives a working tool that captures the

docs/ROADMAP.md

@@ -89,6 +89,14 @@ Ubuntu + NVIDIA first; `.deb` distribution (see `DECISIONS.md`).
 - [removed] The read-only stats view (`share serve`) and bundle export — dropped per D23; the
       shared terminal is the only sharing mode.
 
+## Phase 7 — AI assistant  (M14, D24)
+- [x] **Explain diagnostics with AI** — opt-in, never automatic (`core/ai.py`, "Explain with AI"
+      button + `rigdoctor ai explain`). Provider chosen explicitly: **Ollama** (local) or
+      **Claude** (Anthropic). Grounded with a curated reference KB (`core/ai_knowledge.py`,
+      RAG-lite, exact match — no embeddings); stdlib `urllib`. Settings → AI assistant.
+- [ ] *Possible follow-ups:* interactive chat grounded in the data; more reference-KB entries;
+      an "Explain" button on the System Health page.
+
 > **Out of scope:** stress/repro module (D7); multi-distro support and packaging beyond
 > Ubuntu/apt + `.deb` (D15) — a thin seam is kept but not built out.
 

docs/SPEC.md

@@ -152,6 +152,16 @@ type too (e.g. a sudo password, which stays local and is never sent to B). Accou
 Gitea token; per-session share code. The shared terminal preserves colors/theming and can be
 viewed full-screen. *(The earlier read-only stats view / bundle export were dropped — D23.)*
 
+### M14 — AI assistant (D24)
+Optional module that explains the collected diagnostics in plain language. **Strictly opt-in and
+never automatic** — the model is contacted only when the user presses "Explain with AI" (GUI) or
+runs `rigdoctor ai explain`; configuring it contacts nothing. The user explicitly chooses a
+provider (no default): **Ollama** (local, private, no key) or **Claude** (Anthropic Messages
+API, key in the keyring, with a consent prompt before sending data). Answers are **grounded** in
+the actual findings plus matched reference facts from a curated, exact-match knowledge base
+("RAG-lite" — no embeddings/vector store, stdlib only); no fine-tuning. HTTP via stdlib `urllib`
+(no new core dependency); output is advisory (consistent with D9).
+
 ## 5. Non-functional requirements
 - **Zero hard deps for the core/CLI/daemon** — Python stdlib + tools already present. **Qt
   (PySide6) is required only by the GUI (M10) and tray (M11) modules**, declared in the

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "rigdoctor"
-version = "0.26.1"
+version = "0.27.0"
 description = "Modular hardware monitoring & crash diagnostics for Linux gamers."
 readme = "README.md"
 requires-python = ">=3.11"

src/rigdoctor/__init__.py

@@ -1,3 +1,3 @@
 """RigDoctor — modular hardware monitoring & crash diagnostics for Linux gamers."""
 
-__version__ = "0.26.1"
+__version__ = "0.27.0"

src/rigdoctor/cli.py

@@ -438,6 +438,40 @@ def cmd_service(args) -> int:
     return 0
 
 
+def cmd_ai(args) -> int:
+    """AI assistant (M14) — opt-in; only contacts a provider on `test`/`explain`."""
+    from .core import ai
+
+    sub = args.ai_cmd or "status"
+    if sub == "status":
+        print(f"Provider: {ai.provider() or 'not configured'}")
+        if ai.provider():
+            print(f"  {ai.provider_label()}")
+            print(f"  ready:    {'yes' if ai.is_configured() else 'no'}")
+        else:
+            print("  Configure it in the GUI: Settings → AI assistant.")
+        return 0
+
+    if not ai.is_configured():
+        print("AI is not configured. Set it up in the GUI (Settings → AI assistant).")
+        return 1
+
+    if sub == "test":
+        ok, msg = ai.explain("Connectivity test — reply exactly: RigDoctor AI is working.")
+        print(msg)
+        return 0 if ok else 1
+
+    # explain: gather the current health findings and ask the provider to explain them.
+    from .core import health
+
+    findings = health.run_health_checks()
+    text = ai.format_findings(findings)
+    print(f"Asking {ai.provider_label()} to explain the current health findings…\n")
+    ok, msg = ai.explain(text)
+    print(msg)
+    return 0 if ok else 1
+
+
 def cmd_gameenv(args) -> int:
     from dataclasses import asdict
 
@@ -645,6 +679,13 @@ def build_parser() -> argparse.ArgumentParser:
     mode_p.add_argument("mode", choices=("manual", "always-on", "game-launch"))
     mode_p.set_defaults(func=cmd_service)
     svc_p.set_defaults(func=cmd_service, service_cmd=None)
+
+    ai_p = sub.add_parser("ai", help="AI assistant (M14): explain diagnostics — opt-in, never automatic")
+    ai_sub = ai_p.add_subparsers(dest="ai_cmd")
+    ai_sub.add_parser("status", help="show the configured provider (contacts nothing)").set_defaults(func=cmd_ai)
+    ai_sub.add_parser("test", help="send a tiny probe to verify connectivity").set_defaults(func=cmd_ai)
+    ai_sub.add_parser("explain", help="explain the current health findings with AI").set_defaults(func=cmd_ai)
+    ai_p.set_defaults(func=cmd_ai, ai_cmd=None)
     return p
 
 

src/rigdoctor/config.py

@@ -43,6 +43,11 @@ GAMES_FILE = STATE_DIR / "games.json"
 TOKEN_FILE = CONFIG_DIR / "token"
 _SECRET_ATTRS = ["application", "rigdoctor", "type", "update-token"]
 
+# AI assistant (M14, D24) — API key for the Claude provider, stored in the keyring like the
+# update token (Ollama is local and needs none). Separate keyring entry + file fallback.
+AI_KEY_FILE = CONFIG_DIR / "ai-key"
+_AI_SECRET_ATTRS = ["application", "rigdoctor", "type", "ai-key"]
+
 
 def _secret_tool() -> str | None:
     return shutil.which("secret-tool")
@@ -53,27 +58,27 @@ def keyring_available() -> bool:
     return _secret_tool() is not None
 
 
-def _keyring_store(token: str) -> bool:
+def _keyring_store(value: str, attrs: list[str], label: str) -> bool:
     tool = _secret_tool()
     if not tool:
         return False
     try:
         proc = subprocess.run(
-            [tool, "store", "--label", "RigDoctor update token", *_SECRET_ATTRS],
+            [tool, "store", "--label", label, *attrs],
-            input=token, text=True, capture_output=True, timeout=20,
+            input=value, text=True, capture_output=True, timeout=20,
         )
         return proc.returncode == 0
     except (subprocess.SubprocessError, OSError):
         return False
 
 
-def _keyring_lookup() -> str | None:
+def _keyring_lookup(attrs: list[str]) -> str | None:
     tool = _secret_tool()
     if not tool:
         return None
     try:
         proc = subprocess.run(
-            [tool, "lookup", *_SECRET_ATTRS], text=True, capture_output=True, timeout=20
+            [tool, "lookup", *attrs], text=True, capture_output=True, timeout=20
         )
         if proc.returncode == 0 and proc.stdout.strip():
             return proc.stdout.strip()
@@ -82,54 +87,67 @@ def _keyring_lookup() -> str | None:
     return None
 
 
-def _keyring_clear() -> None:
+def _keyring_clear(attrs: list[str]) -> None:
     tool = _secret_tool()
     if not tool:
         return
     try:
-        subprocess.run([tool, "clear", *_SECRET_ATTRS], capture_output=True, timeout=20)
+        subprocess.run([tool, "clear", *attrs], capture_output=True, timeout=20)
     except (subprocess.SubprocessError, OSError):
         pass
 
 
+def _load_secret(env_var: str | None, attrs: list[str], file: Path) -> str | None:
+    if env_var:
+        env = os.environ.get(env_var)
+        if env and env.strip():
+            return env.strip()
+    from_keyring = _keyring_lookup(attrs)
+    if from_keyring:
+        return from_keyring
+    try:
+        value = file.read_text().strip()
+        return value or None
+    except OSError:
+        return None
+
+
+def _save_secret(value: str, attrs: list[str], label: str, file: Path) -> None:
+    value = value.strip()
+    if _keyring_store(value, attrs, label):
+        try:  # don't leave a plaintext copy once it's in the keyring
+            file.unlink()
+        except OSError:
+            pass
+        return
+    CONFIG_DIR.mkdir(parents=True, exist_ok=True)
+    file.write_text(value + "\n")
+    try:
+        file.chmod(0o600)
+    except OSError:
+        pass
+
+
+def _clear_secret(attrs: list[str], file: Path) -> None:
+    _keyring_clear(attrs)
+    try:
+        file.unlink()
+    except OSError:
+        pass
+
+
 def load_token() -> str | None:
     """Token from $RIGDOCTOR_TOKEN, then the OS keyring, then a 0600 file."""
-    env = os.environ.get("RIGDOCTOR_TOKEN")
+    return _load_secret("RIGDOCTOR_TOKEN", _SECRET_ATTRS, TOKEN_FILE)
-    if env and env.strip():
-        return env.strip()
-    from_keyring = _keyring_lookup()
-    if from_keyring:
-        return from_keyring
-    try:
-        token = TOKEN_FILE.read_text().strip()
-        return token or None
-    except OSError:
-        return None
 
 
 def save_token(token: str) -> None:
     """Save to the OS keyring if possible (encrypted); else a 0600 file."""
-    token = token.strip()
+    _save_secret(token, _SECRET_ATTRS, "RigDoctor update token", TOKEN_FILE)
-    if _keyring_store(token):
-        try:  # don't leave a plaintext copy once it's in the keyring
-            TOKEN_FILE.unlink()
-        except OSError:
-            pass
-        return
-    CONFIG_DIR.mkdir(parents=True, exist_ok=True)
-    TOKEN_FILE.write_text(token + "\n")
-    try:
-        TOKEN_FILE.chmod(0o600)
-    except OSError:
-        pass
 
 
 def clear_token() -> None:
-    _keyring_clear()
+    _clear_secret(_SECRET_ATTRS, TOKEN_FILE)
-    try:
-        TOKEN_FILE.unlink()
-    except OSError:
-        pass
 
 
 def token_backend() -> str:
@@ -137,12 +155,25 @@ def token_backend() -> str:
     env = os.environ.get("RIGDOCTOR_TOKEN")
     if env and env.strip():
         return "env"
-    if _keyring_lookup() is not None:
+    if _keyring_lookup(_SECRET_ATTRS) is not None:
         return "keyring"
     if TOKEN_FILE.exists():
         return "file"
     return "none"
 
+
+def load_ai_key() -> str | None:
+    """Claude API key from $RIGDOCTOR_AI_KEY, then the OS keyring, then a 0600 file (M14)."""
+    return _load_secret("RIGDOCTOR_AI_KEY", _AI_SECRET_ATTRS, AI_KEY_FILE)
+
+
+def save_ai_key(key: str) -> None:
+    _save_secret(key, _AI_SECRET_ATTRS, "RigDoctor AI key", AI_KEY_FILE)
+
+
+def clear_ai_key() -> None:
+    _clear_secret(_AI_SECRET_ATTRS, AI_KEY_FILE)
+
 DEFAULTS: dict = {
     "interval": 1.0,           # sampling interval in seconds (default ≤1 Hz — NFR)
     "log_max_bytes": 20_000_000,  # rotate a log segment past this size
@@ -156,6 +187,9 @@ DEFAULTS: dict = {
     "steam_libraries": [],        # Steam library paths to scan for games (M6); empty = none picked yet
     "trigger_mode": "manual",     # crash-logger trigger (D6): manual | always-on | game-launch
     "setup_done": False,          # first-run GUI setup wizard completed (M9)
+    "ai_provider": "",            # AI assistant (M14, D24): "" (unset) | "ollama" | "claude"
+    "ai_model": "",               # model name (e.g. "llama3.1" for Ollama; blank = Claude default)
+    "ai_endpoint": "http://localhost:11434",  # Ollama server base URL (Claude uses a fixed endpoint)
 }
 
 

src/rigdoctor/core/ai.py

@@ -0,0 +1,173 @@
+"""AI assistant (M14, D24): explain the collected diagnostics in plain language.
+
+**Strictly opt-in and never automatic** — the model is contacted ONLY from a direct user
+action ("Explain with AI" / ``rigdoctor ai explain``), never on launch, after a diagnostic, or
+in any loop. Choosing/configuring a provider does not contact anything. The user must pick a
+provider explicitly (there is no default).
+
+Two providers, both over stdlib ``urllib`` (no pip deps in core):
+  * **ollama** — a local server (data stays on the machine, no key).
+  * **claude** — the Anthropic Messages API (key in the keyring).
+
+Answers are *grounded*: we pass the actual findings plus matched reference facts
+(:mod:`ai_knowledge`) and ask the model to reason over them. Output is advisory (D9).
+"""
+
+from __future__ import annotations
+
+import json
+import urllib.error
+import urllib.request
+
+from .. import config
+from . import ai_knowledge
+
+PROVIDERS = ("ollama", "claude")
+OLLAMA_DEFAULT_ENDPOINT = "http://localhost:11434"
+CLAUDE_ENDPOINT = "https://api.anthropic.com/v1/messages"
+CLAUDE_DEFAULT_MODEL = "claude-opus-4-7"
+CLAUDE_MAX_TOKENS = 2000
+ANTHROPIC_VERSION = "2023-06-01"
+
+SYSTEM_PROMPT = (
+    "You are RigDoctor's hardware-diagnostics assistant for Linux gamers. You are given the "
+    "structured findings RigDoctor collected from this machine, and a set of reference facts. "
+    "Explain in plain language what the findings mean, identify the most likely root cause of "
+    "any problem, and give concrete, ordered next steps (exact commands where useful). Base "
+    "your reasoning ONLY on the findings and reference facts provided — do not invent readings, "
+    "hardware, or log lines. Be concise and practical. Present fixes as suggestions, and clearly "
+    "warn before any step that could cause data loss or instability."
+)
+
+
+def provider() -> str:
+    return config.load_config().get("ai_provider", "")
+
+
+def model() -> str:
+    m = config.load_config().get("ai_model", "").strip()
+    if m:
+        return m
+    return CLAUDE_DEFAULT_MODEL if provider() == "claude" else ""
+
+
+def endpoint() -> str:
+    ep = config.load_config().get("ai_endpoint", OLLAMA_DEFAULT_ENDPOINT).strip()
+    return ep or OLLAMA_DEFAULT_ENDPOINT
+
+
+def is_local() -> bool:
+    return provider() == "ollama"
+
+
+def is_configured() -> bool:
+    """Whether the chosen provider is ready (does NOT contact anything)."""
+    p = provider()
+    if p == "claude":
+        return bool(config.load_ai_key())
+    if p == "ollama":
+        return bool(model())  # a model name is required; endpoint has a default
+    return False  # no provider chosen
+
+
+def provider_label() -> str:
+    p = provider()
+    if p == "claude":
+        return f"Claude ({model()})"
+    if p == "ollama":
+        return f"Ollama ({model() or '?'} @ {endpoint()})"
+    return "not configured"
+
+
+def build_prompt(findings_text: str) -> str:
+    """The user-message content: matched reference facts + the collected findings."""
+    facts = ai_knowledge.relevant(findings_text)
+    parts = []
+    if facts:
+        parts.append("Reference facts (use these to interpret the findings):")
+        parts += [f"- {f}" for f in facts]
+        parts.append("")
+    parts.append("Collected findings:")
+    parts.append(findings_text.strip() or "(no findings provided)")
+    return "\n".join(parts)
+
+
+def explain(findings_text: str, timeout: float = 120.0) -> tuple[bool, str]:
+    """Contact the configured provider to explain the findings. Returns (ok, text | error).
+
+    The caller MUST be a direct user action (D24) — this never runs automatically.
+    """
+    content = build_prompt(findings_text)
+    try:
+        if provider() == "claude":
+            return _claude(content, timeout)
+        if provider() == "ollama":
+            return _ollama(content, timeout)
+        return False, "No AI provider is configured (Settings → AI assistant)."
+    except urllib.error.HTTPError as exc:
+        return False, _http_error(exc)
+    except (urllib.error.URLError, OSError, TimeoutError) as exc:
+        return False, f"Couldn't reach the AI provider: {exc}"
+    except (ValueError, KeyError, IndexError) as exc:
+        return False, f"Unexpected response from the AI provider: {exc}"
+
+
+def _post(url: str, payload: dict, headers: dict, timeout: float) -> dict:
+    req = urllib.request.Request(
+        url, data=json.dumps(payload).encode("utf-8"),
+        headers={"Content-Type": "application/json", **headers},
+    )
+    with urllib.request.urlopen(req, timeout=timeout) as resp:
+        return json.load(resp)
+
+
+def _ollama(content: str, timeout: float) -> tuple[bool, str]:
+    if not model():
+        return False, "No Ollama model is set (Settings → AI assistant)."
+    payload = {"model": model(), "system": SYSTEM_PROMPT, "prompt": content, "stream": False}
+    out = _post(endpoint().rstrip("/") + "/api/generate", payload, {}, timeout)
+    return True, (out.get("response") or "").strip() or "(the model returned an empty response)"
+
+
+def _claude(content: str, timeout: float) -> tuple[bool, str]:
+    key = config.load_ai_key()
+    if not key:
+        return False, "No Claude API key is set (Settings → AI assistant)."
+    # One-shot call: no prompt caching (single request, short system prompt) and no thinking
+    # (keeps a button-press snappy). Sampling params are omitted (removed on current Opus).
+    payload = {
+        "model": model(),
+        "max_tokens": CLAUDE_MAX_TOKENS,
+        "system": SYSTEM_PROMPT,
+        "messages": [{"role": "user", "content": content}],
+    }
+    headers = {"x-api-key": key, "anthropic-version": ANTHROPIC_VERSION}
+    out = _post(CLAUDE_ENDPOINT, payload, headers, timeout)
+    text = "\n".join(b.get("text", "") for b in out.get("content", []) if b.get("type") == "text")
+    return True, text.strip() or "(the model returned no text)"
+
+
+def _http_error(exc: urllib.error.HTTPError) -> str:
+    detail = ""
+    try:
+        body = exc.read().decode("utf-8", "replace")
+        detail = json.loads(body).get("error", {}).get("message", "") or ""
+    except (ValueError, OSError):
+        pass
+    hint = " — check your API key in Settings → AI assistant." if exc.code in (401, 403) else ""
+    return f"AI request failed (HTTP {exc.code}){hint}{(': ' + detail) if detail else ''}"
+
+
+def format_findings(findings, header: str = "") -> str:
+    """Render M4 Finding objects (or similar) into the plain-text block we send the model."""
+    lines = [header] if header else []
+    for f in findings:
+        severity = str(getattr(f, "severity", "")).upper()
+        category = getattr(f, "category", "")
+        title = getattr(f, "title", "")
+        detail = getattr(f, "detail", "")
+        line = f"- [{severity}] {category}: {title}".rstrip()
+        if detail:
+            line += f" — {detail}"
+        lines.append(line)
+    return "\n".join(lines) if lines else "No findings."

src/rigdoctor/core/ai_knowledge.py

@@ -0,0 +1,79 @@
+"""Curated reference knowledge for the AI assistant (M14, D24) — "RAG-lite".
+
+A small, hand-written set of domain facts (Xid codes, SMART attributes, common Linux-gaming
+error signatures, tunable meanings). At explain-time we select the entries whose triggers
+appear in the collected findings and inject them into the prompt, so even a small local model
+gets the relevant facts instead of having to recall them. Provider-agnostic — it sharpens
+Claude too.
+
+Retrieval is exact keyword/substring matching, not embeddings: the keys here (``Xid 79``,
+``SMART 197``, ``fallen off the bus``) are precise, so a vector store would be overkill and
+would break the stdlib-only rule. Each entry is ``(triggers, fact)``; a trigger matches
+case-insensitively against the findings text.
+"""
+
+from __future__ import annotations
+
+# (triggers, fact). Keep facts short, factual, and cause-oriented — they go into the prompt.
+ENTRIES: list[tuple[tuple[str, ...], str]] = [
+    (("xid 79", "fallen off the bus", "gpu has fallen"),
+     "NVIDIA Xid 79 / 'GPU has fallen off the bus' = the driver lost PCIe contact with the GPU "
+     "mid-operation. Usual causes, in order: insufficient/unstable PSU power or a bad power "
+     "cable, an unstable overclock/undervolt, PCIe link or riser issues, or overheating. Often "
+     "fatal to the session (hard freeze)."),
+    (("xid 13", "graphics engine exception"),
+     "NVIDIA Xid 13 = graphics engine exception, frequently an unstable GPU overclock or a "
+     "faulty application shader; revert any OC/UV and test."),
+    (("xid 31", "fifo: mmu fault", "mmu fault"),
+     "NVIDIA Xid 31 = MMU fault (illegal memory access by the app/driver) — often a game/driver "
+     "bug or unstable VRAM overclock."),
+    (("xid 8", "xid 62", "xid 63", "xid 64"),
+     "These Xid codes commonly indicate VRAM/ECC or memory-training problems — suspect failing "
+     "VRAM or an unstable memory overclock."),
+    (("smart 197", "current_pending_sector", "pending sector"),
+     "SMART 197 (Current Pending Sector) > 0 = sectors the drive can't read and is waiting to "
+     "reallocate — early sign of a failing disk. Back up now and run an extended self-test."),
+    (("smart 198", "offline_uncorrectable", "uncorrectable"),
+     "SMART 198 (Offline Uncorrectable) > 0 = sectors that failed to read/write — the drive is "
+     "degrading; back up immediately."),
+    (("smart 5", "reallocated_sector", "reallocated sector"),
+     "SMART 5 (Reallocated Sectors) climbing over time = the drive is using spares for bad "
+     "sectors; a rising count predicts failure."),
+    (("media and data integrity errors", "percentage used", "available spare"),
+     "NVMe health: 'Media and Data Integrity Errors' > 0 is concerning; 'Percentage Used' near "
+     "or over 100% and 'Available Spare' below the threshold mean the SSD is near end-of-life."),
+    (("thermal throttling", "throttle", "tjmax", "package id 0"),
+     "Sustained CPU/GPU temperatures at the thermal limit cause throttling (clocks drop to shed "
+     "heat) — check cooling, fan curves, paste, and case airflow."),
+    (("oom", "out of memory", "oom-killer", "killed process"),
+     "The kernel OOM-killer terminates processes when RAM (and swap) are exhausted — a freeze "
+     "or a game crashing to desktop under memory pressure points here; check swap and "
+     "vm.swappiness, and watch for a memory leak."),
+    (("segfault", "general protection fault", "segmentation fault"),
+     "A segfault/GP-fault is a process accessing invalid memory — for games under Proton it's "
+     "often a Proton/Wine or anticheat incompatibility, or unstable RAM (run memtest)."),
+    (("proton", "wine", "d3d", "vkd3d", "dxvk"),
+     "Proton/Wine issues: mismatched Proton version, missing vkd3d/DXVK, or shader-cache "
+     "corruption are common. Try a known-good Proton version and clear the shader cache."),
+    (("pcie_aspm", "aspm"),
+     "PCIe ASPM (Active State Power Management) can cause GPU/NVMe instability on some boards; "
+     "setting pcie_aspm=off is a common stability fix at a small idle-power cost."),
+    (("cpu_governor", "powersave", "schedutil", "performance governor"),
+     "The CPU frequency governor sets the clock policy; 'performance' avoids latency spikes from "
+     "ramp-up at a higher power draw, while 'powersave'/'schedutil' favor efficiency."),
+    (("nvidia persistence", "persistence mode"),
+     "NVIDIA persistence mode keeps the driver loaded when no app is using the GPU, avoiding "
+     "re-init stalls — harmless to enable."),
+]
+
+
+def relevant(findings_text: str, limit: int = 8) -> list[str]:
+    """Reference facts whose triggers appear in the findings text (case-insensitive)."""
+    haystack = findings_text.lower()
+    hits: list[str] = []
+    for triggers, fact in ENTRIES:
+        if any(t in haystack for t in triggers):
+            hits.append(fact)
+        if len(hits) >= limit:
+            break
+    return hits

src/rigdoctor/gui/diagnostic_dialog.py

@@ -2,15 +2,19 @@
 
 from __future__ import annotations
 
-from PySide6.QtCore import Qt
+import threading
+
+from PySide6.QtCore import Qt, Signal
 from PySide6.QtGui import QFont
 from PySide6.QtWidgets import (
     QDialog,
     QFrame,
     QHBoxLayout,
     QLabel,
+    QMessageBox,
     QPushButton,
     QScrollArea,
+    QTextEdit,
     QVBoxLayout,
     QWidget,
 )
@@ -20,8 +24,12 @@ from .widgets import finding_card
 
 
 class DiagnosticDialog(QDialog):
+    _explained = Signal(object)  # (ok, text) from a user-triggered AI explanation
+
     def __init__(self, result, parent=None) -> None:
         super().__init__(parent)
+        self._result = result
+        self._explained.connect(self._on_explained)
         self.setWindowTitle(f"Diagnostic — {result.game}" if result.game else "Diagnostic")
         self.resize(660, 680)
 
@@ -73,9 +81,66 @@ class DiagnosticDialog(QDialog):
         root.addWidget(scroll, 1)
 
         buttons = QHBoxLayout()
+        self._explain_btn = QPushButton("Explain with AI")
+        self._explain_btn.clicked.connect(self._explain_with_ai)
+        from ..core import ai
+        self._explain_btn.setVisible(ai.is_configured())  # opt-in only; hidden if not set up
+        buttons.addWidget(self._explain_btn)
         buttons.addStretch(1)
         close = QPushButton("Close")
         close.setObjectName("PrimaryButton")
         close.clicked.connect(self.accept)
         buttons.addWidget(close)
         root.addLayout(buttons)
+
+    # --- AI explanation (M14, D24) — runs only on this button press ----------------
+    def _explain_with_ai(self) -> None:
+        from ..core import ai
+
+        if not ai.is_local():  # cloud provider → explicit consent before sending data
+            confirm = QMessageBox.question(
+                self, "Send to AI provider",
+                f"This sends your diagnostic findings to {ai.provider_label()}.\n\nContinue?",
+                QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.No,
+                QMessageBox.StandardButton.No,
+            )
+            if confirm != QMessageBox.StandardButton.Yes:
+                return
+        self._explain_btn.setEnabled(False)
+        self._explain_btn.setText("Asking the AI…")
+        threading.Thread(target=self._work_explain, daemon=True).start()
+
+    def _work_explain(self) -> None:
+        from ..core import ai
+
+        text = ai.format_findings(self._result.findings, header="Diagnostic findings:")
+        text += "\n\nCapture summary:\n" + render_summary(self._result.summary)
+        self._explained.emit(ai.explain(text))
+
+    def _on_explained(self, result) -> None:
+        ok, text = result
+        self._explain_btn.setEnabled(True)
+        self._explain_btn.setText("Explain with AI")
+        self._show_explanation(text if ok else f"AI explanation failed:\n\n{text}")
+
+    def _show_explanation(self, text: str) -> None:
+        from ..core import ai
+
+        dlg = QDialog(self)
+        dlg.setWindowTitle(f"AI explanation — {ai.provider_label()}")
+        dlg.resize(620, 520)
+        lay = QVBoxLayout(dlg)
+        view = QTextEdit()
+        view.setObjectName("Report")
+        view.setReadOnly(True)
+        view.setPlainText(text)
+        lay.addWidget(view)
+        note = QLabel("AI-generated suggestions — verify before acting, especially anything that changes settings or data.")
+        note.setObjectName("Muted")
+        note.setWordWrap(True)
+        lay.addWidget(note)
+        close = QPushButton("Close")
+        close.setObjectName("PrimaryButton")
+        close.clicked.connect(dlg.accept)
+        lay.addWidget(close, alignment=Qt.AlignmentFlag.AlignRight)
+        dlg.exec()

src/rigdoctor/gui/setup_page.py

@@ -8,6 +8,7 @@ from PySide6.QtCore import Qt, QUrl, Signal
 from PySide6.QtGui import QDesktopServices
 from PySide6.QtWidgets import (
     QApplication,
+    QButtonGroup,
     QCheckBox,
     QComboBox,
     QDoubleSpinBox,
@@ -18,6 +19,7 @@ from PySide6.QtWidgets import (
     QLineEdit,
     QMessageBox,
     QPushButton,
+    QRadioButton,
     QSizePolicy,
     QTextEdit,
     QVBoxLayout,
@@ -54,6 +56,7 @@ class SetupPage(QWidget):
     _installed = Signal(int, str)
     _upd_state = Signal(object)
     _mode_applied = Signal(object)  # (mode, ok, message) from a trigger-mode change
+    _ai_tested = Signal(object)  # (ok, message) from an AI connectivity test
     changed = Signal()  # alert settings saved — main window re-applies them live
 
     def __init__(self) -> None:
@@ -62,6 +65,7 @@ class SetupPage(QWidget):
         self._installed.connect(self._on_installed)
         self._upd_state.connect(self._on_upd_state)
         self._mode_applied.connect(self._on_mode_applied)
+        self._ai_tested.connect(self._on_ai_tested)
 
         root = QVBoxLayout(self)
         root.setContentsMargins(20, 18, 20, 18)
@@ -158,6 +162,58 @@ class SetupPage(QWidget):
             self._trigger_status.setText("systemd --user isn't available on this system.")
         root.addWidget(trig_card)
 
+        # AI assistant (M14, D24): explain diagnostics. Strictly opt-in — the model is only
+        # contacted when the user presses "Explain with AI"; this panel just configures it.
+        ai_card, ai_layout = _panel("AI assistant")
+        ai_desc = QLabel(
+            "Optionally let an AI explain your diagnostics in plain language. It runs <b>only</b> "
+            "when you press “Explain with AI” — never automatically. Choose a provider:\n"
+            "•  Ollama — a local model on your machine (private, no key; needs Ollama running).\n"
+            "•  Claude — Anthropic's API (higher quality; sends findings to Anthropic; needs a key)."
+        )
+        ai_desc.setObjectName("Muted")
+        ai_desc.setWordWrap(True)
+        ai_layout.addWidget(ai_desc)
+
+        prov_row = QHBoxLayout()
+        self._ai_group = QButtonGroup(self)
+        self._ai_ollama = QRadioButton("Ollama (local)")
+        self._ai_claude = QRadioButton("Claude (Anthropic)")
+        self._ai_group.addButton(self._ai_ollama)
+        self._ai_group.addButton(self._ai_claude)
+        self._ai_ollama.toggled.connect(self._on_ai_provider_changed)
+        prov_row.addWidget(self._ai_ollama)
+        prov_row.addWidget(self._ai_claude)
+        prov_row.addStretch(1)
+        ai_layout.addLayout(prov_row)
+
+        self._ai_model = QLineEdit()
+        self._ai_model.setPlaceholderText("Model (e.g. llama3.1 for Ollama; blank = Claude default)")
+        ai_layout.addWidget(self._ai_model)
+        self._ai_endpoint = QLineEdit()
+        self._ai_endpoint.setPlaceholderText("Ollama server URL (default http://localhost:11434)")
+        ai_layout.addWidget(self._ai_endpoint)
+        self._ai_key = QLineEdit()
+        self._ai_key.setEchoMode(QLineEdit.EchoMode.Password)
+        self._ai_key.setPlaceholderText("Claude API key (stored in your keyring)")
+        ai_layout.addWidget(self._ai_key)
+
+        ai_btn_row = QHBoxLayout()
+        ai_save = QPushButton("Save")
+        ai_save.setObjectName("PrimaryButton")
+        ai_save.clicked.connect(self._save_ai)
+        self._ai_test_btn = QPushButton("Test")
+        self._ai_test_btn.clicked.connect(self._test_ai)
+        ai_btn_row.addWidget(ai_save)
+        ai_btn_row.addWidget(self._ai_test_btn)
+        ai_btn_row.addStretch(1)
+        ai_layout.addLayout(ai_btn_row)
+        self._ai_status = QLabel("")
+        self._ai_status.setObjectName("Muted")
+        self._ai_status.setWordWrap(True)
+        ai_layout.addWidget(self._ai_status)
+        root.addWidget(ai_card)
+
         # Account access (M13/M12): one Gitea token gates updates and session sharing.
         upd_card, upd_layout = _panel("Account access")
         hint = QLabel("A Gitea access token unlocks updates and session sharing. "
@@ -203,8 +259,64 @@ class SetupPage(QWidget):
         self._refresh()
         self._load_alerts()
         self._trigger.setCurrentText(config.load_config().get("trigger_mode", "manual"))
+        self._load_ai()
         self._refresh_update_status()
 
+    # --- AI assistant (M14) ---------------------------------------------------
+    def _load_ai(self) -> None:
+        cfg = config.load_config()
+        prov = cfg.get("ai_provider", "")
+        self._ai_claude.setChecked(prov == "claude")
+        self._ai_ollama.setChecked(prov == "ollama")
+        self._ai_model.setText(cfg.get("ai_model", ""))
+        self._ai_endpoint.setText(cfg.get("ai_endpoint", "http://localhost:11434"))
+        if config.load_ai_key():
+            self._ai_key.setPlaceholderText("Claude API key saved — type to replace")
+        self._on_ai_provider_changed()
+
+    def _ai_provider(self) -> str:
+        if self._ai_claude.isChecked():
+            return "claude"
+        if self._ai_ollama.isChecked():
+            return "ollama"
+        return ""
+
+    def _on_ai_provider_changed(self) -> None:
+        prov = self._ai_provider()
+        self._ai_endpoint.setVisible(prov == "ollama")
+        self._ai_key.setVisible(prov == "claude")
+        self._ai_test_btn.setEnabled(prov != "")
+
+    def _save_ai(self) -> None:
+        prov = self._ai_provider()
+        config.update_config(
+            ai_provider=prov,
+            ai_model=self._ai_model.text().strip(),
+            ai_endpoint=self._ai_endpoint.text().strip() or "http://localhost:11434",
+        )
+        if prov == "claude" and self._ai_key.text().strip():
+            config.save_ai_key(self._ai_key.text().strip())
+            self._ai_key.clear()
+            self._ai_key.setPlaceholderText("Claude API key saved — type to replace")
+        self._ai_status.setText("Saved." if prov else "Saved — no provider selected (AI stays off).")
+
+    def _test_ai(self) -> None:
+        self._save_ai()
+        self._ai_status.setText("Testing… contacting the provider.")
+        self._ai_test_btn.setEnabled(False)
+        threading.Thread(target=self._work_test_ai, daemon=True).start()
+
+    def _work_test_ai(self) -> None:
+        from ..core import ai
+
+        ok, msg = ai.explain("Connectivity test — reply exactly: RigDoctor AI is working.")
+        self._ai_tested.emit((ok, msg))
+
+    def _on_ai_tested(self, result) -> None:
+        ok, msg = result
+        self._ai_test_btn.setEnabled(True)
+        self._ai_status.setText(("✓ " if ok else "✗ ") + (msg[:200] if msg else ""))
+
     def _run_wizard(self) -> None:
         from .setup_wizard import SetupWizard
 

tests/test_ai.py

@@ -0,0 +1,101 @@
+"""Tests for the M14 AI assistant: provider selection, grounding, parsing (no network)."""
+
+import unittest
+from unittest import mock
+
+from rigdoctor.core import ai, ai_knowledge
+
+
+class KnowledgeTests(unittest.TestCase):
+    def test_matches_xid_and_smart(self):
+        facts = ai_knowledge.relevant("Kernel: NVRM: Xid 79: GPU has fallen off the bus")
+        self.assertTrue(any("fallen off the bus" in f for f in facts))
+
+    def test_matches_smart_pending(self):
+        facts = ai_knowledge.relevant("SMART 197 Current_Pending_Sector = 8")
+        self.assertTrue(any("Pending Sector" in f for f in facts))
+
+    def test_no_match_returns_empty(self):
+        self.assertEqual(ai_knowledge.relevant("everything is fine"), [])
+
+
+class ConfigStateTests(unittest.TestCase):
+    def _cfg(self, **over):
+        base = {"ai_provider": "", "ai_model": "", "ai_endpoint": "http://localhost:11434"}
+        base.update(over)
+        return base
+
+    def test_unconfigured_by_default(self):
+        with mock.patch.object(ai.config, "load_config", return_value=self._cfg()):
+            self.assertFalse(ai.is_configured())
+
+    def test_ollama_needs_model(self):
+        with mock.patch.object(ai.config, "load_config", return_value=self._cfg(ai_provider="ollama")):
+            self.assertFalse(ai.is_configured())
+        with mock.patch.object(ai.config, "load_config",
+                               return_value=self._cfg(ai_provider="ollama", ai_model="llama3.1")):
+            self.assertTrue(ai.is_configured())
+
+    def test_claude_needs_key(self):
+        with mock.patch.object(ai.config, "load_config", return_value=self._cfg(ai_provider="claude")), \
+             mock.patch.object(ai.config, "load_ai_key", return_value=None):
+            self.assertFalse(ai.is_configured())
+        with mock.patch.object(ai.config, "load_config", return_value=self._cfg(ai_provider="claude")), \
+             mock.patch.object(ai.config, "load_ai_key", return_value="sk-ant-x"):
+            self.assertTrue(ai.is_configured())
+
+    def test_claude_default_model(self):
+        with mock.patch.object(ai.config, "load_config", return_value=self._cfg(ai_provider="claude")):
+            self.assertEqual(ai.model(), ai.CLAUDE_DEFAULT_MODEL)
+
+
+class PromptTests(unittest.TestCase):
+    def test_build_prompt_includes_facts_and_findings(self):
+        prompt = ai.build_prompt("Xid 79: GPU has fallen off the bus")
+        self.assertIn("Reference facts", prompt)
+        self.assertIn("Collected findings", prompt)
+        self.assertIn("fallen off the bus", prompt)
+
+    def test_format_findings(self):
+        class F:
+            severity, category, title, detail = "warn", "GPU", "Hot", "92C"
+        text = ai.format_findings([F()])
+        self.assertIn("[WARN] GPU: Hot — 92C", text)
+
+
+class ExplainTests(unittest.TestCase):
+    def _cfg(self, **over):
+        base = {"ai_provider": "", "ai_model": "", "ai_endpoint": "http://localhost:11434"}
+        base.update(over)
+        return base
+
+    def test_no_provider(self):
+        with mock.patch.object(ai.config, "load_config", return_value=self._cfg()):
+            ok, msg = ai.explain("x")
+        self.assertFalse(ok)
+        self.assertIn("No AI provider", msg)
+
+    def test_ollama_parses_response(self):
+        with mock.patch.object(ai.config, "load_config",
+                               return_value=self._cfg(ai_provider="ollama", ai_model="llama3.1")), \
+             mock.patch.object(ai, "_post", return_value={"response": "It's the PSU."}) as post:
+            ok, msg = ai.explain("Xid 79")
+        self.assertTrue(ok)
+        self.assertEqual(msg, "It's the PSU.")
+        self.assertIn("/api/generate", post.call_args[0][0])
+
+    def test_claude_parses_content_blocks(self):
+        with mock.patch.object(ai.config, "load_config", return_value=self._cfg(ai_provider="claude")), \
+             mock.patch.object(ai.config, "load_ai_key", return_value="sk-ant-x"), \
+             mock.patch.object(ai, "_post", return_value={"content": [
+                 {"type": "text", "text": "Likely a failing disk."}]}) as post:
+            ok, msg = ai.explain("SMART 197")
+        self.assertTrue(ok)
+        self.assertEqual(msg, "Likely a failing disk.")
+        headers = post.call_args[0][2]
+        self.assertEqual(headers["anthropic-version"], ai.ANTHROPIC_VERSION)
+        self.assertEqual(headers["x-api-key"], "sk-ant-x")
+
+
+if __name__ == "__main__":
+    unittest.main()