remove plan

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diff --git a/PLAN-OPENROUTER.md b/PLAN-OPENROUTER.md
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-# Plan to Implement OpenRouter.ai Support
-
-This document outlines the steps to integrate OpenRouter.ai as a new LLM provider within Hexai. The implementation will follow the existing provider pattern, using `internal/llm/openai.go` as a template due to API similarities.
-
-## 1. Update Configuration (`internal/appconfig/config.go`)
-
-Status: Completed - configuration structs, env overrides, and sample config updated.
-
-The application needs to be aware of the new provider and its specific configuration settings.
-
--   **Add OpenRouter section to `fileConfig`:**
-    -   Create a new `sectionOpenRouter` struct in `internal/appconfig/config.go` to handle settings from `config.toml`.
-    -   It will contain `Model`, `BaseURL`, and `Temperature` fields.
-    -   Add `OpenRouter sectionOpenRouter` to the `fileConfig` struct.
-
--   **Add fields to `App` struct:**
-    -   Add `OpenRouterBaseURL`, `OpenRouterModel`, and `OpenRouterTemperature` to the main `App` struct.
-
--   **Update `loadFromEnv`:**
-    -   Modify the `loadFromEnv` function in `internal/appconfig/config.go` to read `HEXAI_OPENROUTER_MODEL`, `HEXAI_OPENROUTER_BASE_URL`, and `HEXAI_OPENROUTER_TEMPERATURE` from environment variables.
-
--   **Update `toApp()` and `mergeProviderFields`:**
-    -   Add logic to `fileConfig.toApp()` to convert the `sectionOpenRouter` into the `App` struct.
-    -   Add logic to `App.mergeProviderFields()` to merge the OpenRouter configuration from different sources.
-
--   **Update `config.toml.example`:**
-    -   Add a new `[openrouter]` section to the `config.toml.example` file to demonstrate configuration for users.
-
-## 2. Create OpenRouter Provider (`internal/llm/openrouter.go`)
-
-Status: Completed - provider implemented with required headers and logging.
-
-This will be the core implementation of the OpenRouter client.
-
--   **Create new file `internal/llm/openrouter.go`:**
-    -   This file will be a copy of `internal/llm/openai.go` initially, and then modified for OpenRouter.
-
--   **Define `openRouterProvider` struct:**
-    -   It will implement the `llm.Client` and `llm.Streamer` interfaces.
-    -   It will hold the `httpClient`, `apiKey`, `baseURL`, `defaultModel`, etc.
-
--   **Implement `Chat` and `ChatStream` methods:**
-    -   The request logic will be adapted from the OpenAI implementation.
-    -   The base URL will default to `https://openrouter.ai/api/v1` if not provided in the config.
-    -   **Add the required OpenRouter headers to every request:**
-        -   `Authorization: Bearer $OPENROUTER_API_KEY`
-        -   `HTTP-Referer: "https://github.com/snonux/hexai"` (or another suitable value)
-        -   `X-Title: "Hexai"`
-
--   **Model Handling:**
-    -   The `model` from the `Options` will be passed directly in the JSON request body, allowing users to leverage any model available on OpenRouter.
-    -   Default model now uses `openrouter/auto` so requests succeed without additional configuration.
-
-## 3. Integrate into Provider Factory (`internal/llm/provider.go`)
-
-Status: Completed - provider factory wiring and API key plumbing added.
-
-The application needs to be able to select and instantiate the new provider.
-
--   **Update `Config` struct:**
-    -   Add `OpenRouterBaseURL`, `OpenRouterModel`, and `OpenRouterTemperature` fields to the `llm.Config` struct.
-
--   **Update `NewFromConfig` function:**
-    -   Add a new `case "openrouter":` to the `switch` statement.
-    -   This case will be responsible for:
-        1.  Accepting an `openRouterAPIKey` string, which will be read from the `OPENROUTER_API_KEY` environment variable by the caller.
-        2.  Returning an error if the key is missing.
-        3.  Instantiating the new `openRouterProvider` with the correct configuration (API key, model, base URL, temperature).
-
-## 4. Add Tests (`internal/llm/openrouter_test.go`)
-
-Status: Completed - streaming and header unit tests added.
-
--   **Create `internal/llm/openrouter_test.go`:**
-    -   Add unit tests for the `openRouterProvider`.
-    -   Use an HTTP mock (similar to `openai_http_test.go`) to test the `Chat` and `ChatStream` implementations without making real API calls.
-    -   Verify that the correct headers (`Authorization`, `HTTP-Referer`, `X-Title`) are being sent in the mock requests.
-    -   Verify that the request body is correctly formatted.
-
-## 5. Update Documentation
-
-Status: Completed - README and configuration docs now mention OpenRouter setup.
-
--   Update `docs/configuration.md` and `README.md` to include instructions on how to configure and use the OpenRouter.ai provider.
--   Explicitly mention the `OPENROUTER_API_KEY` environment variable and the new settings available in `config.toml`.
diff --git a/PLAN.md b/PLAN.md
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-# Runtime Model Configuration Plan
-
-Implement a /reload> endpoint that reloads all the configuration from hexai.toml and updates the running application's state without requiring a restart.
-
-## Progress
-- [x] Phase 1 – Design approach for making settings dynamically changeable
-- [x] Phase 2 – Implement dynamic configuration plumbing
-- [x] Phase 3 – Expose `/reload>` command and emit change summary (file values override env on reload)
-
-## Phase 1 Notes (in progress)
-- Current config flow: each entry point calls `appconfig.Load(logger)` which merges defaults + `config.toml` + env overrides, then copies fields into long-lived structs (e.g. `lsp.Server`).
-- LSP server captures many scalar copies (`maxTokens`, prompts, triggers, stats window), so runtime changes require a reapply step that updates these cached fields plus the `llm.Client` instance and stats window.
-- Proposed shape: introduce a central runtime config manager wrapping `appconfig.App` with an `RWMutex`, diff helpers, and subscription callbacks. All components pull the latest snapshot or subscribe to updates instead of keeping independent copies.
-- Reload path should reuse shared loader logic that can optionally skip env overrides so `/reload>` can make file values authoritative.
-- Applying updates must be atomic per component (e.g. server lock + swap) and should emit a structured list of changed settings for user feedback.
-- Manager responsibilities: (1) hold current snapshot, (2) surface `Subscribe(func(old, new appconfig.App))` for live update hooks, (3) expose `Reload(ctx)` that re-parses `config.toml`, produces diff keys, updates snapshot, and returns the changes for logging/UX.
-- LSP integration: pass manager into `RunWithFactory`, add `Server.applyAppConfig(cfg appconfig.App)` guarded by lock, rebuild `llm.Client` when provider/model change, refresh prompts/triggers/debounce/temps, and re-run `initializeModelConfig`/stats window.
-- Logging/stats: ensure updates propagate (e.g., call `stats.SetWindow` within manager or server update when `StatsWindowMinutes` changes) so runtime metrics align with new config.
-- `/reload>` command: hook into existing chat command detection (extend `chatCommandResponse`) to invoke manager `Reload`, then write the diff summary back to the buffer as a synthetic assistant response.
-
-## Phase 2 Notes (progress)
-- Added `appconfig.LoadWithOptions` to support skipping env overrides and introduced a `runtimeconfig.Store` (subscribe + diff) as the runtime configuration backbone.
-- `hexailsp.RunWithFactory` now builds a shared store, subscribes the LSP server to config updates, and rebuilds the LLM client + stats/log settings when config changes.
-- Implemented CLI-style slash commands in the LSP (`/reload>` currently) so runtime reloads can be triggered without restarting; reload skips env overrides and reports diffed keys.
-- Remaining work: surface change summaries inside the server logs, ensure other entry points (CLI/action) share the same store, and add verification around env override precedence.
diff --git a/PLAN2.md b/PLAN2.md
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-# Per-Surface LLM Model Configuration Plan
-
-Goal: allow users to configure distinct LLM models for (1) code completion, (2) code actions, (3) in-editor chat, and (4) the `hexai` CLI while keeping defaults sensible and maintaining backward compatibility. The new options must remain hot-reloadable via the existing runtime config store.
-
-## Phase 1 – Configuration Design
-- [x] Audit current config structures (`internal/appconfig`) and identify the model/temperature fields each surface consumes.
-- [x] Propose TOML schema extensions (e.g., `[models] completion = "..."`) plus environment variable overrides.
-- [x] Define precedence rules and fallback behavior when only a global model is provided.
-- [x] Sketch migration approach (default legacy fields map to all surfaces).
-
-## Phase 2 – Loader & Runtime Store Updates
-- [x] Extend `appconfig` to parse per-surface model settings (and optional temperature overrides) with validation.
-- [x] Update `runtimeconfig.Store` diff/flatten logic to include the new fields and guarantee reload propagation works without restart.
-- [x] Ensure reload summaries list per-surface changes cleanly.
-- [x] Add unit tests covering config parsing, env overrides, and diff output, plus runtime reload coverage.
-
-## Phase 3 – Surface Wiring
-- [x] Completion: adjust LSP completion code to pick the configured completion model, falling back to provider defaults.
-- [x] Code actions: ensure code-action prompts and CLI action runner request the configured model.
-- [x] In-editor chat: pass chat-specific model to chat requests and CLI chat command handling.
-- [x] Hexai CLI: respect the CLI model when building `llm.Config` or request options.
-- [x] Provide logging to confirm which model each surface uses for easier debugging.
-
-## Phase 4 – Validation & Docs
-- [x] Add integration/unit tests covering each surface model selection path.
-- [x] Verify runtime reload switches models without restart (including diff output).
-- [x] Update docs (`docs/configuration.md`, examples) with new keys and environment variables.
-- [x] Announce in scratchpad or release notes placeholder for future update.
diff --git a/PLAN3.md b/PLAN3.md
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-# Parallel Provider/Model Comparison Plan
-
-Goal: allow configuring multiple provider:model pairs per surface so users can run a local and cloud LLM side-by-side for manual comparison. Completions should fan out and show one suggestion per configured entry; Hexai CLI should emit one response per entry; code actions remain single-provider.
-
-## Phase 1 – Configuration & Schema
-- [x] Audit existing per-surface config to support arrays of provider:model entries (preserving current single-entry behavior by default).
-- [x] Design updated TOML and env schema (e.g., `[[models.completion]] provider="openai" model="gpt-4o"`).
-- [x] Define merge, validation, and backward-compatibility rules (single entry auto-wraps into list).
-
-## Phase 2 – Runtime Plumbing
-- [x] Extend appconfig/runtime store to emit ordered slices for multi-entry surfaces, including diff output.
-- [x] Update request-spec helpers to iterate across configured entries, building dedicated request specs (and caching clients per provider/model combo).
-- [x] Ensure logging/stats capture provider/model context per entry.
-
-## Phase 3 – Surface Implementations
-- [x] Completion: fan out requests concurrently, gather one suggestion per entry, and surface them distinctly to the editor (labelled with provider/model).
-- [x] CLI: run all configured providers in parallel and print separate responses per entry with stats.
-- [x] Code actions: keep single-provider flow and warn/ignore additional `[[models.code_action]]` entries.
-- [x] Add concurrency safeguards (debounce/throttle gate still respected before fan-out).
-
-## Phase 4 – UX & Validation
-- [x] Tests covering multi-entry parsing, diffing, and surface behavior (expanded CLI/LSP/appconfig suites).
-- [x] Update docs and example TOML with array syntax and dual-provider guidance.
-- [x] Capture lessons/issues in scratchpad for follow-up polishing.