added /models cmd

README.md

@@ -66,6 +66,7 @@ All implementations read the same `model.cfg` and `system.txt` from the current
    - `/load <id>` — load a saved session (exact id or unique prefix) and continue from there
    - `/compact` — compact context down to the system message and a concise summary using the LLM; the compaction prompt is appended to the conversation to derive the summary, then the conversation is reset to `[system, summary-user-message]` (a fresh prefix, so downstream prompt-cache hits depend on the provider and are not guaranteed)
    - `/cfg <param> [val]` — inspect or update a configuration parameter in `model.cfg` live
+   - `/models` — query the `/models` path on the current inference endpoint and print a plain list of supported model IDs, marking the currently configured model with a leading `* ` (Go port)
    - `!<cmd>` — execute a shell command directly through `shell_exec` without adding the result to the conversation context (Go port)
    - `/help` — show all supported commands
    - `/clear` — reset the conversation to just the system prompt
@@ -112,7 +113,7 @@ Using these rules, everyone can build their own copy of Bantam from scratch in l
 2. Read model parameters from `model.cfg` (`key=value` format) and discover context window size.
 3. Prepare a new message list with the system prompt (`role: "system"`).
 4. Read the first command-line parameter. If non-empty, read user prompt from the specified file. If prefixed with `!`, execute the shell command directly via `shell_exec` and exit. Otherwise, append to `messages` (`role: "user"`), run `AL(cfg, messages)`, display token usage, and exit.
-5. Read user prompt from standard input (with `readline` line editing and history in `~/.bantam_history`; **Ctrl+J** inserts a real newline into the line being edited). If equal to `/quit` or EOF, exit. If equal to `/clear`, reset `messages` to step 3 and return to step 5. If equal to `/save`, write the whole `messages` array to `~/.bantam/sessions/<id>.json` (with an auto-generated summary) and return to step 5. If equal to `/list`, print saved sessions and their summaries and return to step 5. If starting with `/load`, replace `messages` with the saved session's messages (by exact id or unique prefix) and return to step 5. If equal to `/compact`, ask the LLM to summarize the conversation by appending the compaction prompt to derive the summary, replace `messages` with `[system, summary-user-message]`, and return to step 5. If starting with `/cfg`, display the current value (`/cfg <param>`) or update `model.cfg` live (`/cfg <param> <val>`) and return to step 5. If starting with `!`, execute the command directly via `shell_exec` without adding the result to `messages` and return to step 5. If equal to `/help`, print the command list and return to step 5. After every user turn and on exit, auto-save `messages` to `~/.bantam/sessions/autosave.json`.
+5. Read user prompt from standard input (with `readline` line editing and history in `~/.bantam_history`; **Ctrl+J** inserts a real newline into the line being edited). If equal to `/quit` or EOF, exit. If equal to `/clear`, reset `messages` to step 3 and return to step 5. If equal to `/save`, write the whole `messages` array to `~/.bantam/sessions/<id>.json` (with an auto-generated summary) and return to step 5. If equal to `/list`, print saved sessions and their summaries and return to step 5. If starting with `/load`, replace `messages` with the saved session's messages (by exact id or unique prefix) and return to step 5. If equal to `/compact`, ask the LLM to summarize the conversation by appending the compaction prompt to derive the summary, replace `messages` with `[system, summary-user-message]`, and return to step 5. If starting with `/cfg`, display the current value (`/cfg <param>`) or update `model.cfg` live (`/cfg <param> <val>`) and return to step 5. If equal to `/models`, query the `/models` path on the current inference endpoint and print a plain list of supported model IDs (the currently configured model marked with a leading `* `), then return to step 5. If starting with `!`, execute the command directly via `shell_exec` without adding the result to `messages` and return to step 5. If equal to `/help`, print the command list and return to step 5. After every user turn and on exit, auto-save `messages` to `~/.bantam/sessions/autosave.json`.
 6. Append user prompt to `messages` (`role: "user"`), run `AL(cfg, messages)`, display token usage, check 60% context threshold for auto-compaction, and go to step 5.
 
 ### Agentic loop (`AL(cfg, messages)`) function