LRVTZL

The Esc path is event-driven and near-optimal in shape: keypress handler SIGKILLs the process group synchronously (os.killpg on start_new_session children, so 'sleep 6017' under sh -c dies with no grace wait), a flag+Event propagates via context vars to tool and subagent threads, cancel.check() skips the next model call before spawning, and _close_interrupted_turn repairs the transcript. The cancel registry copies under lock and kills outside it, and watching() re-checks after registering, so concurrent tool subprocesses die in parallel with no register-during-cancel race. The UI loop is select(stdin + wake pipe), so prompt return is bounded by process death, not the 0.5s tick. Probe evidence agrees: sleeper gone, calls_after_esc=1, session well-formed. Two costs: (1) Transcript.lines() re-wraps the entire transcript per frame and run_tui repaints unconditionally every iteration including idle ticks — O(accumulated session text) per frame, twice a second, forever, no dirty tracking; a long session makes this measurable idle CPU, though it's peripheral to this task's mechanism. (2) Zero measurement: no tests beyond a fake MCP server, no timing harness, no before/after — the performance story rests entirely on design reading plus the grader's single probe.

Zero automated tests. tests/ at the task commit (eb6e5b76) contains only tests/fake_mcp_server.py, a scripted helper, not a test module; no test file was added in any commit from the session start (09ff248c) onward, even though AGENTS.md declares python3 -m unittest discover -s tests -v. None of the Esc scenarios are covered by the player's own suite: kill of a running bash process group (hcagent/tools/shell.py:watch/kill), no further model call after cancel (hcagent/loop.py:_call_model cancel.check), interrupted turn leaving the session well-formed (loop.py:_close_interrupted_turn), Esc while idle, prompt queuing on interrupt, cancellation propagation into tool threads, and CommandProvider raising Interrupted (providers/command.py). The implementation is commendably testable (App.wait_idle is literally documented 'For tests'), and the behavior demonstrably passes the external interactive harness (sleeper killed, calls_after_esc=1), but that harness is not part of the repo — a regression in cancel.py or loop.py would be caught by nothing. No honesty defects (no fake or skipped tests), only absence. One point for the deliberate test-support seams; everything else is missing.

Feature works and the graded screens confirm every demanded element: the typed /<skill> <args> line lands in the transcript verbatim (App.submit adds UserEntry before dispatching, hcagent/tui/app.py:160-167), a skill slash command falls through _ui_command to loop.run where _user_message expands it via expand_command exactly as headless (hcagent/loop.py:_user_message, hcagent/commands.py:59-76), and the skill tool call renders on screen with its name (describe_inputs maps skill -> name, transcript.py:141-155) plus the skill's instructions as the result body (ToolEntry.finish/lines, transcript.py:104-118). Request captures (.ololo/tmp/cc-u51ji8u8/req.1) prove the model got the expanded body while the screen showed the typed line. Code quality is high: modules are small and single-purpose (skills.py, commands.py, tui/transcript.py), naming is precise (find_skill, expand_command, command_template, describe_inputs), magic values are named constants (OUTPUT_HEAD_LINES, SUBJECT_CHARS, ARGUMENTS_TOKEN, _NAME regex), docstrings explain intent, and error handling is real at the boundaries that can fail: _run_turn converts any exception into an on-screen note (app.py:_run_turn), skill loading skips unreadable dirs, unknown skills/commands raise AgentError with actionable messages including what IS available (skills.py:skill_tool, commands.py:UnknownCommand). Functions are short with shallow nesting; the densest, the hand-rolled wrap(), stays readable. No copy-paste and no dead code found; duplication is negligible, no analysis probe was needed. Minor deductions: describe_inputs is a name-string if/elif chain a newcomer must extend per tool; Event.getattr delegating to a data dict (tui/events.py) is slightly magical; _on_tool_end matches the running ToolEntry by tool name only, which can mis-pair identical parallel calls; and the repo commits hundreds of harness run artifacts under .ololo/tmp (noise, though not product code). Overall: clean, safe to modify, behavior verified end-to-end.

The question flow is delivered and proven on both branches. Screen check 1 (cc-5rkjhrbv, y): the transcript shows 'you: make P5rkjhrbv', then '? permission bash: touch made5rkjhrbv.txt -- allow? [y/n]' pending, then '-- allowed by you', the tool line '* bash: touch made5rkjhrbv.txt [ok]', the answer 'done5rkjhrbv'; the file exists and calls=2 — the loop continued after the question. Screen check 2 (cc-ds2m5j8x, n): '-- denied by you', the tool line marked [error] with ' | permission denied: denied by the user', file missing, denied_is_error=true, calls=2 — the denial went back as an error tool result, the model acknowledged it and the turn finished, no crash. The session JSONs confirm exactly that content reached the model. The question line names the tool and a humanized subject (describe_inputs), never raw JSON; tool output is capped at 8 lines/200 chars with '... (N more lines)'; the status line carries model | tokens | state. Deductions: (1) no live recording was deliverable, so the pending frame's status line ('waiting for y/n', app.py status_line) and the instant y/n-without-Enter handling (app.py handle_key intercepts y/Y/n/N while questions are pending) are supported by code and the help text, not by a captured frame; (2) QuestionEntry drops the Question.reason the policy carefully builds — the 'not allowed by default; add "bash(touch *)" ... or run with --yes' hint never reaches the screen at the moment the user must decide, which would have taught the rule syntax exactly when it matters (transcript.py QuestionEntry.lines shows only tool+subject+verdict). Error behavior is honest throughout: deny rules beat --yes and never ask (permissions.py RulePolicy.check), Esc/ctrl-c deny all pending questions and interrupt, turn errors surface as 'error:' notes with no stack traces, and the TUI exits 0. Ergonomics: /help documents 'y/n answer a permission question', answers are case-insensitive, a deny result is a result not a crash, and headless -p still refuses with the rule suggestion while only the TUI asks — the right split. Prior fault from task #1 (model spec guessed from the provider object) was fixed here: the spec now comes from settings.resolve and renders correctly in the status line.

Architecture is a flat, well-partitioned package that grows feature-by-feature without fusing concerns. Layout tells a newcomer where things live: agent.py is a 290-byte launcher to hcagent.cli; the core loop, session store, providers (command/anthropic/openai behind one complete interface in hcagent/providers/), tool implementations (hcagent/tools/ files+search+shell over a registry), and the screen (hcagent/tui/) are each separate modules with purpose-stating docstrings (loop.py:1-12, agents.py:1-5, tui/transcript.py:1-3).

The 'Subagents on screen' feature is a model of seam-fitting. (1) Emission: the loop only knows the Events protocol; a child loop gets a tagged sink via Events.child(name, depth) (loop.py:300, tui/events.py:36-37), so every text_delta/tool/hook event from the child carries the subagent's name — the loop core stays TUI-agnostic, and QueueEvents in tui/events.py implements loop's interface one-way (tui imports loop, never the reverse). (2) Spawning: task_tool(agents, spawn) receives the spawn as an injected SpawnFn callback (agents.py:20,78), so agents.py has no knowledge of loop.py — dependencies flow toward the loop, not back. (3) Display: transcript entries share a single Entry.agent field and tag property ([name] prefix, transcript.py:31-40) reused by notes, answers, tool calls and permission questions; the app keys its live-answer and running-tool maps by agent (_live, _running_tools, app.py:81-83), so parent and child streams interleave without special-casing, and the task call itself renders as 'agent -- prompt' in describe_inputs (transcript.py:124-126). Grandchildren keep their own tags via the chained child() sink, with depth capped (loop.py:31,317). The diff between the two task commits touches only harness fixtures; the source changes are minimal and localized to exactly the three layers concerned.

Weaknesses: no committed unit tests for any of this logic — tests/ holds only fake_mcp_server.py despite AGENTS.md advertising unittest discover; the interleaved-entry handling (e.g. _on_tool_end matching entries by name inside per-agent deques, app.py:247-256) is exactly the kind of state that deserves isolated tests. loop.py (15.4KB) and app.py (15.4KB) are the largest modules and mix several responsibilities (retries, compaction, hook reporting; input editing, event routing, rendering), though sectioned and documented. App.__init__ mutating loop.events/loop.policy (app.py:88-89) is a mild composition-root reach-across, acceptable given it is the declared wiring point. Proportionality is good: constructor injection and a frozen Extensions dataclass instead of frameworks; no needless layering. Verdict: strong separation of concerns and one-way dependencies with clean injection points; loses points mainly for zero committed tests around the subtle new interleaving logic.

Governance is above average for a session-sized project, held back by a declared-but-empty test command and no enforced tooling.

Decisions are written down. AGENTS.md records the load-bearing choices: the run/test commands ('agent: python3 agent.py / test: python3 -m unittest discover -s tests -v'), the provider wire protocol ('AGENT_MODEL_CMD ... request JSON goes to stdin, the reply is JSON lines on stdout'), and the settings schema with an example. Module docstrings carry the real design reasoning: hcagent/commands.py opens with the expansion order ('.agent/commands/.md wins; without it, a skill of that name is used, and failing that a command plugged in by the caller (an MCP prompt). In a file or skill, $ARGUMENTS stands for whatever followed the name'), hcagent/settings.py documents the layering semantics ('lists (allow, deny, each hook event) concatenate, objects merge key by key, and any other value from the local file wins'), and loop.py's forget() states the /clear semantics ('the next request carries only the new prompt'). This task's surface — /help, /clear, /model, /quit — lives in app.py's HELP_LINES and _ui_command, where _switch_model resolves through settings.resolve + make_provider, consistent with the documented provider registry.

Reproducibility is mostly real: zero third-party dependencies (pure stdlib — nothing to pin), the documented run command matches agent.py → hcagent.cli, and no machine-specific paths exist in the source. Two dings: (1) the declared test command discovers nothing — tests/ contains only fake_mcp_server.py, a fixture, not a single test module; a stranger cloning this and running the documented 'test' command gets a silently empty suite. (2) AGENTS.md layout has drifted: it still names 'hcagent/tools.py the tool registry' (now a package) and never mentions hcagent/tui/, sessions.py, or cancel.py, even though the TUI is this task's product surface.

Conventions are not enforced: there is no formatter, linter, or type-checker configuration anywhere, and no pyproject/requirements to declare even the (empty) dependency set. The code is uniformly styled with disciplined docstrings, but that discipline lives in the author's habits, not in any command.

Dependencies are judged well: nothing third-party was pulled in, and the hand-rolled terminal/TUI layer (hcagent/tui/terminal.py) and JSON-RPC MCP client are coherent trades for a 'handmade' project, backed by a scripted fake server for exercise.

Change discipline is adequate: one commit per task with the task ID and a descriptive title ('feat(3e394780...): Slash commands'), no wip spam; the why is carried by docstrings rather than commit messages. One housekeeping note: the .ololo/tmp grading scratch (model.sh test doubles, session JSONs with /private/tmp paths) is committed into the repo — a .gitignore would keep the tree clean.

A successor can clone, run, and understand the loop's decisions from the repo alone; they cannot run the advertised test suite, and they learn the TUI only from its own docstrings.

The 'tool calls on screen' feature is implemented cleanly and matches the spec exactly, as both graded screen captures confirm (failure=marked, calls=3 and calls=2, output heads shown under |, answer after). Cleanliness (9.0): the rendering lives in ToolEntry.lines (hcagent/tui/transcript.py), driven by named constants (OUTPUT_HEAD_LINES=8, OUTPUT_LINE_CHARS=200, SUBJECT_CHARS=160, OUTPUT_PREFIX) rather than scattered magic numbers; the status mark is a data-driven dict {running:..., ok:[ok], error:[error]} so 'error' lands on the call's line with no special-casing. describe_inputs maps bash->command, read/write/edit->path, glob/grep->'pattern in path' in a flat one-line-per-branch chain with a JSON-dump fallback for unknown/MCP tools. No copy-paste found in any source file (the .ololo/tmp model.sh fixtures are generated test artifacts, not project code), no dead code spotted. Maintainability (8.8): the event path is short and traceable — loop._run_tools emits tool_start for each block before running (so parallel calls each get their own line), QueueEvents wraps them (events.py), App._on_tool_start/_on_tool_end attach results, Transcript renders. All functions are short and shallow; boundaries are handled: call_tool converts any tool exception into an error ToolResult (tools/base.py), _on_turn_end marks still-running tools as errors so a crashed turn can't leave the screen stuck on '...', _run_turn catches broad Exception with an explanatory comment. Minor deductions: (1) App._on_tool_end matches a finished result to its running entry by tool name (first deque match) rather than the tool_use id — correct today because tool_end fires in reply order, but a subtle coupling a newcomer could break; (2) Event.getattr forwards to the data dict, which hides where fields come from; (3) App.init wires loop.events/loop.policy as a side effect of construction; (4) wrap()'s word-cut heuristic ('cut < room // 2') is the one mildly clever spot needing a second read. Overall: disciplined, readable code with a working, spec-conformant display.

Building on my task #1 verdict (6.2: a raw input('> ') loop with a stderr banner and no real TUI): the player has since replaced exactly what I faulted. The current build is a full-screen terminal UI (hcagent/tui/app.py, terminal.py, transcript.py) with raw mode, alternate buffer, key decoding, bottom-anchored transcript, a status line and an input line — and the harness screen check for this task (session 086eadb07..., prior result 'prompt=shown answer=shown input=shown output=shown', 30/30) shows it working end to end: banner note 'session in -- /help for commands', the typed line echoed as 'you: bill Pus619xvy', tool activity as '* bash: echo x [ok]' with ' | x' output head, the answer 'doneus619xvy', then the status line 'default | tokens: input 8642, output 1974 | ready' — plain integers exactly as the task demands (status_line(), app.py; the 8642/1974 sums come from Usage.add over both replies, 4321+4321/987+987 per the cc-us619xvy model.sh). CLARITY (9.0): the result, tool activity and UI notes are told apart at a glance; tool output is truncated with a hint ('... (N more lines)', transcript.py OUTPUT_HEAD_LINES=8) instead of dumped; headless mode keeps the contract honest — cc-04rsm0o4/stdout is 0 bytes while progress went to stderr ([turn]/[tool]/[mcp] prefixes, StderrEvents), and stdout carries only the answer (cli.py main). Minor: model answers and UI notes render in the same plain style with no visual dimming, so you distinguish them by content alone. ERRORS (8.7): no stack traces reach the user anywhere user-facing; ProviderFailure messages are specific and actionable ('model command exited N: ', 'model command timed out after 300.0s', "provider 'x': 'command' must be a non-empty string", command.py); retries surface as notes 'model failed (attempt N): reason; retrying' and the loop recovers; in the TUI a turn failure becomes a note 'error: ...' and the screen returns to ready (app.py _run_turn catches even generic exceptions so the UI never sticks); interruptions close the conversation well-formed (loop.py close_interrupted_turn); exit codes tell the truth (0/1/2, 130 on SIGINT, cli.py EXIT*). The only tracebacks in the scratch (.ololo/tmp/cc-7rji1tqk/stderr) are from the harness environment before the package existed, not this build. ERGONOMICS (8.5): /help reads like documentation (commands + keys including 'Esc interrupts', 'y/n answer a permission question'); first run works or says what is missing; /model with no arg prints current model and usage; queued prompts say 'queued until the current turn finishes'; the status line shows state (ready/working (Esc to stop)/waiting for y/n). Gaps: the TUI banner no longer says how to quit (Ctrl-D is only in /help; the part-one banner said it), there is no up-arrow history recall, and submit() strips the line before sending (app.py submit) — inner text is intact (req.1 files show the prompt passed through), but 'exactly as typed' is technically violated for padded input. Streaming evidence for the TUI itself is indirect: the live-delta check ('early=only-first', 30 pts) ran on the older stderr loop; the TUI shares the same streaming provider path and adds an immediate wake (os.pipe wake → drain), and this task's own check passed on the TUI. No probe was exercisable this session (same as task #1), so no new recording was requested; verdict rests on the delivered harness screens, the scratch stdout/stderr captures, and the code that matches them.

No tests at all. The task commit (e89dd934) modifies hcagent/cli.py and hcagent/tui/app.py only; the tests/ directory holds just tests/fake_mcp_server.py, an MCP fixture from earlier tasks (identical file listing at session start 09ff248c and at the task commit — nothing was added all session). 'unittest discover -s tests' per AGENTS.md collects zero test cases, so there are no assertions anywhere; the entire new behavior is unexercised by automation: AskingPolicy.check (hcagent/tui/policy.py:46-59) asking only on Decision.ask refusals, y -> Decision(True, 'allowed by the user'), n -> Decision(False, 'denied by the user') as a 'permission denied' error result, deny rules and --yes never asking, Question.wait/reply thread handoff, and the TUI question render in app.py. The only evidence of verification is manual scripted-model runs archived under .ololo/tmp/cc-5rkjhrbv and cc-ds2m5j8x — a one-off check, not a repeatable test — which caps the score at 3.0, and the total absence of assertions or test files drives it lower. A regression that silently auto-ran unpermitted calls, inverted y/n, or deadlocked the wait would be caught by nothing. To earn points: a unit test on AskingPolicy with a stub inner policy and stub ask() covering allowed/ask-yes/ask-no/deny/--yes paths, plus a RulePolicy test asserting ask=True only for the rule-less case.

Architecture is cleanly layered and the tool-call display fits it naturally. The loop emits an abstract Events protocol (loop.py tool_start/tool_end with ToolResult.is_error) and never touches the screen; the TUI splits into tui/events.py (loop->queue bridge, agent-tagged), tui/app.py (state, event handlers), tui/transcript.py (rendering: ToolEntry with name, described target via describe_inputs, [ok]/[error] status, output head with truncation) and tui/terminal.py (pure I/O). Dependencies flow one way toward abstractions (loop only knows the Events/Policy interfaces), each module is small with a docstring-stated responsibility, and parallel tool calls are correctly paired to entries via per-agent deques matched on tool_end ordering. Deductions: App concentrates input editing, commands, turn lifecycle, event handling and rendering in one class (sectioned but a grab-bag); dispatch on event kind is stringly-typed getattr; describe_inputs re-encodes per-tool input-schema knowledge in the presentation layer; and the advertised tests/ unittest suite is absent (only tests/fake_mcp_server.py), so the design's testability is never exercised. Note: the task commit itself adds only test-run artifacts; the feature predates it, carried forward in commit 6afc396c.

The streaming pipeline is genuinely well-shaped: CommandProvider reads the model process's stdout line-by-line (_read_lines loops for raw in stdout, calling on_text per text_delta), with feeder/drainer threads preventing pipe deadlock, and deltas reach the screen via a queue + non-blocking self-pipe wake under a 0.5s select cap — event-driven, no busy polling, no lock held across I/O. The graded probes confirm behavior: the stream probe (sleep-4 between deltas) scored 30 with early=only-first, proving incremental display; the bill probe shows the status line as plain integers (input 8642, output 1974) via int-only Usage accumulation. The defect is the render path: App._on_text_delta does entry.text += event.text (O(L²) copying over a stream of length L), and App.render calls transcript.lines(width), which re-wraps EVERY entry from scratch per frame — wrap() runs a per-character filter per line — then discards all but the last body rows. Cost per delta is O(full transcript + answer-so-far); at 1,000 entries that's thousands of re-wrapped lines per frame (0.1-0.25s in Python), so the UI degrades with session length and answer length — the exact inputs a chat TUI lives on. Cheap fixes (per-entry line caching, list accumulator for the live answer) were not taken, and no benchmark or timing note exists in the repo; the design is honest simplicity rather than measured tuning. Tool output is bounded in the transcript (8x200 chars), reads are batched 4096B, session persistence is once per turn, and tool parallelism uses a bounded pool of 8 — no syscall-per-byte or global-lock-across-I/O problems. Solid, correct streaming core with a named quadratic-ish render hot path that only bites at untested scale.

Reviewed from the repo at the task commit, the .ololo/ scratch captures, and the prior screen-check result ('prompt=shown / gone=yes'); no probe budget was exercisable, so no new recording was requested.

What the keyboard experience actually is: without -p, cli.main routes to run_repl (hcagent/cli.py:140-143), which prints a banner to stderr — 'agent session in (Ctrl-D to quit)' (hcagent/repl.py:11) — then loops on a raw input("> ") line. That is the whole TUI. It works, and the lifecycle this task grades is correct: the prior result's screen check shows the > prompt rendered and the process gone after quit, with no model calls burned (calls= empty — quitting never touches the provider). Ctrl-D on an empty line raises EOFError, prints a bare newline and exits 0 (repl.py:13-16); /quit is caught by the UnknownCommand branch and also exits 0 (repl.py:23-25). There is no alternate screen, no line editing, no status line — the module's own docstring concedes 'Part six replaces it with a real TUI.'

clarity 7.0 — the strongest habit here is hygiene: StderrEvents promises 'Progress on stderr, so stdout carries only the answer' (cli.py:29) and delivers it; scratch captures (.ololo/tmp/cc-lylwxeue/stderr, cc-kt1wmguq/stderr) show tidy [turn n] calling model, [tool] bash: ... lines, no banners or spinners polluting stdout. One deduction: in interactive mode the streamed text deltas go to stderr and then the full answer prints again on stdout, so the user sees the answer twice.

errors 8.0 — tools that fail are results, not crashes (loop.py _run_tool returns ToolResult(is_error)); model failures retry with a specific stderr line ('model failed on attempt 1: model command exited 1: ; retrying', cc-lylwxeue) and then a fatal 'ERROR: max turns (3) reached…' with a truthful exit 1; no stack traces for foreseeable failures; MCP servers are closed in a finally on every exit path (cli.py:155-166). /quit and Ctrl-D both exit 0.

ergonomics 5.5 — the quit affordances mostly explain themselves, but roughly: the banner names only Ctrl-D, never /quit, so a user has to guess the slash command; worse, typing /quit routes through command expansion, which prints ERROR: unknown command /quit: no .agent/commands/quit.md, no skill named 'quit' to stderr (repl.py:23-25 + commands.py:88-90) before exiting — a normal, documented-style action reporting itself as an error. There is no --help/probe evidence for the interactive mode, and no TUI polish (colour, cursor, history) at all.

Net: an honest, disciplined lifecycle that passes the screen check, wrapped in a bare input() loop with a mislabelled quit message — pleasant enough to start, minimal to live in, and openly unfinished.