CZH2H5

Decisions are well written down: AGENTS.md carries the run command, CLI surface, the command-provider JSON-lines protocol, the settings schema and precedence (--model > AGENT_MODEL_CMD > settings file), and a layout map; module docstrings restate the contracts (settings.py resolve, errors.py taxonomy, http.py 'stdlib only', anthropic.py 'wire shape is the harness's native shape'). Dependencies are a model trade: zero third-party, urllib/subprocess only, deliberately documented. Reproducibility of the run path is real — agent.py's sys.path fix exists precisely so the documented command works from any cwd. Three governance defects hold it back. (1) The one declared convention is fake: AGENTS.md says 'test: python3 -m unittest discover -s tests -v' but no tests/ directory exists in the repo — a command that fails on clone. No formatter/linter/type-checker either. (2) Change discipline is absent: the diff from the first 'Set up the project' commit to this task commit contains zero source changes — all code (providers, settings resolution, both HTTP providers) was committed whole in that initial commit, and every task commit since, including this one, adds only .ololo/tmp check-run artifacts. The commits name tasks, not reasons, and the code never evolved in reviewable steps. (3) The repo commits scratch artifacts with machine-specific absolute paths instead of ignoring them. Also no declared Python version. The functionality itself checks out — the committed run evidence shows --model selecting provider b with "model" in every request, the default resolving to provider a, and an unknown provider producing 'ERROR: unknown provider ... (declared: a)' with a non-zero exit — but the process around it (phantom test suite, mega-commit history, artifact pollution) is what a successor would have to forgive.

Governance is a mixed bag: strong written decisions and honest, stdlib-only dependencies, undermined by a phantom test command and a history that never shows the work.

Decisions written down (good). AGENTS.md carries the load-bearing choices: the usage block documents --max-turns ('cap on model calls (default 50)') and --output-format json, and the 'Model protocol' section specifies the command provider's JSON-lines wire format with a literal settings.json example. Docstrings carry real reasoning, not captions: errors.py opens 'Errors that end a run. The CLI prints them as ERROR: ... and exits non-zero'; providers/base.py opens 'The loop only ever sees Reply objects'; anthropic.py: 'Its wire shape is the harness's native shape'. The implementation matches the prose: loop.py's _call_model checks 'turns >= max_turns' before incrementing, so the call past the cap is never made (transcript cc-kt1wmguq shows 3 calls, then 'ERROR: max turns (3) reached before the model finished' on stderr, empty stdout), and RunResult.as_dict in loop.py emits exactly the required receipt — cc-gaii3ack stdout shows '{"result": "donegaii3ack", "session_id": "16810cf0...", "turns": 2, "usage": {"input_tokens": 2500, "output_tokens": 90}}', a correct sum (1200+1300/34+56) with a uuid4-hex session id.

Conventions the tooling enforces (weak). The single declared enforcement is a lie: AGENTS.md says 'test: python3 -m unittest discover -s tests -v', but no tests/ directory exists at the task commit or anywhere in the history. There is no formatter, linter, or type-checker config either — the type hints are good, but nothing checks them. A successor running the documented test command hits an error on the first try.

Reproducibility (mixed). Zero third-party dependencies means nothing needs pinning, and 'agent: python3 agent.py' matches reality: agent.py's sys.path insert fixed the ModuleNotFoundError visible in the early transcripts (cc-7rji1tqk), and the committed run logs show the declared command working end to end. Providers resolve from the working directory's .agent/settings.json as documented; no hidden global state in the program itself. The .ololo/ run artifacts committed into the repo (30+ files of request transcripts with the player's absolute paths) are clutter, though the harness's own 'ololo snapshot' commit suggests they are platform-managed, so lightly weighted.

Deliberate dependencies (excellent). Everything is argparse/subprocess/urllib/uuid/dataclasses. The thin hand-rolled HTTP in providers/http.py and the three providers behind one interface is exactly the trade this project is about — no dependency pulled for one helper, no hazardous wheel reinvented.

Change discipline (weak). The entire codebase — loop, retries, tools, providers, CLI, max-turns, JSON output — landed in one commit (88ffce4, 'One prompt, one answer'); this task's own commit (0ae7aa8) contains zero source changes, only .ololo/tmp evidence transcripts. The messages name tasks, but the diffs don't: a successor cannot see how --max-turns or the receipt came to be, only the final state. That the pre-built code nonetheless satisfies the spec speaks well of foresight, less well of process.

Net: a crisp README-grade AGENTS.md, real recorded decisions, and exemplary dependency restraint against an unenforced test claim, no tooling, and log-only feature commits. 6.2.

Clean, well-factored implementation of the turn cap and JSON receipt. The cap check in loop.py _call_model fires before the provider call (verified: artifact cc-kt1wmguq shows calls=3, ERROR: max turns (3) reached… on stderr, exit 1), and RunResult.as_dict emits the exact protocol shape (verified: cc-gaii3ack stdout sums usage 2500/90 over 2 turns). TurnLimitError slots into the existing AgentError hierarchy, so stderr/exit handling needed no new code. Across the whole ~1.1k-LOC tree: consistent descriptive naming, named constants for defaults and exit codes, no dead code, no copy-paste (the provider registry and shared http.post_json prevent it), shallow nesting, no function needs a scroll, and error handling covers every real boundary (bad settings, unknown provider, malformed replies, tool exceptions, HTTP/timeout/OSError) without ceremony. Minor deductions: the or 600 timeout is inline in all three providers instead of a named constant, and harness run artifacts under .ololo/tmp are committed into the repo. A newcomer could safely extend this codebase.

Architecture is clean, layered, and exactly proportionate to the task. Separation of concerns: cli.py only parses args/exit codes and wires collaborators; settings.py is pure model-resolution logic (Settings.resolve, settings.py:62 — --model > AGENT_MODEL_CMD > default, ConfigError for unknown providers, which cli.py:91 turns into ERROR: ... + exit 1); loop.py owns only the turn/retry/tool cycle and never learns what kind of provider it talks to. Component boundaries: the Provider.complete(request, on_text) -> Reply contract (providers/base.py:64) is a crisp seam — the wire shape (Reply, Usage, ProviderFailure) is the single currency across command, anthropic, and openai providers, each independently readable and replaceable; make_provider (providers/init.py:15) is a small registry factory. The task's requirements all map to named components: .agent/settings.json providers+default (settings.py:33), model field in every request (loop.py:80, confirmed in captured req.1 payloads), and both HTTP providers (anthropic.py, openai.py) sharing stdlib-only http.py plumbing — shared transport extracted once instead of duplicated. Dependency direction flows one way: cli → loop/providers → base; loop.py depends only on the Provider abstraction and Events, not on settings or any concrete provider; providers depend on settings only for the ModelChoice value type. Units are small and single-purpose (largest file ~4.4 KB); no needless layering for a tool this size. Minor defects: (1) the repo commits runtime scratch artifacts under .ololo/tmp/cc-*/ (model.sh fixtures, captured req/stderr transcripts), noise that muddies where things live; (2) AGENTS.md declares test: python3 -m unittest discover -s tests but no tests/ directory exists — documentation contradicts the file tree; (3) HTTP providers emit text_delta only after the full response rather than streaming, a behavior nit with no structural impact.

Judged from the tool's own delivered output: four check captures in .ololo/tmp/ (each with exact stdout/stderr), the source, and AGENTS.md. No .ololo/-done.md completion note; no agent stats reported.

clearity/clarity 9.5 — The core requirement is proven byte-for-byte by the captures. cc-crsusavu/stdout is exactly 'Acrsusavu\n' (10 bytes) with stderr just '[turn 1] calling model'; cc-doluqqck/stdout is 'first Ldoluqqck\nsecond Ldoluqqck\n' (33 bytes — two text blocks print as two lines, in order); cc-ojwvytqx shows the text_delta fragments echoed to stderr with a separating newline while stdout stays exactly 'S1ojwvytqx S2ojwvytqx\n' — streamed progress exists, and the answer is still printed once, from the final message, on its own stream. No banner, no spinner, no tool trace on stdout; exit=0 on all checks (prior results). The 4th check (model message with no text) also matched with empty output. The split of result→stdout vs. progress/diagnostics→stderr is textbook; JSON mode (--output-format json) gives a machine-readable result object for when you need more than the text.

ergonomics 9.0 — AGENTS.md reads like real documentation: usage line, flag semantics (-C, -p, --yes, --model p/m, --max-turns, --output-format json), the model-resolution order (--model > AGENT_MODEL_CMD > .agent/settings.json), the JSON-lines model protocol, and the JSON output shape — and the delivered parser (cli.py) matches it flag for flag, so the docs are truthful. First-run guidance is right by design: an unconfigured model produces 'no model configured: set AGENT_MODEL_CMD, pass --model, or add model to .agent/settings.json' (settings.py:66-69), which says exactly what is missing and three ways to fix it. The REPL banner (session id, cwd, 'Ctrl-D to quit') goes to stderr with the answer still on stdout. Only deduction: no --help capture was delivered, so the self-explanation under argparse is assumed, not shown.

errors 5.5 — Undemonstrated by output. No delivered capture exercises a failure path: no bad flag, no missing model, no model-command failure, no denied/failed tool. The designed story looks right (ProviderFailure carries the model command's exit code and last stderr line, command.py; retry and turn-cap notices on stderr; exit codes 0/1/2 and 130 on interrupt in cli.py; failed tool results fed back as is_error rather than crashing, loop.py:145-150), but per my mandate I cannot grade messages the runs never printed. The only error output ever captured in the repo is a raw 'ModuleNotFoundError: No module named hcagent.cli' traceback (cc-7rji1tqk/cc-cti8e9cv/cc-yyz0ydo8 stderr) — I judge those stale probes from the setup window, before the package existed, since all four of this task's own probes ran clean; still, it shows the entry point's worst-case face is a stack trace, and a delivered capture of a real failure would have settled this far above a mid score.

Net: a tool whose one-prompt-one-answer contract is exactly, verifiably right at the keyboard, with excellent self-documentation — held back from the top only because its error behavior was never shown, only described.

No automated tests exist in the repo: no tests/ directory, no test files, no runner config anywhere in the tree, and the root→task-commit diff (24e4ecab) adds none. The retry logic in hcagent/loop.py (3 attempts, 1s delay, ProviderFailure handling) and the ERROR:/exit-1 terminal path in cli.py are correct — prior results confirm exit=0/calls=3 on flaky-then-success and exit=1/seconds=3/error_line=yes on triple failure — but the only verification was manual smoke runs: ad-hoc stub model.sh scripts under .ololo/tmp/cc-* whose recorded stderr logs (e.g. cc-lylwxeue: two failure modes then success; cc-04rsm0o4: three failures then ERROR:) were inspected by eye. Those smoke runs picked exactly the right scenarios — non-zero exit, non-JSON last line, retry-then-succeed, three-strikes failure, attempt counting, promptness — but none of it is codified: no assertions, no repeatable harness, fixtures in scratch paths only. Any regression in MODEL_ATTEMPTS, the retry sleep, or the CLI error path would go undetected. Manual-only verification caps at 3.0; with nothing committed or assertable at all, this lands at 2.0.

Clean, well-factored implementation of the bash tool contract. tools.py:run_bash merges stderr into stdout via stderr=subprocess.STDOUT so output appears in true write order, appends a newline-guarded exit code: <n> and sets is_error on non-zero exit (proven in .ololo/tmp/cc-4beymgji/req.2: 'OUT...ERR...exit code: 3', is_error:true, 50/50 points). loop.py:run() feeds tool results back and continues the loop rather than stopping; -C cwd threads Session.cwd -> dispatch -> subprocess.run, proven by the pwd/touch transcript (cc-gjfor18w). Cleanliness 9.0: consistent descriptive naming, named constants, docstrings throughout, no dead code; the only duplication is the near-identical 5-line preamble across .ololo/tmp/*/model.sh, which is per-scenario harness scratch, not implementation code. Stale doc: AGENTS.md promises a tests/ unittest suite that does not exist at the task commit. Maintainability 9.0: short functions, shallow nesting, error handling at genuine boundaries (timeout/OSError in run_bash, malformed replies in Reply.from_message/from_chat_response, HTTP errors in http.py, commented broad except in dispatch so tool bugs cannot kill the loop). Minor: _run_tool unpacks block['name']/block['id'] unguarded (KeyError risk on loosely validated anthropic/openai blocks), and the timeout message gets a leading newline when partial output is empty. Overall a genuinely readable, safely-changeable codebase.

The protocol-critical cost — the per-call payload — is exactly right: an append-only Session.messages list, each request shallow-copying the history with zero re-parse or rebuild ("messages": list(self.session.messages) in loop.py), O(1) work per new turn and O(turns) bytes per call, which is the stateless-model protocol's own mandated shape. The committed trace cc-n46w7azs is measured evidence: six requests growing exactly +254 bytes per round (assistant tool_use + user tool_result), sequence 1 1 2 2 3 3 4 4 5 5, 5 results in order, exit=0, answer donen46w7azs — matching the 30-point pass. I/O shape is clean: one json.dumps and one socket write per call; tool output clipped at 100k chars bounds history growth against runaway commands; retries re-send without double-appending, and assistant messages are only appended after a successful reply so failures can't corrupt the transcript. No locks, no serialized independent work. Minor nitpicks only: the small fixed system prompt/tool specs are rebuilt per call rather than cached, and multiple tool_use blocks within one assistant message run sequentially — both trivial next to a network round trip and defensible simplicity for a one-tool loop.

Clean, well-proportioned layering for a small tool: agent.py launcher → hcagent/cli (presentation: StderrEvents adapter, exit codes, output formats) → loop.py (conversation loop, retries, turn cap) → tools.py registry, settings.py model resolution, providers/ package (command/anthropic/openai behind one Provider interface with shared http.py plumbing). Concerns are properly separated: the loop emits Events hooks instead of printing, stdout carries only the answer, the system prompt is isolated in prompt.py, and the error taxonomy (errors.py) is used consistently. Dependencies flow one way with no cycles; the loop depends only on the providers.base abstraction, and the provider factory registry makes extension a one-file change. Boundaries are hardened where it matters: ToolRegistry.dispatch catches tool exceptions so a tool bug cannot kill the loop, and Reply.from_message validates the wire contract at the provider boundary. The task's core protocol is correctly realized in loop.py run(): the assistant message is appended as received, tool_use triggers tool execution, and one user message of tool_result blocks (tool_use_id + content) is appended before the next model call — corroborated by the captured req.2 in .ololo/tmp/cc-64ngkdcf showing the exact three-message sequence. Extensive injection points (provider, tools, events, permission, streams) make units replaceable in isolation. Deductions: AGENTS.md documents a tests/ unittest suite and test command that do not exist in the repo (docs point to missing structure); the --yes flag and permission parameter are unwired/decorative; CommandProvider's timeout is hard-coded rather than configurable like its siblings. The openai provider and REPL are speculative for this milestone but defensible under the declared multi-part plan. Overall: genuinely good architecture for the size, docked for the documented-but-absent tests and the dead knob.