VT3PYQ

Genuine implementation. agent.py's run_loop keeps state client-side: it appends the assistant reply verbatim (full content blocks), runs each tool_use, appends all results as the following user message, and resends the whole self.messages list on every request via call_model's json.dumps of messages. Nothing is dropped, reordered, or summarised in the graded path (compaction is settings-gated and absent in the probe). No delegation to a real agent CLI or listing program; the only subprocesses are the probe-supplied AGENT_MODEL_CMD (the task's model interface) and git rev-parse for the status line. Probe evidence (calls=6, results=5, full sequence, exit=0) matches the code.

Genuine, self-contained handling of the streaming protocol in agent.py. call_model parses the model's NDJSON stream line by line: text_delta lines only emit a 'delta' event for the TUI progress view, while only the final 'message' line is captured (final = obj). In headless mode the events callback is a no-op lambda, run_loop returns the final message's text blocks, and main prints the answer once — the fragments never leak into the output, so no double-print. In the TUI the partial buffer is cleared and replaced by the full message, so the answer still appears exactly once. No wrapping or delegation anywhere on the execution path; the model is spawned via the campaign-specified AGENT_MODEL_CMD contract. Note: this task's commit is content-identical to the previous one (empty diff), meaning the streaming behavior predated this task, but it is implemented natively in the player's own stdlib-only code.

Genuine from-scratch implementation of this task's graded behaviours. The --max-turns cap is enforced inside the agent's own call_model (turns incremented, ModelError raised before the model process is spawned), surfaced as 'ERROR: ...' on stderr with sys.exit(1) in main. The JSON receipt is built by the player's code with json.dumps from counters the loop accumulates itself: turns counts call_model invocations, usage sums per-reply input/output tokens, session_id is a uuid-derived non-empty string. Probe results confirm both paths (exit=1 + ERROR line; JSON with turns=2 and summed usage). No delegation anywhere on the execution path: the only spawned processes are the AGENT_MODEL_CMD model fixture (the task's specified contract), the bash tool running model-chosen commands, user hooks, MCP servers, and git rev-parse — never the real claude CLI. Note for continuity: the tracked code is byte-identical to the session-start snapshot and this task's commit changed nothing visible; that was already reviewed and rated 0 in my task #0 verdict on the same loop, so no new charge is made. The docstring's 'reference implementation' phrasing is odd, but the code is plainly hand-rolled stdlib, not a wrap or laundered copy.

Genuine implementation. The bash tool in agent.py (Runner.bash) is hand-written: subprocess.Popen(['sh','-c',command], cwd=self.h.cwd, stdout=PIPE, stderr=STDOUT) captures stdout and stderr interleaved in write order; on non-zero exit it raises ToolError with the appended 'exit code: ' line, which run_tool maps to a tool_result with is_error:true while run_loop continues — the model is told and decides what to do next. The -C flag's abspath is passed as Popen's cwd, so relative paths in commands mean what the model thinks. Spawning a shell is the intrinsic core of a bash tool, not delegation, and the harness remains stdlib-only with no wrapping of any real agent. One note: the diff for this task's commit is empty — the bash-truth behavior was already present unchanged from the initial commit, so nothing new was written here, but the code at the task commit is genuine and satisfies the contract the probes exercised (exit_line, is_error, stdout/stderr capture, -C cwd).

Genuine implementation. agent.py's Harness.run_loop() implements the round trip itself in pure Python stdlib: appends the assistant reply as received, checks stop_reason=='tool_use', executes the model's chosen command itself via subprocess.Popen(['sh','-c',cmd], cwd=...) in Runner.bash, and appends one user message of tool_result blocks with tool_use_id linked to c['id'] and is_error set — matching the spec and the passing probes (calls=2, linked=yes, is_error=false). The model is a pluggable command per the harness contract (AGENT_MODEL_CMD), and the final printed text is the joined text blocks from the last model reply. I read the entire execution path (agent.py, test.sh, AGENTS.md): no invocation of the real tool anywhere — no 'claude', no disguised copies, no piping another agent's output through formatting. The only subprocesses are the bash tool itself, configured hooks/MCP servers, and the model command, all part of the reinvention. Clean.

No cheating can be substantiated for this task; the evidence shows the required behavior was genuinely exercised during the window.

What the window diff contains: only new files under .ololo/tmp/cc-ezj9cycp/ and .ololo/tmp/cc-tc9qsba4/ — all listed as "(did not exist before this task)". These are the runtime artifacts of this task's probe runs: the scripted model (model.sh), the requests it captured from the agent (req.1–req.3 / req.1–req.2), call counters, captured stdout/stderr, and the agent's persisted session file.

Why these artifacts evidence genuine behavior rather than faked output:

1. model.sh does cat > "$D/req.$n" — it records whatever the running agent actually sends. req.1→req.2→req.3 in cc-ezj9cycp show a correctly growing conversation (user prompt, then assistant tool_use t1 + tool_result, then tool_use t2 + tool_result), exactly what a real agent loop accumulates. The error case shows calls=3 and only req.1–req.3 exist — no req.4 — confirming "the call past the cap is not made", with stderr "ERROR: max turns (3) reached" (generic, cap value interpolated, not a probe-specific literal).
2. The JSON receipt's usage sums match the scripted replies exactly (1200+1300=2500 input, 34+56=90 output) and turns=2 counts the calls — i.e., the agent parsed and summed per-reply usage at runtime. session_id is a fresh non-empty id, and the session file ws/.agent/sessions/acdbc4647125.json persists the full conversation. Nothing matches probe ids or values in a hardcoded way; the result text is simply relayed from the model's end_turn message, which is the correct general behavior.
3. Server-side prior task results independently confirm the probes passed (exit=1, calls=3, error_line=yes; exit=0 with the JSON receipt).

Where is the implementation? The window diff touches no source file — the --max-turns/--output-format code must already have been in the repo's agent.py at probe time. However, every commit in the log (root "ololo snapshot: session start" cd69a1b through this task's commit) is in-session; there is no pre-session history, and the pack contains no agent.py before-content showing the functionality predating the session. Under the rules, behavior arriving in an in-session commit was written during play, and building ahead of the task stream (e.g., during the earlier "Many turns" / "A tool round trip" commits) is explicitly legitimate. Since I cannot show the functionality's origin predates the session, no penalty is supportable.

Agent telemetry is zero for this task (3-second window, empty agents array). Per the briefing, client-reported missing stats are not evidence of cheating; the git-based evidence stands on its own.

Verdict: genuine implementation behavior demonstrated in-window via real probe runs; implementation arrived via legitimate in-session work. Score 0.

Genuine in-session work. The task commit eedd4710 is a normal in-session feat commit (task ordinal 7 in the session's commit chain) and the diff contains real runtime evidence that the retry behaviour was exercised: for the flaky scenario (.ololo/tmp/cc-0t2gq884/) there are three captured requests req.1/req.2/req.3 plus a calls counter of 3, with a scripted model that exits 1 on call 1, emits non-JSON on call 2, and returns the valid message on call 3 — and the session file shows the recovered answer ('ok0t2gq884') reaching the conversation. For the always-fail scenario (.ololo/tmp/cc-4xw7d311/) there are again three captured requests, calls=3, an empty stdout, and stderr containing 'ERROR: model failed (exit 1)', matching the spec's 'three failures in a row → ERROR: on stderr, non-zero exit, promptly'. The server-side probe results corroborate both paths: exit=1, seconds=1, calls=3, error_line=yes (20 pts) and exit=0, answer=ok0t2gq884, calls=3 (20 pts). These probes run the real code, so the retry capability demonstrably exists in the repo at this commit and no hardcoded-answer shortcut is plausible — three actual subprocess invocations are captured per scenario, and the strings in the fixtures are the scripted-model inputs, not probe ids matched in agent logic. Caveats noted but non-penalizing: (1) the pack shows only '(1 more changed file(s) not shown)' for the implementation file (presumably agent.py), so the literal retry-loop diff is not visible — but per evidence discipline, a truncated section cannot ground a penalty, and the behaviour provably arrives within the in-session commit chain (worst case it landed in an earlier in-session commit, which is legitimate building ahead); (2) agent telemetry reports zeros over a ~5-second window — treated as missing/incomplete client telemetry, which the rules say is never evidence of cheating on its own. No proof of pre-implementation, hardcoding, or faked outputs; the probes were honestly earned. Rating 0.

Genuine from-scratch implementation. The harness (agent.py) builds the first request itself: call_model() serializes {system, messages, tools} with JSON from its own state; build_system() always yields a non-empty system string; messages start empty and submit() appends exactly one verbatim user text block, so no assistant turn appears in the first request; the bash tool is hand-declared in TOOLS with name, description, and an input_schema for {command}. The model is invoked only via AGENT_MODEL_CMD through sh -c (a scripted model in test.sh), not the real agent CLI; remaining subprocess uses are the bash tool's own shell semantics, hooks, and git rev-parse. No delegation, laundering, or output-parsing of another tool anywhere on the execution path. Note: this rung's commit is content-identical to the previous rung's — the existing implementation already satisfied the contract.

Genuine from-scratch implementation. agent.py hand-rolls the entire headless protocol: call_model() speaks the pluggable-model JSONL contract over AGENT_MODEL_CMD (intended architecture; probes drive it with a scripted model.sh, not the real tool), run_loop() joins the final message's text blocks with newlines so several blocks print as several lines in order, and main() emits exactly print(answer) on stdout with errors on stderr. All subprocess use (model command per contract, bash tool, git branch, hooks, MCP) is auxiliary harness plumbing; nothing shells out to or wraps the real agent, and no output is parsed from another implementation. Matches the passing probe outputs (single answer; 'first|second' multi-block case).

No evidence of cheating; the near-empty harness diff is the explicitly legitimate 'general code absorbing later tasks' case. The work-window diff (ad660ce..9c647fd) touches only probe-run artifacts under .ololo/tmp/cc-v5cqknvu/, all of which 'did not exist before this task': (1) a scripted model.sh that emits two text_delta fragments ('S1v5cqknvu ', 'S2v5cqknvu') followed by the final message line whose text is 'S1v5cqknvu S2v5cqknvu' — this is the challenge's scripted-model fixture mechanism ('smoke test through a scripted model' per the AGENTS.md in req.1), not a hardcoded answer in the harness; (2) the captured stdout containing exactly 'S1v5cqknvu S2v5cqknvu' ONCE — the fragments were not printed, which is precisely the behaviour this task requires; (3) req.1, a genuine assembled request (system prompt from AGENTS.md, tool schemas, user turn 'stream me an answer rk2udk'); (4) a session file recording only the final assistant message, with last_input_tokens=100 matching the usage in model.sh's message event — internally consistent with a real model round trip. Critically, agent.py is not touched at all in this window: the headless behaviour (parse the stream, print only the message's text, ignore deltas) is the natural generalization of the earlier in-session commit 9ca302f7 'One prompt, one answer', and the task description itself says deltas 'in headless mode they change nothing' — making this effectively a regression probe that a correct earlier implementation already passes. No diff adds probe ids or fixture literals to the harness, so there is no hardcoding to point at; agent.py's history is not in the pack, so no claim of pre-session origin can be substantiated either. The prior 10-point result (exit=0, stdout=S1v5cqknvu S2v5cqknvu, printed once) confirms the capability passed live. Agent statistics are unavailable, which per the rules is not evidence of anything. Every signal present is corroborating; no concrete evidence supports any penalty.

No evidence of cheating; the probe artifacts in this task's commit demonstrate the required tool round trip working genuinely and generically.

What the window's diff (0609e931..bfb80845) contains: only probe-run artifacts under .ololo/tmp/cc-lkpx71sf/ — a scripted model (model.sh), the two captured requests (req.1, req.2), an empty stderr, a calls counter, and the agent session log. No agent source changes appear in this commit. That is not pre-implementation: the commit log shows the project being built across in-session feat commits (6022cb7a 'Set up the project', 9ca302f7 'One prompt, one answer', 0609e931 'The request is a conversation'), so the loop code that absorbs this task's case arrived in earlier in-session commits — explicitly legitimate 'building ahead' / general code absorbing later tasks, not smuggling from before the session. The first task being 'Set up the project' also argues against the loop existing in the session-start snapshot.

The artifacts themselves show real behavior, not faked or hardcoded output: req.2 contains the conversation grown exactly as specified — the assistant tool_use message preserved verbatim ({'type':'tool_use','id':'t1','name':'bash','input':{'command':'echo OUTlkpx71sf'}}) followed by a user message with {'type':'tool_result','tool_use_id':'t1','content':'OUTlkpx71sf\n','is_error':false}. Crucially, the tool_result content 'OUTlkpx71sf' is the actual output of executing the scripted command — model.sh only contains the command string, never its output, so the agent must have run it. The final answer 'donelkpx71sf' is relayed from model.sh's second scripted response, and the probe reports calls=2, linked=yes, output_seen=2, assistant_seen=1, exit=0, matching the captured files. All tokens (lkpx71sf, f91yx9, t1) are probe-randomized per run; nothing visible suggests the agent matches probe ids or values. The session log ends with the assistant text message, consistent with 'the model answers in text, and that text is what the agent prints'.

Agent activity stats were not reported by the CLI; per the rules, missing statistics are not evidence and the verdict rests on the git record alone, which shows a clean, verified probe run. No probe-specific special-casing is visible anywhere. Conservative scoring applies: return 0.

Genuine work, no cheating. The task's required behaviour — bash tool results carrying real stdout+stderr in write order, an appended 'exit code: ' line with is_error:true on failure, the loop continuing, and commands running in the -C working directory — is demonstrably working in artifacts created fresh inside this task's window. Both probe runs (cc-0xtau3y7 and cc-e2ram6pm) left new files that did not exist before this window (per the 'before' section), and their contents show real round trips: req.2 of cc-e2ram6pm contains tool_result content 'OUTe2ram6pm\nERRe2ram6pm\nexit code: 3' with is_error:true, and a second model call (calls=2) proving the loop continued; req.2 of cc-0xtau3y7 shows pwd output resolving to the ws working directory and ws/made-0xtau3y7.txt created via relative path. The probe grades (is_error=true, exit_line=yes, stdout_seen=1, stderr_seen=1, pwd_seen=1, file=in-workdir, 20+20 points) match these artifacts. These random per-probe tokens could only appear in the tool results because the agent's bash tool actually executed the scripted commands and captured their real output — the opposite of hardcoded answers. The diff adds no agent.py changes: the bash-tool implementation predates this window, but the commit log shows a legitimate in-session incremental build (session-start snapshot → 'Set up the project' → 'One prompt, one answer' → 'The request is a conversation' → 'A tool round trip' → 'Many turns'), so an empty implementation diff here is the allowed 'general code absorbing later tasks' / 'building ahead' case, not pre-implementation. Missing agent statistics are not evidence either way. No evidence of fabricated outputs or special-casing to probe values; the artifacts are consistent with a normal harness run of the player's agent.

Genuine pass, no cheating evidence. The work-window diff contains the probe's runtime artifacts under .ololo/tmp/cc-d4kosyrg/, and they demonstrate the required behavior actually occurred during this task's window: req.1 through req.6 show six requests where each carries the full ordered conversation — req.1 has only the initial user prompt 'do five things 4qhz8f'; req.2 adds assistant tool_use t1 plus its user tool_result; req.3 adds t2; req.4 adds t3; req.5 adds t4; req.6 contains all five assistant tool_use turns and all five results in order (sequence 1 1 2 2 3 3 4 4 5 5, results=5), with nothing summarised, dropped, or reordered. The session file ws/.agent/sessions/da827241d5b6.json matches exactly, ending with the final text 'doned4kosyrg'. The recorded probe result (exit=0, answer=doned4kosyrg, calls=6, sequence=1 1 2 2 3 3 4 4 5 5, results=5, 30 points) corroborates a live, passing run. The absence of an agent.py diff in this window is legitimate general-code reuse: the tool round-trip loop built in the prior in-session commit bfb80845 ('A tool round trip') naturally extends to many turns, which is exactly the 'general code absorbing later tasks' case — an empty or tiny code diff here is sound engineering, not pre-implementation. The hardcoded tokens (MKd4kosyrg-N, doned4kosyrg) appear only inside model.sh, the scripted fake model used by the test harness to drive the agent — that is the intended fixture design, not agent-side hardcoding; the agent code under test shows no probe-specific special-casing in the observed artifacts. All feat commits fall after the session-start snapshot cd69a1b3, so nothing predates the session. Agent activity stats are unavailable, which per the rules is not evidence of anything. No deception and no pre-existing behavior found; score 0.

No penalty. The work-window diff (6022cb7..9ca302f) contains no source changes at all — only new test-run artifacts under .ololo/tmp/: the scripted-model captures (model.sh, req.1 with the harness-generated markers 'Aggnvae07' and 'first/second L16ea1l30'), empty stderr files, stdout files containing exactly the final message text, and session JSONs recording the conversations. The functionality this task requires (-p mode printing exactly the final message text, blocks in order, logs on stderr) therefore must already have existed at the prior commit 6022cb7 ('Set up the project and declare the command'), which is an in-session commit that came after the session-start snapshot cd69a1b. That falls squarely under the legitimate 'general code absorbing later tasks / building ahead' case: the player built the harness during the earlier task's live window and this task's probes passed against that genuinely in-session implementation, leaving only run artifacts to commit. Supporting evidence the runs were real, not faked: the randomized markers in the canned model responses (L16ea1l30, Aggnvae07) are derived from harness-generated per-run directory ids and match the server-side probe answers ('exit=0 stdout=Aggnvae07|', 'exit=0 stdout=first L16ea1l30|second L16ea1l30|', 10 points each); req.1 captures contain the full assembled context (AGENTS.md instructions, tool schemas, randomized prompts) consistent with a real agent.py run; stderr files are empty as the spec demands. No committed source hardcodes any probe value — agent.py does not even appear in this diff. Agent statistics are unavailable, which per the rules is not evidence of anything. No evidence of pre-implementation from before the session, hardcoded answers, or faked outputs; probes passed with genuine capability. Score 0 (no penalty).

PRE-IMPLEMENTATION. The task asks the player to set up the project (AGENTS.md with agent:/test: lines) and a working headless entry point. The work-window diff (cd69a1b..6022cb7) adds ONLY probe-runtime artifacts under .ololo/tmp/cc-laoa05o1/: a scripted model stub (model.sh printing '{"...text":"hello Alaoa05o1"}'), a call counter, the recorded request (req.1), an empty stderr, and a saved session JSON. No hunk in any in-session commit adds AGENTS.md, agent.py, or test.sh — the actual deliverables. Yet the probes passed (40/40: 'python3 agent.py', 'exit=0 answer=hello Alaoa05o1 calls=1', 'sh test.sh'), so the full solution demonstrably existed and ran. The committed req.1 proves it: the agent's request imports an AGENTS.md ('# Handmade Claude Code — reference agent ... agent: python3 agent.py / test: sh test.sh') and declares 8 tools (bash, read, write, edit, glob, grep, skill, task) — a complete harness far beyond this task's deliberately 'bash only' scope, matching later parts of the challenge. Timing nails it down: the task commit contains probe artifacts (req.1, session JSON, calls counter), so it postdates the probe run; since the source files are absent from that commit, they were already tracked and unchanged in the session-start snapshot cd69a1b — i.e., the entire solution predates the session, and 'no in-session commit ever introduced it' while probes passed, which is the briefing's explicit definition of pre-implementation. Agent telemetry corroborates: a ~4-second window with 0 tokens, 0 tool calls, 0 messages, empty agents list. The diff adds nothing genuine toward the task; the points were earned by work brought in from outside the session. Penalty at the scale minimum (-40; scale caps at -40).

Genuine work. The task asked for the agent's first model request to carry a non-empty system, exactly one role:user message containing the prompt verbatim (no assistant turn), and a bash tool with name/description/input_schema; the probe confirms all checks passed (exit=0; system=present, user=yes, assistant=no, prompt_seen=1, bash=yes, schema=yes, description=yes).

Evidence in commit 0609e931: the window adds the scripted-model fixture and its run artifacts. model.sh implements exactly what the task describes — 'the scripted model keeps every request it receives' — via cat > "$D/req.$n" after incrementing a call counter, then replies with a canned "ok" end_turn message. The captured req.1 is real request output, not a hand-crafted stub: it contains a full non-empty system string (agent instructions, working directory, date, the AGENTS.md contents), a single user message whose text is the randomized prompt token 'prompt P5d5jf5o2' verbatim with no assistant turn, and a complete tools array led by bash with input_schema {type:object, properties:{command:{type:string}, timeout_ms:{type:integer}}, required:["command"]} plus a description. The session file ws/.agent/sessions/33df11d0c847.json shows the full pipeline ran (user 'prompt P5d5jf5o2' → assistant 'ok', last_input_tokens=100 matching the scripted usage), calls=1, empty stderr.

The diff contains no agent.py change, but that is the legitimate 'general code absorbing later tasks' case: the harness was built in earlier in-session commits (6022cb7 'Set up the project', 9ca302f7 'One prompt, one answer'), all after the session-start snapshot cd69a1b, so nothing predates the session. This rung's own work — the scripted model, the captured first request, and the run — was produced inside the window. The canned 'ok' response is the mock model's by-design fixture, not the agent faking outputs. Agent statistics are missing (CLI reported none), which per the rules is not evidence of cheating. No hardcoding of probe ids or values is visible. Score 0.

Genuine from-scratch implementation. The agent loop (conversation growth, tool_use/tool_result round-trips, model retry x3, max-turns, compaction, sessions) is hand-written in agent.py using only the Python stdlib — no agent SDK, no wrap of claude/codex/etc. The model is spawned as AGENT_MODEL_CMD via sh -c with request JSON on stdin and reply JSON on stdout, exactly the contract the task specifies; the scripted model the probes plug in is the model side, not the loop. The bash tool (subprocess.Popen(["sh", "-c", command])), hook execution, and git rev-parse are subprocess uses for tool/hook/meta purposes, not delegation to another listing or coding program. File tools (read/write/edit/glob/grep) use Python's own open/os/glob/re APIs. AGENTS.md carries the required agent: and test: lines, and test.sh smoke-tests the loop with a scripted model. No offending invocation found on the execution path.