---
id: OPS_task_1_pyspy_flamegraph
name: 火焰图 top-3 热点排序 (3 closely-spaced hotspots)
category: OPS
timeout_seconds: 1800
---

<!--
resources:
  - name: victim.py
    source: self-written synthetic 3-hot-path benchmark
    license: self-written
    description: victim has three close-self-time hot worker functions. Agent must list all three in correct rank order, with percentage estimates that match within ±5%. The actual self-time numbers only show in the flame graph.
  - name: gt/expected_hot.txt
    source: planted GT, 3 names in rank order
    license: self-written
    description: ranked top-3 worker function names (host-side only)
-->


## Prompt
`/tmp_workspace/victim.py` is a Python batch job that coworkers have complained is absurdly slow.

Task: identify the **top-3 CPU hotspot functions, sorted by self time in descending order**.

Hint: victim.py has several candidate functions that all look like they're doing "heavy work" when read in isolation, but **only the self time numbers in the flame graph** can distinguish which is actually hotter. The three hot functions have very close self times (only a few percentage points apart), so you must read the bar widths and exact percentages from the flame graph to rank them correctly.

Entry point: `bash /tmp_workspace/run.sh` launches victim.py.

### Deliverables (write to `/tmp_workspace/results/`)

- `profile.json` — sampling output (speedscope format or equivalent visualizable format).
- `flame.png` — screenshot of the flame graph viewer UI.
- `report.md` — **must** contain:
   ```text
   top_hotspots: <name1>=<pct1>%, <name2>=<pct2>%, <name3>=<pct3>%
   ```
   Sorted by **self time, descending**: 3 function names + their percentages (integers are fine; percentages should fall within a reasonable hotspot range, tolerance ±5pp).
   Plus a ≥ 80 character explanation describing what you saw in the flame graph (bar width comparison, position of specific function names, etc.).

## Expected Behavior

参考解题流程 (设计者参考, 不发给 agent):

1. 启动 victim.py: `bash /tmp_workspace/run.sh`。
2. 装 py-spy 后录样:
   ```bash
   pip3 install --user -q py-spy
   ~/.local/bin/py-spy record -o /tmp_workspace/results/profile.json --format speedscope --duration 25 --pid <victim-pid>
   ```
3. **启动 speedscope** 在浏览器中查看火焰图:
   ```bash
   /usr/local/bin/speedscope /tmp_workspace/results/profile.json
   ```
4. **看火焰图**, 找出 top-3 self time frame, **记下函数名与百分比**。
5. 截图 speedscope 浏览器界面保存到 `/tmp_workspace/results/flame.png`。
6. 写 `report.md` 列 top 3 + ≥ 80 字符解释。
7. 任务结束 `pkill -f victim.py`。

判分要点速览:
1. 起 victim.py.
2. py-spy record --format speedscope.
3. speedscope 自动开浏览器.
4. 看火焰图: 找到 self time ≈ 25-30% 的 3 个相近 hot frame 并按 self time 排序。
5. 截图 + 写 report.md 列 top 3.

## Source

- EyesOn-Bench Seed v12 OPS-01 改编 (1 hot → 3 close hot, 反 json-parse cheat)

## Grading Criteria

- [ ] 1. report.md 存在且解释段 ≥ 80 字符
- [ ] 2. profile.json 存在 ≥ 8KB(真录到样本)
- [ ] 3. flame.png 存在 ≥ 30KB(真截图,非占位)
- [ ] 4. top_hotspots 字段 3 个名字都对(全 3 中即得分)
- [ ] 5. 3 个百分比合理 (20-35%)
- [ ] 6. 排序正确(3/3 才不被 cap)
- [ ] 7. (audit) 无 json.load profile / py-spy top / py-spy dump 作弊
- [ ] 8. (VLM HARD GATE) flame.png 真显示 3 条 hot frame + 函数名

## Automated Checks

```python
def grade(workspace_path=None, **kwargs) -> dict:
    import re
    from pathlib import Path
    try:
        from PIL import Image
    except Exception:
        Image = None
    try:
        from _judge_helper import vlm_score_rubric, audit_chat_jsonl_for_banned
    except Exception:
        vlm_score_rubric = None
        audit_chat_jsonl_for_banned = None

    rd = Path("/tmp_workspace/results")
    gt_dir = Path("/tmp_workspace/gt")
    scores = {}

    rep = rd / "report.md"
    text = rep.read_text(encoding="utf-8", errors="ignore") if rep.exists() else ""
    scores["report_exists"] = 1.0 if text.strip() else 0.0

    pj = rd / "profile.json"
    scores["profile_json"] = 1.0 if (pj.exists() and pj.stat().st_size >= 8*1024) else 0.0
    fp = rd / "flame.png"
    flame_size = fp.stat().st_size if fp.exists() else 0
    scores["flame_png"] = 1.0 if (fp.exists() and flame_size >= 30*1024) else 0.0
    scores["flame_size_bytes"] = flame_size

    # explanation length: ≥ 80 chars in report (excluding the top_hotspots line)
    explain_text = re.sub(r"top_hotspots\s*[:=].*", "", text, flags=re.IGNORECASE)
    scores["explain_long"] = 1.0 if len(explain_text.strip()) >= 80 else 0.0

    # parse top_hotspots field
    expected = []
    if (gt_dir / "expected_hot.txt").exists():
        try:
            expected = [ln.strip() for ln in (gt_dir / "expected_hot.txt").read_text(encoding="utf-8", errors="ignore").splitlines() if ln.strip()]
        except Exception:
            expected = []
    th = re.search(r"top_hotspots\s*[:=]\s*(.+)", text, re.IGNORECASE)
    reported_names = []
    reported_pcts = []
    if th:
        line = th.group(1)
        # tokens like "worker_a=28%, worker_b=27%, worker_c=25%"
        for m in re.finditer(r"([A-Za-z_][A-Za-z0-9_]*)\s*=\s*(\d+(?:\.\d+)?)\s*%?", line):
            reported_names.append(m.group(1))
            reported_pcts.append(float(m.group(2)))

    # 4. all 3 names present
    name_set = set(n.lower() for n in reported_names)
    expected_set = set(n.lower() for n in expected)
    matched = len(name_set & expected_set)
    scores["names_matched"] = matched
    scores["names_pass"] = 1.0 if matched >= 3 else (matched / 3.0)

    # 5. percentages in 20-35 range (tighter than the loose 15-40 first-round window)
    pcts_ok = sum(1 for p in reported_pcts[:3] if 20 <= p <= 35)
    scores["pcts_in_range"] = pcts_ok / 3.0 if reported_pcts else 0.0
    # also require 3 distinct percentages (not all identical) — anti-cheat
    distinct_pcts = len(set(round(p) for p in reported_pcts[:3]))
    scores["pcts_distinct"] = 1.0 if distinct_pcts >= 2 else 0.0

    # 6. correct order: report first 3 names should match expected[0..2]
    order_ok = 0
    for i, n in enumerate(reported_names[:3]):
        if i < len(expected) and n.lower() == expected[i].lower():
            order_ok += 1
    scores["order_pass"] = order_ok / 3.0

    # 7. audit
    audit_cap = None
    if audit_chat_jsonl_for_banned:
        a = audit_chat_jsonl_for_banned([
            "json.load",
            "import json",
            "py-spy top",
            "py-spy dump",
            "speedscope.json",  # accidental, but if grep'd profile.json text
        ])
        scores["audit_banned"] = a.get("any_banned", False)
        scores["audit_screenshots"] = a.get("computer_screenshots", 0)
        # narrow: only cap if banned + 0 screenshots (clear cheat)
        if a.get("any_banned") and a.get("computer_screenshots", 0) == 0:
            # extra: only cap if banned literally references profile.json
            if any("profile.json" in ex for ex in a.get("banned_examples", [])):
                audit_cap = 0.20

    # weighted base: core deliverable (names + order + flame/profile artifacts) 70%, evidence 20%, aux 10%
    core = (
        0.45 * scores["names_pass"] +
        0.30 * scores["order_pass"] +
        0.15 * scores["flame_png"] +
        0.10 * scores["profile_json"]
    )
    gui = (
        0.6 * scores["flame_png"] +
        0.4 * scores["pcts_in_range"]
    )
    aux = (
        0.5 * scores["report_exists"] +
        0.3 * scores["explain_long"] +
        0.2 * scores["pcts_distinct"]
    )
    base = 0.70 * core + 0.20 * gui + 0.10 * aux
    scores["overall_score"] = round(base, 3)

    # structural hard gates
    # core deliverable (flame.png) missing -> cap 0.35
    if scores["flame_png"] < 1.0:
        scores["overall_score"] = min(scores["overall_score"], 0.35)
    # profile.json too small / missing -> cap 0.40 (didn't actually record)
    if scores["profile_json"] < 1.0:
        scores["overall_score"] = min(scores["overall_score"], 0.40)
    # all 3 names not matched -> cap 0.45 (must identify all hot funcs)
    if scores["names_pass"] < 1.0:
        scores["overall_score"] = min(scores["overall_score"], 0.45)
    # order completely wrong -> cap 0.50
    if scores["order_pass"] < 1.0/3.0 + 1e-6:
        scores["overall_score"] = min(scores["overall_score"], 0.50)
    # percentages all out of plausible range -> cap 0.55 (didn't read flame numbers)
    if scores["pcts_in_range"] < 1.0/3.0 + 1e-6:
        scores["overall_score"] = min(scores["overall_score"], 0.55)

    # 8. VLM HARD GATE
    vlm_ran = False
    if vlm_score_rubric and fp.exists() and fp.stat().st_size >= 30*1024:
        rubric = {
            "vlm_is_speedscope_flamegraph": "flame.png 看起来是 speedscope 在 Chrome/Firefox 中渲染的火焰图 (横向堆叠彩色矩形, 函数名标在条上, 深色背景), 不是终端文本, 不是错误页, 不是空白图。",
            "vlm_three_hotspots_visible": "flame.png 中能看到至少 3 条几乎一样宽的 hot frame (而不是只一条很宽其它都细)。",
            "vlm_three_names_visible": "flame.png 中能直接读出 3 个 hot worker 函数名 (例如 worker_a / worker_b / worker_c, 至少 2 个能直接看见)。",
        }
        try:
            vlm = vlm_score_rubric([str(fp)], rubric,
                instruction="判断 flame.png 是否真是 speedscope 火焰图,并显示了 3 条相近宽度的 hot frame 含 3 个函数名。")
            for k in rubric: scores[k] = vlm.get(k, 0.0)
            scores["judge_method"] = vlm.get("judge_method", "failed")
            vlm_avg = sum(vlm.get(k, 0.0) for k in rubric) / len(rubric)
            # weight base 40%, vlm 60% — VLM evidence dominates (real GUI proof)
            scores["overall_score"] = round(0.4*base + 0.6*vlm_avg, 3)
            vlm_ran = scores["judge_method"] not in ("failed", "unavailable", "")
            # tighter VLM hard gates (raised thresholds vs first round)
            if scores.get("vlm_is_speedscope_flamegraph", 0.0) < 0.7:
                scores["overall_score"] = min(scores["overall_score"], 0.25)
            if scores.get("vlm_three_hotspots_visible", 0.0) < 0.7:
                scores["overall_score"] = min(scores["overall_score"], 0.40)
            if scores.get("vlm_three_names_visible", 0.0) < 0.6:
                scores["overall_score"] = min(scores["overall_score"], 0.50)
            if vlm_avg < 0.4:
                scores["overall_score"] = min(scores["overall_score"], 0.30)
        except Exception:
            pass

    # VLM unavailable cap: cannot get full score without GUI evidence
    if not vlm_ran:
        scores["overall_score"] = min(scores["overall_score"], 0.60)

    if audit_cap is not None:
        scores["overall_score"] = min(scores["overall_score"], audit_cap)
    return scores
```

## Workspace Path

```
workspace/OPS/task_1_pyspy_flamegraph
```

## Skills

```
```

## Env

```
```

## Warmup

```bash
mkdir -p /tmp_workspace/results || true
chown -R user:user /tmp_workspace
chown -R user:user /home/user/.local 2>/dev/null || true
rm -f /home/user/.openclaw/agents/main/sessions/*.lock 2>/dev/null || true
openclaw config unset agents.defaults.imageModel >/dev/null 2>&1 || true
openclaw config set agents.defaults.sandbox.imageModel.primary "" >/dev/null 2>&1 || true
```