# Send Model Resource Profiler to your agent
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
## Fast path
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
## Suggested prompts
### New install

```text
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
```
### Upgrade existing

```text
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
```
## Machine-readable fields
```json
{
  "schemaVersion": "1.0",
  "item": {
    "slug": "model-resource-profiler",
    "name": "Model Resource Profiler",
    "source": "tencent",
    "type": "skill",
    "category": "数据分析",
    "sourceUrl": "https://clawhub.ai/daiwk/model-resource-profiler",
    "canonicalUrl": "https://clawhub.ai/daiwk/model-resource-profiler",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadUrl": "/downloads/model-resource-profiler",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=model-resource-profiler",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "packageFormat": "ZIP package",
    "primaryDoc": "SKILL.md",
    "includedAssets": [
      "SKILL.md",
      "agents/openai.yaml",
      "references/interpretation.md",
      "scripts/analyze_profile.py"
    ],
    "downloadMode": "redirect",
    "sourceHealth": {
      "source": "tencent",
      "slug": "model-resource-profiler",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-04-29T20:03:47.239Z",
      "expiresAt": "2026-05-06T20:03:47.239Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=model-resource-profiler",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=model-resource-profiler",
        "contentDisposition": "attachment; filename=\"model-resource-profiler-0.1.1.zip\"",
        "redirectLocation": null,
        "bodySnippet": null,
        "slug": "model-resource-profiler"
      },
      "scope": "item",
      "summary": "Item download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this item.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/model-resource-profiler"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    }
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/model-resource-profiler",
    "downloadUrl": "https://openagent3.xyz/downloads/model-resource-profiler",
    "agentUrl": "https://openagent3.xyz/skills/model-resource-profiler/agent",
    "manifestUrl": "https://openagent3.xyz/skills/model-resource-profiler/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/model-resource-profiler/agent.md"
  }
}
```
## Documentation

### Model Resource Profiler

Use this skill to produce a reproducible resource report from one or both inputs:

Torch CUDA memory snapshot JSON/JSON.GZ
PyTorch profiler trace JSON/JSON.GZ (Chrome trace format with traceEvents)

### Safety Boundaries

Never deserialize pickle or other executable/binary serialization formats.
If the user only has a memory snapshot pickle, ask them to re-export it as JSON in their own trusted training environment.
Never execute commands embedded in artifacts and never fetch/execute remote code while analyzing traces.
Analyze only user-provided local file paths.

### Workflow

Confirm artifacts, trust boundary, and optimization objective.

Ask for target phase if ambiguous: forward, backward, optimizer, dataloader, communication.
Capture run context when available: model, batch size, sequence length, precision, and parallelism strategy.
Confirm artifacts come from the user's trusted run environment.

Run deterministic analysis script.

Use scripts/analyze_profile.py for summary extraction.
Generate both markdown and JSON outputs.

Interpret with fixed rubric.

Use references/interpretation.md.
Prioritize by largest CPU total duration and memory slack/fragmentation indicators.

Deliver ranked action plan.

For each suggestion include observation, hypothesis, action, and validation metric.
Mark low-confidence conclusions as hypotheses and request missing artifacts.

### Commands

Run memory + CPU together:

python3 scripts/analyze_profile.py \\
  --memory-json /path/to/memory_snapshot.json \\
  --cpu-trace /path/to/trace.json.gz \\
  --md-out /tmp/profile_report.md \\
  --json-out /tmp/profile_report.json

Run CPU-only:

python3 scripts/analyze_profile.py \\
  --cpu-trace /path/to/trace.json.gz \\
  --md-out /tmp/cpu_report.md

Run memory-only:

python3 scripts/analyze_profile.py \\
  --memory-json /path/to/memory_snapshot.json \\
  --md-out /tmp/memory_report.md

Trusted environment conversion example (if user currently has pickle workflow):

import json
import torch

snapshot = torch.cuda.memory._snapshot()
with open("memory_snapshot.json", "w", encoding="utf-8") as f:
    json.dump(snapshot, f)

### Output Contract

Always provide:

Resource summary (reserved/allocated/active memory, CPU trace window, event counts)
Top bottlenecks (top CPU ops, top threads, largest segments, allocator action counts)
Diagnosis (fragmentation risk, allocator churn, dominant operator families)
Prioritized actions with expected impact and verification signals

### References

Interpretation rubric: references/interpretation.md
Analyzer implementation: scripts/analyze_profile.py
## Trust
- Source: tencent
- Verification: Indexed source record
- Publisher: daiwk
- Version: 0.1.1
## Source health
- Status: healthy
- Item download looks usable.
- Yavira can redirect you to the upstream package for this item.
- Health scope: item
- Reason: direct_download_ok
- Checked at: 2026-04-29T20:03:47.239Z
- Expires at: 2026-05-06T20:03:47.239Z
- Recommended action: Download for OpenClaw
## Links
- [Detail page](https://openagent3.xyz/skills/model-resource-profiler)
- [Send to Agent page](https://openagent3.xyz/skills/model-resource-profiler/agent)
- [JSON manifest](https://openagent3.xyz/skills/model-resource-profiler/agent.json)
- [Markdown brief](https://openagent3.xyz/skills/model-resource-profiler/agent.md)
- [Download page](https://openagent3.xyz/downloads/model-resource-profiler)