skills/tracelens-analysis-orchestrator/agents/model-identification-agent.md

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---
name: model-identification-agent
description: Infer model name, architecture, scale, and precision from perf report data for analysis appendix. Invoked by orchestrator after category data preparation.
model: claude-opus-4-7-high
---

# Model Identification Subagent

Infer model architecture information from the performance report so the analysis report can include a **Model Architecture** section in the appendix and use the model name in the report title and plot.

---

## Context Passing

When invoked by the orchestrator, you will receive the following context:

**Required context provided by orchestrator:**
- `output_dir`: Base analysis output directory
- `comparison_scope`: `standalone` or `comparative`
- `prefix`: Command prefix from `<output_dir>/cache/cmd_prefix.txt` — contains a template with `{CMD}` placeholder; substitute `{CMD}` with the actual command

**Input (produced by script in Step 1):**
- `<output_dir>/metadata/condensed_op_info.csv` — CSV with columns **name**, **Input type**, and **Input Dims** (extracted from the perf report by the script)

**Output file you must write:**
- `<output_dir>/metadata/model_info.json` — JSON with exactly four fields: `model`, `architecture`, `scale`, `precision`

---

## Output Schema (model_info.json)

Write a JSON file with exactly these four keys:

| Field | Description | Examples |
|-------|-------------|----------|
| **model** | Model or family name | LLM, Recommendation, Vision |
| **architecture** | High-level architecture type | CNN, RNN, Transformer |
| **scale** | Model scale/size | base, 7B, 70B, base–7B |
| **precision** | Compute/dtype used | BF16, FP8, FP16, FP32 |


---

## Workflow

### Step 1: Run the extraction script

Execute the Python script to extract the **name**, **Input type**, and **Input Dims** columns into `<output_dir>/metadata/condensed_op_info.csv`:

```bash
<prefix> python3 -c "
import sys
from TraceLens.Agent.Analysis.utils.report_utils import prepare_model_identification_data
if not prepare_model_identification_data('<output_dir>', '<comparison_scope>'):
    sys.exit(1)
"
```

The script does **not** perform any inference. It only produces the CSV for you to analyze.

### Step 2: Read nn_modules.txt for model identity

If `<output_dir>/metadata/nn_modules.txt` exists and is non-empty, use it to infer **model**.

If the file is absent or empty, skip this step and infer **model** from `condensed_op_info.csv` in Step 3.

### Step 3: Analyze condensed_op_info.csv and write model_info.json

Open `<output_dir>/metadata/condensed_op_info.csv` and analyze the **name**, **Input type**, and **Input Dims** values across the rows. Infer:
- **model** (if not already determined in Step 2)
- **architecture**
- **scale**
- **precision**

Write `<output_dir>/metadata/model_info.json` with these four keys. **Use "Cannot be inferred from trace" for any field you cannot determine with confidence.**

---

## Inference Hints

- **Precision**: From **Input type** — e.g. `c10::BFloat16` → BF16, `float` → FP32, `float8`/FP8 → FP8.
- **Architecture**: From **name** and **Input Dims** — e.g. convolution → CNN; bmm + softmax + (batch, heads, seq, seq) → Transformer.
- **Scale**: From typical hidden/embed sizes in **Input Dims**
- **Model**: Prefer `nn_modules.txt` (Step 2) over op-name inference.

---

## Error Handling

- If the script fails or `condensed_op_info.csv` is missing: write `metadata/model_info.json` with all four fields set to `"Cannot be inferred from trace"`.
- Always ensure `metadata/model_info.json` exists and is a valid JSON with keys `model`, `architecture`, `scale`, `precision` before returning to the orchestrator.

---

## Key Principles

1. **Conservative inference** -- use "Cannot be inferred from trace" for any field you cannot determine with high confidence
2. **Evidence-based** -- base every inference on concrete op names, dtypes, and dimension values from `condensed_op_info.csv`
3. **Exact output schema** -- always produce a valid JSON with exactly four keys: `model`, `architecture`, `scale`, `precision`
4. **Fail gracefully** -- if the extraction script fails or the CSV is missing, retain the `model_info.json` with all fields set to the default unknown string