Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AxGord/claude-workflow --skill domain-reidgit clone --depth 1 https://github.com/AxGord/claude-workflowWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/axgord/claude-workflow/domain-reid)<a href="https://agentmods.dev/skills/axgord/claude-workflow/domain-reid"><img src="https://agentmods.dev/badge/skills/axgord/claude-workflow/domain-reid/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/axgord/claude-workflow/domain-reid"><img src="https://agentmods.dev/badge/skills/axgord/claude-workflow/domain-reid.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00011 | $0.01174 |
| Opus 5 | $0.00005 | $0.00587 |
| Sonnet 5 | $0.00002 | $0.00235 |
| Haiku 4.5 | $0.00001 | $0.00117 |
Grade A, and why
domain-reid scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Re-ID — Verified Gotchas
BNNeck Trick (distance metric direction)
BNNeck = BatchNorm before classifier. The trick:
- Training: Euclidean distance in triplet loss (features BEFORE BNNeck)
- Inference: Cosine similarity (features AFTER BNNeck)
This direction is commonly reversed — getting it wrong silently degrades results.
Loss Recipe
Typical: loss = CE_label_smoothing + triplet_hard_mining + 0.0005 * center_loss
The center loss weight (lambda=0.0005) is critical — too high destabilizes training.
CLIP-ReID
Two-stage approach (NOT generic CLIP fine-tuning):
- Learn text tokens per identity — BOTH encoders frozen; only the id-specific text tokens train
- Fine-tune visual encoder with learned text supervision (text side stays fixed)
MSMT17 ~86.7% mAP is the SIE+OLP + re-ranking configuration — plain ViT-B CLIP-ReID lands in the low-to-mid 70s mAP. Don't quote 86.7 as the vanilla-model number.
SOTA Performance Reference
| Method | Market-1501 R1/mAP | MSMT17 R1/mAP |
|---|---|---|
| CLIP-ReID (ViT-B, SIE+OLP, +re-rank) | 96.4 / 93.3 | 91.1 / 86.7 |
| TransReID (ViT-B) | 95.2 / 89.5 | 86.2 / 69.4 |
| BoT (ResNet-50) | 94.5 / 85.9 | 77.5 / 47.5 |
The CLIP-ReID row already includes k-reciprocal re-ranking — do NOT add the re-ranking boost from the section below on top of these numbers (double-count).
Key Datasets
| Dataset | IDs | Images | Notes |
|---|---|---|---|
| Market-1501 | 1,501 | 32,668 | Most widely used |
| MSMT17 | 4,101 | 126,441 | Largest, preferred for SOTA |
| CUHK03 | 1,467 | 14,096 | Use "new protocol" (767/700 split) |
| MARS | 1,261 | 1,191,003 | Video-based (tracklets) |
Note: DukeMTMC-reID was retracted due to privacy concerns — avoid citing it.
k-Reciprocal Re-Ranking
Parameters: k1=20, k2=6, lambda=0.3. Boosts mAP by 5-10%. Use for offline, skip for real-time.
torchreid install — from git, NOT PyPI
pip install torchreid fetches a stale 0.2.5 (2019) with a different API — import torchreid
often outright fails on a modern torch/numpy. The real KaiyangZhou/deep-person-reid is 1.4.x, git-only. Its setup.py imports numpy/Cython
at build time, so a plain pip install git+... fails under PEP-517 build isolation
(error: getting requirements to build wheel → ModuleNotFoundError: No module named 'torchreid'
/ numpy). Two working installs (both need numpy+Cython in the env):
pip install --no-build-isolation git+https://github.com/KaiyangZhou/deep-person-reid.git, or the
official git clone … && cd deep-person-reid && pip install -r requirements.txt && python setup.py develop
(its requirements.txt does NOT pin torch, so a preinstalled CUDA torch survives).
So a "package is installed but import raises" state ≠ missing — detect it with
importlib.util.find_spec(m) is not None + a real import attempt, and surface the actual exception
(don't report "missing" and re-suggest the PyPI name that caused it).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 86 lines · 11 tokens per session scan A 1867e69a2d86
domain-reid is a skill published in the GitHub repository AxGord/claude-workflow (5 stars, last pushed 1mo ago), licensed MIT. It adds 11 tokens to every session and 1,174 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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