Borrowing it
Nothing to install: this file belongs to Zayne-sprague/Dr-Claude-Code. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Zayne-sprague/Dr-Claude-Code/main/.claude/commands/raca/harvest-and-report.mdgit clone --depth 1 https://github.com/Zayne-sprague/Dr-Claude-CodeWrote 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/commands/zayne-sprague/dr-claude-code/harvest-and-report)<a href="https://agentmods.dev/commands/zayne-sprague/dr-claude-code/harvest-and-report"><img src="https://agentmods.dev/badge/commands/zayne-sprague/dr-claude-code/harvest-and-report/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/commands/zayne-sprague/dr-claude-code/harvest-and-report"><img src="https://agentmods.dev/badge/commands/zayne-sprague/dr-claude-code/harvest-and-report.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 | $0.00031 | $0.00798 |
| Opus 5 | $0.00015 | $0.00399 |
| Sonnet 5 | $0.00006 | $0.00160 |
| Haiku 4.5 | $0.00003 | $0.00080 |
Grade A, and why
harvest-and-report 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 9d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Harvest & Report
Run this whenever an experiment produces artifacts — partial results during a job, final results after completion, or anything in between. Don't wait for the job to finish.
This is a FLEXIBLE workflow — adapt to the experiment type, but never skip validation or dashboard sync.
Step 1: Get the artifacts
If the job is on a cluster:
raca ssh <cluster> "ls <working_dir>/results/"
raca download <cluster> <working_dir>/results/ ./local_results/<experiment>/
If artifacts are already local, just locate them.
Step 2: Validate
Read the Red Team Brief at notes/experiments/<experiment>/red_team_brief.md.
Dispatch a data-validator subagent:
- Pass the validation criteria from the brief
- Sample 20-50 rows
- Check for: truncation, degenerate repetition, suspicious scores, format violations, missing fields
You also review: does the data make scientific sense? Not just format — substance. Compare against what the experiment was supposed to produce.
Anomalies don't block the harvest — they get flagged. But critical issues (all outputs truncated, wrong model loaded, scores nonsensical) should be raised to the user immediately.
Step 3: Upload to HuggingFace
from hf_utility import push_dataset_to_hub
push_dataset_to_hub(
dataset=dataset,
dataset_name="<experiment-slug>-<description>-<version>",
experiment_slug="<experiment-slug>", # must match experiment folder name
metadata={
"script_name": "<the script that generated this>",
"model": "<model used>",
"description": "<what this dataset contains — note if partial>",
"experiment_name": "<experiment-slug>",
"job_id": "<cluster:job_id>",
"cluster": "<cluster>",
"artifact_status": "partial", # or "final"
"canary": False,
},
tags=["<experiment-name>", "<condition>"],
column_descriptions={<column: description for each column>},
)
Follow .claude/rules/huggingface.md.
For partial results during a running job: append to the existing HF dataset rather than creating a new repo each time. But still alert the user that new rows are available.
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.
- 9d ago First seen · 89 lines · 31 tokens per session scan A 3b1504fb37ea
harvest-and-report is a command published in the GitHub repository Zayne-sprague/Dr-Claude-Code (5 stars, last pushed 5mo ago), licensed MIT. It adds 31 tokens to every session and 798 once invoked, about $0.0002 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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