Borrowing it
Nothing to install: this file belongs to ltjed/freephdlabor. 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/ltjed/freephdlabor/main/.claude/commands/refine_agent_prompt.mdgit clone --depth 1 https://github.com/ltjed/freephdlaborWrote 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/ltjed/freephdlabor/refine_agent_prompt)<a href="https://agentmods.dev/commands/ltjed/freephdlabor/refine_agent_prompt"><img src="https://agentmods.dev/badge/commands/ltjed/freephdlabor/refine_agent_prompt/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/ltjed/freephdlabor/refine_agent_prompt"><img src="https://agentmods.dev/badge/commands/ltjed/freephdlabor/refine_agent_prompt.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 | $0.00000 | $0.00557 |
| Opus 5.5 | $0.00000 | $0.00223 |
| Sonnet 5.5 | $0.00000 | $0.00111 |
| Haiku 4.5 | $0.00000 | $0.00056 |
Grade A, and why
refine_agent_prompt 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 2mo 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are tasked with optimizing the multi-agent workflow by iteratively improving agent instructions to produce high-quality papers publishable at top AI conferences.
Arguments provided: $ARGUMENTS
The arguments may or may not contain the following:
- agent_name (optional): The specific agent to optimize (manager, ideation, experimentation, or writeup). If not specified, analyze all agents.
- results_dir (optional): A specific results directory to analyze (e.g., "20250715_152703_adaptive_lr_cnn"). If not specified, run a new experiment first.
Your workflow:
Step 1: Determine Starting Point
- If a results_dir is provided, skip to Step 2 with that directory
- Otherwise, run the multi-agent system:
eval "$(conda shell.bash hook)" && conda activate freephdlabor && export $(cat .env | grep -v '^#' | xargs) && python launch_multiagent.py --model gpt-5.5 2>&1 | tee logs/output_$(date +%Y%m%d_%H%M%S).log
Step 2: Analyze Results
- Read the JSONL file at
results/{results_dir}/agent_llm_calls.jsonl - Focus on the specified agent_name if provided, otherwise analyze all agents
- Identify issues such as:
- Agent coordination problems
- Task execution efficiency issues
- Research quality and depth problems
- Experimental design and execution issues
- Inter-agent communication breakdowns
Step 3: Improve Instructions
Based on identified issues, modify the corresponding instruction files:
- For specific agent:
freephdlabor/prompts/{agent_name}_instructions.py - For all agents: Review and update all instruction files as needed
Important constraints:
- Instructions are system prompts - keep them concise and actionable
- Focus on improving agent behavior and coordination
- Prioritize research quality and paper publishability
- Do NOT modify code structure, only instruction content
Step 4: Commit Changes
Create a git commit with a clear message summarizing:
- Which agent(s) were optimized
- Key issues identified
- Main improvements made
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.
- 2mo ago First seen · 55 lines · 0 tokens per session scan A 11a9a51fc19f
refine_agent_prompt is a command published in the GitHub repository ltjed/freephdlabor (718 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 557 tokens. 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 2026-08-30.
refine_agent_prompt compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| refine_agent_promptltjed/freephdlabor | Command | 718 | 2mo ago | MIT | A | 0 | Claude Code |
| orchestrateruvnet/ruflo | Command | 81k | yesterday | MIT | A | 0 | Claude Code |
| fest-showObedience-Corp/festival | Command | 123 | 2d ago | Apache-2.0 | A | 17 | Claude Code |
| performance-bottlenecksruvnet/ruflo | Command | 81k | yesterday | MIT | A | 0 | Claude Code |
| reviewdork-labs/dorkos | Command | 11 | yesterday | MIT | A | 21 | Claude Code |
| typesdork-labs/dorkos | Command | 11 | yesterday | MIT | A | 13 | Claude Code |
| tidyOdinMB/ops-workflow | Command | 6 | 10d ago | MIT | A | 33 | Claude Code |