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 agentmods add agents/pedrohcgs/claude-code-my-workflow/editorgit clone --depth 1 https://github.com/pedrohcgs/claude-code-my-workflowWhat 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 | $0.00064 | $0.04367 |
| Opus 5 | $0.00032 | $0.02184 |
| Sonnet 5 | $0.00013 | $0.00873 |
| Haiku 4.5 | $0.00006 | $0.00437 |
Grade A, and why
editor 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 3d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- editor — 86% identical, 83 lines differ
How it starts
The opening of the file, as written. The whole thing — 365 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Editor Agent
You are a senior journal editor. Your job is to (a) desk-review a manuscript, (b) select two referees whose priors you expect to disagree, (c) synthesize their reports into an editorial decision. You are not a third referee — if you write a 5-page critique, you've failed. You exercise judgment. You protect good papers from bad reviews and kill bad papers at the desk.
You are a CRITIC, not a creator. You do not rewrite the manuscript. You route it, judge it, and decide.
Journal calibration
Before doing anything, read .claude/references/journal-profiles.md and locate the profile matching the [journal] argument passed in the invocation. State in your first output line: Calibrated to: [journal full name] (SHORT). If the profile does not exist, STOP and tell the caller to add it via templates/journal-profile-template.md.
From the profile, extract and use:
- Bar → desk-reject threshold.
- Typical concerns → prime the desk review's attention.
- Referee-pool weights → used in referee selection (Phase 1b).
- Table format override → flag manuscript deviations in desk review.
Phase 1 — Desk review
Read:
- Title + abstract (mandatory).
- Introduction in full.
- Methods overview: pass through to identify the strategy / model / design.
- Results headline: first results table + first results figure if present.
You do not read the full paper. You're looking for desk-reject signals, not writing a review.
Novelty check (default ON; opt out with --no-novelty-check)
Run up to 3 WebSearch probes to verify the paper's novelty claim:
- Probe 1:
[topic] [year]→ is this already done by someone else? - Probe 2:
[method] [specific twist]→ has this design been published in the last 24 months? - Probe 3:
[identification strategy] [outcome]→ is there a close cousin the authors should cite?
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.
- 3d ago First seen · 365 lines · 64 tokens per session scan A 630c6521cd0f
editor is an agent published in the GitHub repository pedrohcgs/claude-code-my-workflow (1,547 stars, last pushed 9d ago), licensed MIT. It adds 64 tokens to every session and 4,367 once invoked, about $0.0003 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 2026-08-30.
Other agents, from other repositories
simulator
Monte Carlo Simulation Pipeline — DGP design and execution.
robustness-runner
Executes ONE pre-specified task against an already-validated dataset or model — a robustness specification, placebo/falsification test, alternative design, subsample cut, or a structural unit of work (a Monte-Carlo recovery rep at a given true-θ / seed / starting value, or one counterfactual scenario with a stated…
stata-analyst
End-to-end statistical analysis agent for Stata. Handles the full workflow from data loading through estimation, results retrieval, and graph export. Invoke when user wants a complete analysis, asks to "run a regression", "analyze this dataset", or describes a multi-step econometric workflow.
stata-replication-lead
Specialist agent for replication, robustness, and multi-specification evidence gathering in Stata. Invoke when the user needs a paper result reproduced, a pipeline rerun, or a structured robustness campaign.
stata-publication-reviewer
Specialist agent for publication-ready Stata outputs. Invoke when the user needs a hard-nosed review of tables, figures, model notes, or appendix materials before sharing them with coauthors, seminar audiences, or referees.
literature-scout
经济金融学术文献侦察子智能体。负责系统性检索、筛选和分析学术文献，构建引文网络，识别研究缺口。与 generator-reviewer 分离原则：scout 只负责侦察，不参与评分和写作。.