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 skills/james-traina/compound-science/workflows-reviewnpx skills add James-Traina/compound-science --skill workflows-reviewgit clone --depth 1 https://github.com/James-Traina/compound-scienceWrote 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/james-traina/compound-science/workflows-review)<a href="https://agentmods.dev/skills/james-traina/compound-science/workflows-review"><img src="https://agentmods.dev/badge/skills/james-traina/compound-science/workflows-review.svg" alt="Measured on agentmods" 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 | $0.00021 | $0.02859 |
| Opus 5 | $0.00010 | $0.01430 |
| Sonnet 5 | $0.00004 | $0.00572 |
| Haiku 4.5 | $0.00002 | $0.00286 |
Grade A, and why
workflows:review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Command
Pipeline mode: This command operates fully autonomously. All decisions are made automatically.
Perform exhaustive econometric and methodological review using multi-agent parallel analysis. Domain-specific reviewers check estimation quality, identification strategy, numerical stability, and mathematical rigor.
Input
<review_target> #$ARGUMENTS </review_target>
Execution Workflow
Phase 1: Scope Detection
-
Eligibility Check
Before launching review agents, verify there is something to review. If no research artifacts are found (no estimation code, no proofs, no pipeline files, no data scripts, no output files), state "No research artifacts found to review" and stop. Do not launch agents against an empty target.
-
Determine Review Target
The review is artifact-centric: it reviews research files (estimation code, proofs, pipelines, data scripts), not git metadata. Determine the target in priority order:
- File paths or directories (e.g.,
estimation.py,src/models/,proof.tex) → review those artifacts directly - Plan reference (e.g.,
plan-3) → find the plan indocs/plans/, review files it references - PR number → fetch file list with
gh pr view --json files - Empty → auto-detect: scan the project for estimation code, proofs, pipeline files, and data scripts. If git shows recent changes, include those.
- File paths or directories (e.g.,
-
Classify Artifacts
Scan the target files and classify by type:
estimation_code: *.py with statsmodels/scipy.optimize/pyblp/linearmodels imports *.R with fixest/lfe/AER/gmm imports *.jl with Optim/NLsolve imports *.do with reg/ivregress/gmm commands simulation_code: Monte Carlo loops, DGP code, bias/RMSE computation proofs: *.tex with theorem/proof environments, *.md with derivation sections pipeline_files: Makefile, Snakefile, dvc.yaml, master.do data_code: data loading, cleaning, merge operations output_files: tables/*, figures/*, *.csv result files
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 334 lines · 21 tokens per session scan A ae53e110b8b0
workflows:review is a skill published in the GitHub repository James-Traina/compound-science (13 stars, last pushed 5mo ago), licensed MIT. It adds 21 tokens to every session and 2,859 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 2026-08-30.
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