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-compoundnpx skills add James-Traina/compound-science --skill workflows-compoundgit 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-compound)<a href="https://agentmods.dev/skills/james-traina/compound-science/workflows-compound"><img src="https://agentmods.dev/badge/skills/james-traina/compound-science/workflows-compound.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 | $0.00015 | $0.02946 |
| Opus 5 | $0.00008 | $0.01473 |
| Sonnet 5 | $0.00003 | $0.00589 |
| Haiku 4.5 | $0.00002 | $0.00295 |
Grade A, and why
workflows:compound 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.
How it starts
The opening of the file, as written. The whole thing — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/compound
Pipeline mode: This command operates fully autonomously. All decisions are made automatically.
Coordinate multiple subagents working in parallel to document a recently solved research problem. Creates structured documentation in docs/solutions/ with YAML frontmatter for searchability and future reference.
Purpose
Captures problem solutions while context is fresh. Uses parallel subagents for maximum efficiency — Phase 1 gathers information, Phase 2 assembles the final document.
Why "compound"? Each documented solution compounds your methodological knowledge. The first time you solve a convergence problem takes hours of research. Document it, and the next occurrence takes minutes. Knowledge compounds.
Usage
/workflows:compound # Document the most recent fix
/workflows:compound convergence failure in BLP inner loop # Provide context
/workflows:compound fixed cluster-robust SEs # Brief description
Execution Strategy: Two-Phase Orchestration
<critical_requirement> Only ONE file gets written — the final documentation.
Phase 1 subagents return TEXT DATA to the orchestrator. They must NOT use Write, Edit, or create any files. Only the orchestrator (Phase 2) writes the final documentation file. </critical_requirement>
Phase 1: Parallel Research
<parallel_tasks>
Launch these subagents IN PARALLEL. Each returns text data to the orchestrator.
1. Context Analyzer
- Extracts conversation history for the problem-solving session
- Identifies problem type, estimation method, symptoms, error messages
- Auto-categorizes the problem (see Category Classification below)
- Returns: YAML frontmatter skeleton with problem metadata
2. Solution Extractor
- Analyzes all investigation steps taken during the session
- Identifies root cause (e.g., "ill-conditioned Hessian due to poor starting values")
- Extracts working solution with code examples
- Documents what didn't work and why (important for future reference)
- Returns: Solution content block with code snippets
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.
- 3d ago First seen · 327 lines · 15 tokens per session scan A 19e882b96c77
workflows:compound is a skill published in the GitHub repository James-Traina/compound-science (13 stars, last pushed 5mo ago), licensed MIT. It adds 15 tokens to every session and 2,946 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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