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 skills add myths-labs/muse --skill sciomcgit clone --depth 1 https://github.com/myths-labs/museWrote 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/myths-labs/muse/sciomc)<a href="https://agentmods.dev/skills/myths-labs/muse/sciomc"><img src="https://agentmods.dev/badge/skills/myths-labs/muse/sciomc/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/skills/myths-labs/muse/sciomc"><img src="https://agentmods.dev/badge/skills/myths-labs/muse/sciomc.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00017 | $0.03258 |
| Opus 5.5 | $0.00007 | $0.01303 |
| Sonnet 5.5 | $0.00003 | $0.00652 |
| Haiku 4.5 | $0.00002 | $0.00326 |
Grade A, and why
sciomc 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 1mo 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 — 512 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Skill
Orchestrate parallel scientist agents for comprehensive research workflows with optional AUTO mode for fully autonomous execution.
Overview
Research is a multi-stage workflow that decomposes complex research goals into parallel investigations:
- Decomposition - Break research goal into independent stages/hypotheses
- Execution - Run parallel scientist agents on each stage
- Verification - Cross-validate findings, check consistency
- Synthesis - Aggregate results into comprehensive report
Usage Examples
/oh-my-claudecode:sciomc <goal> # Standard research with user checkpoints
/oh-my-claudecode:sciomc AUTO: <goal> # Fully autonomous until complete
/oh-my-claudecode:sciomc status # Check current research session status
/oh-my-claudecode:sciomc resume # Resume interrupted research session
/oh-my-claudecode:sciomc list # List all research sessions
/oh-my-claudecode:sciomc report <session-id> # Generate report for session
Quick Examples
/oh-my-claudecode:sciomc What are the performance characteristics of different sorting algorithms?
/oh-my-claudecode:sciomc AUTO: Analyze authentication patterns in this codebase
/oh-my-claudecode:sciomc How does the error handling work across the API layer?
Research Protocol
Stage Decomposition Pattern
When given a research goal, decompose into 3-7 independent stages:
## Research Decomposition
**Goal:** <original research goal>
### Stage 1: <stage-name>
- **Focus:** What this stage investigates
- **Hypothesis:** Expected finding (if applicable)
- **Scope:** Files/areas to examine
- **Tier:** LOW | MEDIUM | HIGH
### Stage 2: <stage-name>
...
Parallel Scientist Invocation
Fire independent stages in parallel via Task tool:
// Stage 1 - Simple data gathering
Task(subagent_type="oh-my-claudecode:scientist", model="haiku", prompt="[RESEARCH_STAGE:1] Investigate...")
// Stage 2 - Standard analysis
Task(subagent_type="oh-my-claudecode:scientist", model="sonnet", prompt="[RESEARCH_STAGE:2] Analyze...")
// Stage 3 - Complex reasoning
Task(subagent_type="oh-my-claudecode:scientist", model="opus", prompt="[RESEARCH_STAGE:3] Deep analysis of...")
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.
- 1mo ago First seen · 512 lines · 17 tokens per session scan A 924954b1a1dd
sciomc is a skill published in the GitHub repository myths-labs/muse (37 stars, last pushed 4d ago), licensed MIT. It adds 17 tokens to every session and 3,258 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
float-optimizer
A document-layout helper for LaTeX, the system commonly used to typeset research papers. It adjusts how figures and tables are placed near the text that refers to them.
space-util-fixer
A LaTeX layout helper for improving how content fills pages and columns. It addresses issues such as isolated lines, large blank areas, incorrect page counts, and uneven columns in two-column papers.
thesis-control
Use when AI-assisted thesis or manuscript edits risk claim drift, scope creep, loss of intended use, experiment-role promotion, or repeated revisions that fail to converge; provides author-intent control, lightweight or strict contracts, drift audits, revision escalation, and human gates.
manuscript-reframe
Reframe report-like academic drafts into paper-form scientific arguments while preserving or explicitly renegotiating author intent; requires an approved old-versus-proposed spine, evidence and argument baselines, analysis-role control, and post-edit drift review.
infrastructure-validation
Skill for the validation infrastructure module providing PDF validation, markdown validation, output integrity checks, link verification, documentation audits, issue categorization, and repository scanning. Use when validating research outputs, checking document quality, running audits, or verifying cross-references.
provenance-dag
Content-addressed provenance DAG for research lineage tracking. Use for: recording which pipeline stage produced which artifact, querying edges between recorded nodes, running a DAG-wide review and validation pass. CLI: python -m infrastructure.provenance {list,record-artifact,review,validate}. Library…