Borrowing it
Nothing to install: this file belongs to darylmcd/Roslyn-Backed-MCP. 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/darylmcd/Roslyn-Backed-MCP/main/.claude/skills/mcp-server-stress/SKILL.mdgit clone --depth 1 https://github.com/darylmcd/Roslyn-Backed-MCPWrote 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/darylmcd/roslyn-backed-mcp/mcp-server-stress)<a href="https://agentmods.dev/skills/darylmcd/roslyn-backed-mcp/mcp-server-stress"><img src="https://agentmods.dev/badge/skills/darylmcd/roslyn-backed-mcp/mcp-server-stress/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/darylmcd/roslyn-backed-mcp/mcp-server-stress"><img src="https://agentmods.dev/badge/skills/darylmcd/roslyn-backed-mcp/mcp-server-stress.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 | $0.00320 | $0.01521 |
| Opus 5 | $0.00160 | $0.00760 |
| Sonnet 5 | $0.00064 | $0.00304 |
| Haiku 4.5 | $0.00032 | $0.00152 |
Grade A, and why
mcp-server-stress 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 11d 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.
/mcp-server-stress $ARGUMENTS
Maintainer-only repo-local alias for /mcp-server-surface-test --output-mode=fragments. Routes to the canonical audit prompt at ${CLAUDE_PLUGIN_ROOT}/skills/mcp-server-surface-test/prompts/full.md with fragments-mode emission so findings produce <audited-repo-root>/backlog.d/<finding-id>.md files for the /backlog-intake pipeline.
What this skill does
Invoke /mcp-server-surface-test --output-mode=fragments against the current Claude Code session's repo root (the only path the Roslyn MCP server's sanctioned-root restriction permits — see Limitations below). Forward any additional flags (--no-worktree, --single-agent, --quick) verbatim.
Why this skill exists (and why it's a thin alias)
Before v1.X.Y, /mcp-server-stress used a separate maintainer-overlay.md prompt that duplicated ~85% of full.md while adding three repo-coupled phases (backlog.d/ fragment emission, ai_docs/audit-reports/ report path, ai_docs/backlog.md regression cross-check). Every prompt fix had to be applied to both files, and they drifted in subtle ways.
The v1.X.Y refactor folded the maintainer-overlay's unique content into full.md behind:
--output-mode=fragments→ Phase 19 emits backlog.d/ fragments (the maintainer-overlay's Phase 19 behavior).- Auto-detection of
ai_docs/audit-reports/vsaudit-reports/→ reports land in the maintainer's doc-audit schema location when that schema exists, top-level otherwise. - Auto-detection of
ai_docs/backlog.mdvsbacklog.mdfor regression source → Phase 18's "prior source" probe handles both.
/mcp-server-stress survives as a 1-line alias because:
- Muscle memory. The maintainer types
/mcp-server-stressreflexively; renaming to/mcp-server-surface-test --output-mode=fragmentseverywhere is friction. - Repo-local discoverability. This skill is only available from Claude Code sessions rooted in Roslyn-Backed-MCP (per
.claude/skills/discovery). The repo-local placement signals that fragments-mode is the maintainer-standard invocation.
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.
- 11d ago First seen · 55 lines · 320 tokens per session scan A 64e0eaf6b37a
mcp-server-stress is a skill published in the GitHub repository darylmcd/Roslyn-Backed-MCP (1 stars, last pushed today), licensed MIT. It adds 320 tokens to every session and 1,521 once invoked, about $0.0016 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 2026-08-31.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.