Borrowing it
Nothing to install: this file belongs to ieeecsopen/mcp-cs. 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/ieeecsopen/mcp-cs/main/.agents/skills/algo-stress-testing/SKILL.mdgit clone --depth 1 https://github.com/ieeecsopen/mcp-csWrote 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/ieeecsopen/mcp-cs/algo-stress-testing)<a href="https://agentmods.dev/skills/ieeecsopen/mcp-cs/algo-stress-testing"><img src="https://agentmods.dev/badge/skills/ieeecsopen/mcp-cs/algo-stress-testing/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/ieeecsopen/mcp-cs/algo-stress-testing"><img src="https://agentmods.dev/badge/skills/ieeecsopen/mcp-cs/algo-stress-testing.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 | $0.00245 | $0.08387 |
| Opus 5.5 | $0.00098 | $0.03355 |
| Sonnet 5.5 | $0.00049 | $0.01677 |
| Haiku 4.5 | $0.00024 | $0.00839 |
Grade A, and why
algo-stress-testing scanned grade A with 1 finding 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 2mo 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
proc = subprocess.run( The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 2mo ago First seen · 732 lines · 245 tokens per session scan A 84283b84dfa6
algo-stress-testing is a skill published in the GitHub repository ieeecsopen/mcp-cs (3 stars, last pushed 1mo ago), with no licence file. It adds 245 tokens to every session and 8,387 once invoked, about $0.0010 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
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polya-fix
Phase 5 of the Pólya Heuristic Coder: First-Principles Bug Remediation, Seam Tracing, Bisection Search (O(log n)), Failing Reproduction Tests, and Incubation Circuit-Breaker Gate (docs/bug-reports/{slug}-bugfix.md).
tdd-bug-report
Workflow for analyzing bug reports, tracing root causes, and generating structured bug-fix implementation plans enforcing the Prove-It pattern (failing regression test first) with rollback strategies.
to-fix
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doubt-driven-development
Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when you want every assumption cross-examined before proceeding, when stress-testing a plan for hidden failure modes, when correctness matters more than speed, when working in unfamiliar code, when stakes are high…
performance-optimization
Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.