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/microsoft/agent-framework/regex-testernpx skills add microsoft/agent-framework --skill regex-testergit clone --depth 1 https://github.com/microsoft/agent-frameworkWhat 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 | $0.00033 | $0.00358 |
| Opus 5 | $0.00016 | $0.00179 |
| Sonnet 5 | $0.00007 | $0.00072 |
| Haiku 4.5 | $0.00003 | $0.00036 |
Grade A, and why
regex-tester scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- regex-tester — 100% identical, 0 lines differ
What it actually says
Usage
When the user asks you to create, validate, or debug a regular expression:
- Understand the requirement — clarify what the pattern should match and what it should reject.
- Consult the cheatsheet — review
references/regex-cheatsheet.mdfor syntax reminders if needed. - Write and execute test code — use the
execute_codetool to run Python code that:- Compiles the regex with
re.compile() - Tests it against a set of positive examples (should match) and negative examples (should not match)
- Extracts and displays any capturing groups
- Reports pass/fail for each test case
- Compiles the regex with
- Iterate — if any test fails, refine the pattern and re-run until all cases pass.
- Present the result — give the user the final pattern, explain what each part does, and show the test results.
Example Test Script
import re
pattern = re.compile(r'^[\w.+-]+@[\w-]+\.[\w.-]+$')
positives = ["[email protected]", "[email protected]"]
negatives = ["@missing.com", "no-at-sign", "spaces [email protected]"]
for s in positives:
assert pattern.match(s), f"FAIL: expected match for '{s}'"
for s in negatives:
assert not pattern.match(s), f"FAIL: expected no match for '{s}'"
print("All tests passed!")
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.
- 2d ago First seen · 37 lines · 33 tokens per session scan A bb19f508c078
regex-tester is a skill published in the GitHub repository microsoft/agent-framework (13,222 stars, last pushed 3d ago), licensed MIT. It adds 33 tokens to every session and 358 once invoked, about $0.0002 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.
Other skills, from other repositories
swarmclaw
AI agent runtime and multi-agent orchestration platform. Teaches agents how to use SwarmClaw's 6 primitive tools, persistent memory, dreaming, delegation, connectors, credentials, and the skill system. Use when an agent is running on SwarmClaw and needs to understand the platform's capabilities.
agent-collaboration
Use this skill when coordinating multiple AI agents. Covers multi-agent patterns, handoffs, and orchestration strategies.
crewai-multi-agent
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies…
company-product-context
Compiles comprehensive company product context from PDF documents, web research, and industry knowledge.
codebase-context-extractor
This skill provides a comprehensive context extraction system for large codebases. It intelligently analyzes code structure, dependencies, and relationships to extract relevant context for understanding, debugging, or modifying code.
deep-researcher
Performs comprehensive, multi-layered research on any topic with structured analysis and synthesis of information from multiple sources.