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 oyi77/1ai-skills --skill test-agentgit clone --depth 1 https://github.com/oyi77/1ai-skillsWrote 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/oyi77/1ai-skills/test-agent)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/test-agent"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/test-agent/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/oyi77/1ai-skills/test-agent"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/test-agent.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 | $0.00023 | $0.01101 |
| Opus 5 | $0.00012 | $0.00550 |
| Sonnet 5 | $0.00005 | $0.00220 |
| Haiku 4.5 | $0.00002 | $0.00110 |
Grade A, and why
test-agent 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 9d 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Agent
Quick Reference — see parent for full agent ecosystem.
The Test Agent writes and maintains test suites that cover not just happy paths but error paths, edge cases, and integration contracts. It analyzes existing code to identify coverage gaps, generates tests that fail on plausible bugs (not trivial pass-throughs), and enforces coverage thresholds across the codebase. Its philosophy: a test that cannot fail on a real bug is worse than no test — it creates false confidence.
When Not to Use
- Simple or one-off tasks — if the task is straightforward, direct execution is faster than structured methodology.
- Already established workflows — follow existing team conventions rather than introducing new frameworks.
- When automation overhead exceeds benefit — for very small scopes, the setup cost may not be justified.
Dependencies
- Python 3.8+ or Node.js 18+
- Access to relevant APIs/services for your specific use case
- Basic understanding of the domain concepts
Commands
# Refer to the skill's usage section for specific commands
# Adapt these to your workflow
Key Responsibilities
- Coverage gap analysis: Profile the existing test suite to find uncovered branches, error paths, and edge cases — not just line coverage
- Generate meaningful tests: Write tests that defend explicit contracts (inputs → outputs, error states, invariants, transitions) rather than testing implementation details
- Regression test for bugs: For every bug fix, generate a test that reproduces the original failure and confirms it stays fixed
Code Example
"""Minimal test agent pattern — analyze coverage and generate tests."""
import json, sys
from pathlib import Path
def analyze_coverage(source_path: str, test_path: str) -> dict:
"""Identify uncovered functions and generate skeleton tests."""
source = Path(source_path)
tests = Path(test_path)
source_funcs = set()
for file in source.rglob("*.py"):
content = file.read_text()
for line in content.split("\n"):
stripped = line.strip()
if stripped.startswith("def ") and not stripped.startswith("def _"):
name = stripped.split("(")[0].replace("def ", "")
source_funcs.add(name)
test_funcs = set()
for file in tests.rglob("test_*.py"):
content = file.read_text()
for line in content.split("\n"):
stripped = line.strip()
if stripped.startswith("def test_"):
name = stripped.split("(")[0].replace("def ", "")
test_funcs.add(name)
uncovered = source_funcs - test_funcs
return {
"source_functions": sorted(source_funcs),
"test_functions": sorted(test_funcs),
"uncovered": sorted(uncovered),
"coverage_pct": round(len(test_funcs) / max(len(source_funcs), 1) * 100, 1),
"recommendations": [f"Add test for {fn}" for fn in sorted(uncovered)[:10]]
}
if __name__ == "__main__":
result = analyze_coverage(sys.argv[1], sys.argv[2])
print(json.dumps(result, indent=2))
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.
- 9d ago First seen · 127 lines · 23 tokens per session scan A b3b064940fb1
test-agent is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed yesterday), licensed MIT. It adds 23 tokens to every session and 1,101 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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bestow-curse
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confusion
Confusion makes targets act randomly and unpredictably. The real-world version is chaos engineering: injecting controlled randomness, unexpected inputs, and edge cases to discover how systems behave when things go wrong. This is fuzzing, monkey testing, and the art of breaking things on purpose so they do not break by…