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 ArabelaTso/Skills-4-SE --skill counterexample-generatorgit clone --depth 1 https://github.com/ArabelaTso/Skills-4-SEWrote 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/arabelatso/skills-4-se/counterexample-generator)<a href="https://agentmods.dev/skills/arabelatso/skills-4-se/counterexample-generator"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/counterexample-generator/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/arabelatso/skills-4-se/counterexample-generator"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/counterexample-generator.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 | $0.00068 | $0.04970 |
| Opus 5.5 | $0.00027 | $0.01988 |
| Sonnet 5.5 | $0.00014 | $0.00994 |
| Haiku 4.5 | $0.00007 | $0.00497 |
Grade A, and why
counterexample-generator 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 — 848 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Counterexample Generator
Systematically generate counterexamples that demonstrate why verification fails. Provides concrete input values, execution traces, and diagnostic information to help understand and fix specification or implementation issues.
Core Capabilities
1. Precondition Violation Detection
Generate inputs that violate preconditions:
- Invalid parameter values - Out-of-range inputs
- Null/undefined references - Missing required objects
- State violations - Invalid object states
- Type mismatches - Incorrect types
- Constraint violations - Broken business rules
2. Postcondition Failure Analysis
Find executions where postconditions fail:
- Return value violations - Wrong return values
- State inconsistencies - Incorrect final states
- Invariant breaks - Invariants violated after execution
- Side effect errors - Unexpected modifications
- Exception failures - Wrong or missing exceptions
3. Invariant Violation Discovery
Identify cases where invariants break:
- Class invariant violations - Object consistency broken
- Loop invariant violations - Invariant not maintained
- Data structure violations - Consistency broken
- Temporal violations - Time-based properties fail
- Concurrency violations - Race conditions exposed
4. Execution Trace Generation
Produce detailed execution paths:
- Step-by-step traces - Line-by-line execution
- State snapshots - Variable values at each step
- Branch decisions - Which paths taken
- Call stacks - Function call hierarchy
- Symbolic execution - Constraint-based paths
Counterexample Generation Workflow
Step 1: Identify the Failure
Understand what verification failed:
From verification tools:
Verification failed: Postcondition violated
Function: withdraw
Line: 15
Property: account.balance == old(account.balance) - amount
From assertions:
AssertionError: assert result >= 0
in function: sqrt
with input: x = -1
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 · 848 lines · 68 tokens per session scan A d1cd4412f180
counterexample-generator is a skill published in the GitHub repository ArabelaTso/Skills-4-SE (259 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 68 tokens to every session and 4,970 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
diagnose
Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.
skill-incident-management
Incident response, postmortem facilitation, root cause analysis (RCA), blameless culture, incident timeline documentation, and incident classification. Use when responding to production incidents, writing incident reports, facilitating postmortems, or improving incident response processes.
triage
Triage issues through a state machine driven by triage roles. Use when user wants to create an issue, triage issues, review incoming bugs or feature requests, prepare issues for an AFK agent, or manage issue workflow.
skill-performance-optimization
Profile systems, identify bottlenecks, optimize code and infrastructure for speed, throughput, and resource efficiency.
skill-code-quality
This skill should be used when implementing code quality standards, setting up linting/testing/security scans, enforcing coverage thresholds, or establishing CI/CD guardrails.
skill-pr-review
Comprehensive code review for pull requests using parallel multi-agent analysis. Audits CLAUDE.md compliance, checks for bugs, analyzes git history, reviews comments, and filters by confidence score. Use when reviewing a GitHub PR, mentions "code review", "review this PR", or "/code-review".