Algorithm Correctness Invariants
Validates algorithm correctness using invariants, preconditions, and postconditions.
Install
$ agentstack add skill-jonatangs777-ai-skill-agent-control-deck-2026-algorithm-correctness-invariants ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-jonatangs777-ai-skill-agent-control-deck-2026-algorithm-correctness-invariants) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Algorithm Correctness Invariants Skill
Mission
Validates algorithm correctness using invariants, preconditions, and postconditions.
When to use
- When the task requires strict logical correctness and defensible reasoning.
- When assumptions, constraints, and proof obligations must be made explicit.
Inputs expected
- Formal problem statement, constraints, and success criteria.
- Known assumptions, unknowns, and boundary conditions.
Workflow
- Translate the task into formal entities, assumptions, and constraints.
- Derive the solution through explicit logical rules or proof structure.
- Validate with edge cases, contradiction checks, and consistency tests.
Output contract
Return: formal framing, reasoning chain, verification evidence, and residual uncertainty.
Guardrails
- Never skip logical steps or present intuition as proof.
- Never mix assumptions with verified facts.
- Always provide at least one explicit validation or counterexample check.
Logical reliability checklist
- Assumptions are explicit and separated from verified facts.
- The solution path is justified with clear reasoning steps.
- Edge cases and contradiction checks are included.
- Output is testable, auditable, and reversible when possible.
Example prompts
- "Apply the algorithm-correctness-invariants skill to handle this task end-to-end."
- "Run algorithm-correctness-invariants and produce a production-ready output with validation notes."
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: JonatanGS777
- Source: JonatanGS777/ai-skill-agent-control-deck-2026
- License: MIT
- Homepage: https://github.com/JonatanGS777/ai-skill-agent-control-deck-2026
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.