Skip to content
Agentmart
Submit a tool

discover-invariants

Identify always and never expectations and their applicable scope when exploring software behavior.

Skill by testobsessed

IndexedMIT
Write a review

Chalk Labs

Need help deploying discover-invariants?

Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.

Book a call

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/testobsessed-discover-invariants

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Get the weekly diff

The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.

We use your email only for this newsletter. Privacy

About

Identify always and never expectations and their applicable scope when exploring software behavior.

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/discover-invariants

What's inside

Skill name
discover-invariants
Description
Identify always and never expectations and their applicable scope when exploring software behavior.
License
MIT
Instructions
17 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# Discovering Invariants

Identify candidate domain rules that should hold across inputs, sequences, configurations, and failures. Sources include canonical documentation, available repository material, cached domain research, user goals, and good design practices for the interface being explored. These are useful inputs, not prerequisites. Infer candidate rules from domain relationships and observed workflows as well as explicit always/never statements. Use research-domain for domain context within its cache and research limits.

For example, saving a form that receives a server error while leaving the user blocked with no useful feedback challenges a design expectation that users receive meaningful feedback on their actions. Record the visible outcome, response evidence, expectation source, and user impact separately.

Existing patterns include:

- conservation: data, work, money, defects, or records do not appear or disappear without a modeled event;
- isolation: actions on one user, scenario, tenant, stream, or object do not unexpectedly affect another;
- temporal integrity: delayed results apply to the state or candidate actually evaluated.

Record why each candidate should hold and the conditions in which it applies. Treat a candidate as a fallible expectation, not an established fact. Use [report-bugs](../report-bugs/SKILL.md) as the authority for deciding whether an observation warrants a bug report. An unresolved expectation remains a product question unless the behavior meets that skill's reporting criteria; explicit stakeholder agreement is not required for reliably reproducible behavior that is obviously wrong in context.

Surface information for the human or downstream agent to decide what to do; stakeholder agreement is not a prerequisite to reporting a surprise. This skill need not adjudicate or argue whether intended behavior is acceptable. Preserve candidate rules and contradictory evidence promptly in the existing raw log, using exploratory-session-log by default unless the user opts out. Index reusable entries at session closure or handoff; do not repeat run setup or load logging guidance already in context.

From testobsessed/exploratory-testing-skills, MIT.

More from testobsessed/exploratory-testing-skills

Signals

Stars
0
Forks
0
Open issues
0
Last commit (this folder)
Oct 4, 2026 (27 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from testobsessed

Done for you

Rather not set it up yourself?

Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.

See how we build AI agents →Read the case studies

Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.

launches shipped
50+
launches shipped
media placements
500+
media placements
founders on every account
2
founders on every account

Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block

Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.

Get discover-invariants running in your business

Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.

Goes to the Chalk Labs founders, not the newsletter. Privacy

Need help deploying discover-invariants?

CallGet help
Cite this page

discover-invariants skill. Agent Mart. https://agentmart.dev/skills/testobsessed-discover-invariants (last updated 2026-10-24)