contract-validation
Skill2 starsChanged 6 days agoApache-2.0View on GitHub
Validate a feature, workflow, or platform change against an explicit contract. Generic — does not require a dedicated validator agent. Any CLI agent executes the contract, collects evidence, and produces a structured report.
In this repository16 other files it gives its agents.
- AGENTS.mdAGENTS.md
- adopt-evalSkill
- create-prSkill
- designSkill
- graphifySkill
- helm-upgradeSkill
- ingest-readingSkill
- investigate-alertSkill
- k8s-debugSkill
- parallax-reviewSkill
- shiny-engineerSkill
- stakeholder-commsSkill
- stop-slop-codeSkill
- systematic-debuggingSkill
- upstream-triageSkill
- verify-claimsSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 2 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 12
Use when a feature, rollout, or platform change needs to be validated against an explicit pass/fail contract.
- Distinctive: line 27
Read the validation contract.
- Very specific: line 57
Use templates/validation-report.template.json for machine-readable reports or the standard handoff contract for human review.
- Very specific: line 61
When the same contract runs across N targets (clusters, services, dashboards), pair this skill with core/protocols/parallel-dispatch.md: …
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Contract Validation
Use when a feature, rollout, or platform change needs to be validated against an explicit pass/fail contract.
Inputs
- Validation contract — a list of numbered assertions, each with a pass/fail criterion and required evidence.
- Target — repo, service, cluster, dashboard.
- Preconditions — what must be true before starting.
- Test surface — docs, CLI, API, UI, Kubernetes, GitOps, observability.
- Evidence requirements — what counts as proof.
- Output format — JSON report, structured handoff, or both.
A contract is a runnable artifact, not a wish list. See templates/validation-contract.template.md.
Workflow
- Read the validation contract.
- Identify each assertion and its required evidence.
- Confirm prerequisites are satisfied.
- Execute each assertion through the real contract surface where possible (real command, real API, real query).
- Mark each assertion as
pass,fail,blocked, orskipped. - Capture evidence for every result (command output, query result, screenshot, exit code).
- Report blockers and any non-disruptive checks attempted.
- File follow-up
bdtasks for failures or gaps.
Status meanings
pass: observed behavior matches the contract.fail: observed behavior contradicts the contract.blocked: prerequisite unavailable or unsafe to proceed.skipped: explicitly out of scope or not applicable to this run.
Evidence by surface
| Surface | Evidence |
| CLI | command, output summary, exit code |
| API | request, status code, response summary |
| UI | screenshot, console / network summary |
| Kubernetes | read-only command output |
| GitOps | rendered manifests, diff, sync status |
| Observability | query, time window, result summary |
| Logs | LogQL query, count, sample lines |
Output
Use templates/validation-report.template.json for machine-readable reports or the standard handoff contract for human review.
Pairing with parallel dispatch
When the same contract runs across N targets (clusters, services, dashboards), pair this skill with core/protocols/parallel-dispatch.md: launch N workers, each executing the contract against one target, then aggregate.
A 12-check contract run across 3 clusters in parallel completes in ~5 minutes vs ~45 minutes sequential.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

