Use when an agent is about to claim completion, correctness, safety, publication, deployment, or any consequential external fact.
npx skills add https://github.com/Mark393295827/third-brain-v7-skills --skill verify-before-claim
<skill_contract>
<input>One falsifiable claim, its artifact or system, risk, expected result, permissions, and available checks.</input>
<output>A scoped claim decision with fresh evidence, residual risk, approval, and rollback status.</output>
<done>The cheapest direct check after the final material change supports the exact allowed wording.</done>
<non_goals>Producing the artifact, inferring success from effort, or widening a claim beyond checked evidence.</non_goals>
No evidence, no claim. Match the check to the exact claim, use fresh evidence, and keep execution authority separate from approval authority for consequential actions.
Provide: proposed claim, artifact or system, risk level, available checks, expected result, permissions, and rollback path.
<intake>
</intake>
<unknowns_gate>
If the artifact, expected behavior, or verification method is missing, return NEEDS_INPUT. If only indirect evidence exists, return INSUFFICIENT_EVIDENCE or narrow the claim; never fill the gap with confidence language.
</unknowns_gate>
<execute>
Run the selected check after the final material change. Examples: targeted test, lint, build, link check, read-after-write, diff inspection, source comparison, dashboard query, or deployment health check. Capture command/query, timestamp, scope, exit status, and key output.
For material or consequential claims, obtain independent verification from a separate check, reviewer, or evidence source. Require human approval before irreversible publication, deployment, spending, deletion, credential use, or policy change. Confirm the rollback path before execution.
For Graph claims, verify static contract integrity, every required node and join
receipt, terminal acceptance, budgets, permission/compensation state, and
checkpoint identity. Passing nodes do not prove the end-to-end graph.
</execute>
<evaluate>
Compare observed versus expected result. Decide supported, partially_supported, unsupported, or blocked. Check scope: passing one test cannot prove the full suite; a successful write cannot prove link integrity. State residual risk and evidence age.
</evaluate>
<retry_policy>
max_attempts: 2. Retry only after diagnosing the failure and changing input, tool, scope, or strategy. Stop on repeated signature or NO_PROGRESS; never rerun an unchanged check to manufacture confidence.
</retry_policy>
<state_contract>
Persist {run_id, status, attempt, budget, evidence, unknowns, last_error, next_action} plus claim, risk, check specification, expected/observed results, approval receipt, and rollback readiness. Append verification events so evidence age remains visible.
</state_contract>
NEEDS_INPUT: the claim or expected result is ambiguous; ask one discriminating probe.INSUFFICIENT_EVIDENCE: no direct check supports the requested scope; narrow or withhold the claim.BLOCKED_PERMISSION: approval or access is absent; do not perform the action.VERIFY_FAILED: observed evidence contradicts the claim; report failure and recovery.NO_PROGRESS: the same verification signature fails twice; stop and escalate.BUDGET_STOP: verification budget is exhausted; preserve evidence and do not claim completion. max_attempts: 2.Return status, result (claim decision and allowed wording), evidence (fresh receipts), unknowns (including residual risk), and next_action (repair, approval, rollback, or stop).
withhold graph completion and return VERIFY_FAILED.
INSUFFICIENT_EVIDENCE; do not substitute an uncited recollection for the external fact.</skill_contract>
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take mark393295827/verify-before-claim from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.