Loading...
Loading...
Found 1,233 Skills
Use when you need to create defect reports that can be directly pasted into external defect systems (without saving bug files in the Spec Pack) during the verification phase of the Spec Pack, and write the defect references back to `{FEATURE_DIR}/verification/report-*.md`.
Use when you need to perform I2 (Implementation Execution) in the Spec Pack of sdlc-dev, implement in batches with `{FEATURE_DIR}/implementation/plan.md` as the only SSOT, run minimal verification, write back audit information, and report at batch checkpoints; stop immediately when encountering blocking or clarification required items.
Analyze a screen recording of a manual process and produce targeted, working automation scripts. Extracts frames and audio narration from video files, reconstructs the step-by-step workflow, and proposes automation at multiple complexity levels using tools already installed on the user machine.
Check/manage Azure quotas and usage across providers. For deployment planning, capacity validation, region selection. WHEN: "check quotas", "service limits", "current usage", "request quota increase", "quota exceeded", "validate capacity", "regional availability", "provisioning limits", "vCPU limit", "how many vCPUs available in my subscription".
Three-layer verification pipeline for AI output. Extracts verifiable claims, finds supporting or contradicting sources via web search, runs adversarial review for hallucination patterns, and produces a structured verification report with source links for human review.
Launch new products from idea to first customers. Use when launching products, finding early adopters, building launch week playbooks, diagnosing why adoption stalls, or learning that press coverage does not equal growth. Includes the three-layer diagnosis, the 2-week experiment cycle, and the launch that got 50K impressions and 12 signups.
Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity, security, compliance, and multi-resource topologies with WAF alignment. Generates Bicep or Terraform directly (no azd). WHEN: 'plan Azure infrastructure', 'architect Azure landing zone', 'design hub-spoke network', 'plan multi-region DR topology', 'set up VNets firewalls and private endpoints', 'subscription-scope Bicep deployment'. PREFER azure-prepare FOR app-centric workflows.
Helps users find the right Azure RBAC role for an identity with least privilege access, then generate CLI commands and Bicep code to assign it. USE FOR: "what role should I assign", "least privilege role", "RBAC role for", "role to read blobs", "role for managed identity", "custom role definition", "assign role to identity". DO NOT USE FOR: creating managed identities (use azure-security), general security hardening (use azure-security-hardening), networking permissions (use azure-networking).
Use when you need to execute R3 (Prototype Generation) in the product requirement Spec process of sdlc-dev, generate requirements/prototype.md based on requirements/prd.md (including task flow + page structure + ASCII wireframe + AC mapping + walkthrough script), and avoid proceeding with generation without context/PRD, using Open Questions instead of verification checklists, or using non-ASCII formats that make the prototype untraceable and unreviewable.
Use this when the Discover (reverse engineering) of legacy projects tends to get out of control in coverage. You need to first conduct module classification (P0/P1/P2) and constrain the depth of reverse engineering, ensuring that high-ROI modules are made traceable first instead of "writing everything but making it unmaintainable."
Security-first vetting for OpenClaw skills. Use before installing any skill from ClawHub, GitHub, or other sources. Checks for red flags, permission scope, and suspicious patterns.
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer