724 skills · sorted by relevance
Design and implement command-line tools with subcommands, options, interactive prompts, and progress indicators. Get command structures, configuration handling, clear error messages, and shell completion scripts, with guidance for automated use and testing across operating systems.
Manage projects involving multiple Workers with a shared plan that records task status, assignments, and dependencies. It covers project setup, task completion updates, plan changes, blocked work, and final notifications to the administrator.
Use Neo4j GenAI Plugin ai.text.* functions and procedures for in-Cypher embedding generation, text completion, structured output, chat, tokenization, and batch ingestion. Covers ai.text.embed(), ai.text.embedBatch(), ai.text.completion(), ai.text.structuredCompletion(), ai.text.aggregateCompletion(), ai.text.chat(), ai.text.tokenCount(), ai.text.chunkByTokenLimit(), and provider configuration for OpenAI, Azure OpenAI, VertexAI, and Amazon Bedrock. Requires CYPHER 25. Replaces deprecated genai.vector.encode(). Use when writing pure-Cypher GraphRAG, embedding nodes in-graph, generating structured maps from prompts, or calling LLMs inside Cypher queries. Does NOT handle neo4j-graphrag Python library pipelines — use neo4j-graphrag-skill. Does NOT handle vector index creation/search — use neo4j-vector-index-skill.
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Generates shell completion scripts for CLI tools. Parses command structure from help output, commander/yargs configs, or manual specification. Produces completions for bash, zsh, and fish that follow each shell's conventions.
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Generates shell completion scripts for CLI tools. Parses command structure from help output, commander/yargs configs, or manual specification. Produces completions for bash, zsh, and fish that follow each shell's conventions.
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Add shell completion support via a 'completion' subcommand for bash, zsh, and fish. Generic pattern for any Commander.js CLI, with static and dynamic completion examples.
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Design or review repeating agent workflows with machine-checkable goals, protected boundaries, independent verification, and retry limits. It helps identify endless retries, manipulated checks, and incorrect completion while keeping final judgment with a human.
Design and refine an AI agent’s available actions, tool inputs, and result formats. Define recovery steps for errors, manage the information kept in context, and evaluate changes through completion rates, retries, and cost per successful task.
Work with Jira issues through Orca's command-line interface. Search issues, move them through workflow states, create follow-up issues, and record completion with a PR or MR link and a comment using guidance matched to the installed Orca version.
Manage Linear issues through Orca’s command-line interface. Find tickets, move them through workflow states, create follow-up tickets linked to a parent, and record completion with a PR/MR link and a comment.
Work with Linear tickets through Orca's CLI. Find issues, update workflow states, report completion with a PR or MR link and a comment, and create follow-up tickets linked to a parent issue.
Audit an AI agent's completion claims using existing logs, artifacts, approvals, and deployment records. Reconstruct the run and assess each claim separately, distinguishing proven results, partial support, contradictory evidence, and missing proof without rerunning the work.
Break a development task into small requirements with explicit acceptance criteria, then repeat implementation and verification while saving progress. Completion depends on evidence for each requirement and reviewer approval, with configurable reviewers and a final cleanup check.
Coordinate project delivery by routing requests to eight specialist skills or running a verified delivery workflow. Use live Jira data for progress analysis and completion forecasts, with recorded evidence and checks before tasks are closed.
Reviews contact, lead capture, and checkout forms to identify obstacles to completion. Produces justified field recommendations, clearer button and error text, mobile layout guidance, and hypotheses for testing alternative designs while preserving useful submission data.
Use an English working contract in Trae to address repeated failures or unverified completion. It separates execution, self-review, and acceptance, guiding agents through small checks and delivering results with evidence, residual risks, and confirmation needs.
Use the P7 engineering workflow in Codex CLI through a command alias. It guides planning, implementation, and a three-question self-review, then requires command and output evidence before reporting completion.
Address repeated failures, passive execution, or unsupported completion claims with stern corporate-style coaching. The skill ties diagnosis to action, prompts meaningful changes of approach, and requires evidence and remaining risks when reporting results.
Execute development subtasks under a supervising agent, starting with an implementation plan and impact analysis. Deliver code and a three-question self-review in a defined completion format for the supervisor to assess.
Guide Trae through stalled tasks by comparing hypotheses, changing approaches after repeated failures, and checking results with relevant commands. It structures delivery around evidence, self-review, and remaining risks rather than unsupported claims of completion.
Guide a Trae agent through recurring failures with diagnosis, alternative approaches, and relevant checks. The skill defines when to intervene, requires evidence for completion, and separates the agent’s self-review from external validation.
Run iterative development with repeated implementation and verification, using failure history to guide changes. When a verification command is configured, an independent hook checks completion after the agent's own tests, with pause and abort markers available.
Audit a development milestone before archiving it by checking requirements, connections between phases, and complete user flows. It reads existing verification reports and gathers technical debt and deferred gaps to assess whether the original completion criteria were met.
Verify work before reporting completion, committing changes, or creating a pull request. Run the checks that support each claim, inspect their output and exit codes, and report the actual results rather than relying on assumptions or agent reports.