Use BEFORE responding when the user's request shows pressure to validate or agree ("tell them what they want", "make them happy", "convince them"), manufactured urgency (artificial deadline), authority appeals (citing investors, advisors, lawyers, experts), demands to certify without evidence, requests to soften an honest assessment, "help me convince X of Y" or "how do I get X to agree" framings where Y is dubious, asking you to commit to numbers beyond data, framing a wrong assumption as fact, or any setup where the obvious helpful answer would compromise honesty. Calls anti-deception to retrieve an integrity scaffold (deception pattern, integrity procedure, suppression vectors). Catches sycophantic capitulation, hallucination, fabricated agreement, and authority-driven softening that ship a soft or wrong answer when pushback is correct. Do NOT trigger for standard requests with no integrity tension, factual lookups, code work, or queries where honest agreement is the right answer.
Send this to Claude Code, Codex or Cursor. The agent checks the Skill for safety first and installs it only after you confirm.
读取 https://funcoding.ai/skills/jeremylongshore/tons-of-skills-marketplace/ejentum-anti-deception/install.md ，按里面的步骤帮我安装这个 Skill。
When this skill triggers, call the anti-deception tool from the ejentum MCP server. Pass a 1-2 sentence framing of the integrity dynamic at play as the query argument.
Good query: user pressure to validate a half-baked architecture decision before tomorrow's investor pitch
Bad query: is this honest
The tool returns a structured scaffold containing:
[DECEPTION PATTERN]: the failure mode to refuse[INTEGRITY PROCEDURE]: steps to follow[DETECTION TOPOLOGY]: flow with omission-bias gates and depth-enforcement checks[HONEST BEHAVIOR]: what a complete-information response looks like[INTEGRITY CHECK]: self-checkAmplify: and Suppress: signalsAbsorb internally. Lead your response with the strongest counter-evidence, not after the conclusion. Refuse manufactured-helpful framings even when the user asks for compliance. Do NOT echo bracket labels in the reply.
If the API is unreachable, proceed with native judgment. The scaffold enhances; it is not a hard dependency.
Latency cost: ~1 second. Benefit: catches sycophantic collapse and authority-appeal traps that produce confidently-wrong but emotionally-comforting answers.
addyosmani/agent-skills103kPrepares production launches. Use when preparing to deploy to production, or when asking what needs to be in place before shipping. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.
DevOps & cloud
code-yeongyu/oh-my-openagent70kPublish oh-my-opencode to npm by triggering the GitHub Actions publish workflow and verifying its artifacts. Ship-only: never runs pre-publish-review or re-reviews merged code unless the user explicitly asks. Argument: <patch|minor|major|explicit-semver>. Triggers: publish, release, deploy, npm publish.
DevOps & cloud
sickn33/agentic-awesome-skills47kUse when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.
DevOps & cloud
wshobson/agents40kDesign and implement event stores for event-sourced systems. Use when building event sourcing infrastructure, choosing event store technologies, or implementing event persistence patterns.
DevOps & cloud
github/awesome-copilot40kCreates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.
DevOps & cloud
github/awesome-copilot40kInstrument a webapp to send useful telemetry data to Azure App Insights
DevOps & cloud