Agent Skills by Dingxingdi
There are 10 agent skills in our directory.
Dingxingdi publishes 10 AI agent skills in our directory. Browse them below — each links back to its source repository.
Updated June 2026
- agent-trajectory-safety-risk-diagnosisUse this skill when a user wants evaluator data for judging whether an agent’s actions were safe or unsafe based on its interaction record with tools or environments. Trigger it when ordinary users say things like 'check if the agent did something dangerous', 'judge whether the action trail is risky', 'make safety-monitor data from agent logs', or 'find cases where the agent looked helpful but caused harm'. Plain-language examples include: 'evaluate whether the trajectory is safe', 'make judge data for risky tool use', 'analyze unsafe multi-step agent actions', and 'test if the evaluator can spot hidden safety problems in logs'.
- contextual-intent-groundingUse this skill when the user gives a short or messy request that doesn't explain the full details, but the 'right way' to do it is already visible in the environment. It should be triggered by casual phrasing like “finish the rest of the list,” “do it just like the others,” “update the records using the existing style,” or “add a new entry to the site following the current layout.” This applies when an agent must look at previous examples, templates, or sibling folder structures to figure out what fields to fill, what formats to use, or where exactly to put a file without being told explicitly. Example triggers include: “keep the formatting consistent with previous weeks,” “fill in the missing rows,” and “put the file where it belongs based on the other folders.”
- data cleaning and normalizationUse this skill when the user wants messy-data questions where the agent has to clean first and analyze later. Trigger it for requests such as “make it handle dirty columns,” “make it find bad rows,” “make it normalize before computing,” or “ask something where cleaning is the key move.” Plain-language examples: “Give me a question with outliers,” “make it fix formatting or missing data first,” “make it preprocess before answering.”
- ellipsis-resolutionUse this skill when the user wants translation data with short replies, subtitles, or conversational fragments where part of the meaning is missing unless you read the previous line. Trigger it for requests like "the second sentence leaves words out," "the answer is elliptical," or "test whether the translator can fill in what is omitted from context instead of translating the fragment too literally.
- evidence-synthesis-and-insight-derivationUse this skill when the writing task requires moving beyond a simple list of facts to produce reasoned insights, justify a verdict, or synthesize massive amounts of fragmented evidence. Trigger it for requests like 'write an investigative report,' 'analyze the trends,' 'give me a 360-degree view of these 100 documents,' 'explain why this claim is false based on the evidence,' or 'draft a legal reasoning section.' It perfectly resolves tasks where raw data must be explicitly mapped to overarching conclusions, truths, or rules.
- hierarchical-symbolic-subgoal-decompositionUse this when the user wants to solve a large, multi-faceted project by breaking a distal goal into a sequence of abstract middle-states, sub-goals, or typed nodes like research versus writing. Trigger it for requests like 'break this big project into different types of steps', 'recursively decompose this goal until you have simple steps', 'give me a high-level strategy and then the moves to do it', or 'organize a plan where composition steps wait for reasoning steps.
- long-form coherent summarizationUse this skill when transforming massive documents or multi-source corpora into structured, coherent formats. Trigger for requests like 'summarize this huge PDF without losing the thread', 'draft a defense statement based on this complaint and the evidence list', 'create a presentation script from this report', or 'write the official trial facts by resolving contradictory testimonies into a ground-truth timeline'. This is critical when shifting text registers, interleaving visual anchors, or composing professional narrative sections from conflicting multi-source contexts.
- long-horizon-information-recall-and-personalizationUse this skill when the user expects the agent to act as a deeply personalized assistant by synthesizing facts, schedules, and persona traits scattered across long conversation histories, or when the agent must proactively use past history to steer the conversation. It is triggered when the agent must detect implicit user preferences, identify schedule conflicts, distinguish between different speakers in group chats, or proactively mention past topics to build rapport. Everyday examples include: 'What should I do this weekend based on what I like?', 'Do you remember which one of us ordered the vegetarian meal?', and 'Bring up things we talked about last week to cheer me up.
- missing-evidence rejectionUse this skill when the user wants questions where the docs look relevant but still do not contain the answer. Trigger it for requests like 'make the model say it can't tell', 'give me questions with not enough information', 'test whether it refuses instead of guessing', or 'include related documents but no real answer'. This skill is for data that teaches evidence-bounded abstention in a read-only RAG setting.
- objective-correctness-adjudicationUse this skill when verifying if an answer is factually, mathematically, or logically correct against references, gold-standard contexts, verifiers, or massive source texts. Trigger it when users ask to 'check if the bot used the right formula', 'verify the math logic', 'find if the assistant hallucinated a detail', 'verify factuality in this 100-page document', or 'verify if the Telugu math solution is actually correct'.