be more ambitious

    1

    Upgrade basic tasks into expert-level prompts using Altitude, Depth, and Range axes for professional-grade AI outputs.

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    be more ambitious

    Example session with this skill installed

    I need to update the performance numbers in my monthly engineering report. Be more ambitious with this prompt.

    • Read your context and instructions
    • Compiled the be more ambitious

    I have analyzed the task. You are currently looking to sync data for a monthly report, but the goal is likely to identify system bottlenecks and justify infrastructure spend for the next quarter.

    Upgraded Engineering Performance Prompt

    Analyze the raw Prometheus metrics and GitHub deployment logs from the last 30 days to identify the three most significant regressions in system latency. Instead of summarizing the dashboard, correlate latency spikes with specific PR merges and infrastructure changes. 

Act as a Site Reliability Engineer, a Data Scientist, and a Forensic Architect. The SRE will map the causal link between deployments and SLO misses. The Data Scientist will test the assumption that 'database load' was the primary cause by running a correlation analysis against concurrent background jobs. The Forensic Architect will act as a red team, attempting to disprove the findings by finding alternative explanations in the network VPC logs.

Deliver a report that identifies the 'goal beyond the goal': which three services require immediate refactoring to prevent a total outage, and what is the projected cost of inaction?

    Prerequisites

    • Access to raw Prometheus metrics and GitHub API logs.
    • A statistical tool (like a Python notebook) for correlation analysis.

    Outside Judge: Compare findings against the PagerDuty incident history to see if identified spikes actually resulted in pings.

    Risks and limits

    • Large-scale log analysis may hit rate limits or incur high token costs.
    • The model may misinterpret network noise as causal signals without clean data.

    Next steps

    1. Smallest first run: Run this prompt against a single 24-hour window where a known latency spike occurred.
    2. Data Check: Ensure you have the raw logs exported rather than just screenshots of Grafana charts.
    3. Approval: Confirm if you want the model to suggest specific budget reallocations based on the findings.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Stop settling for shallow, template-driven AI outputs. This skill applies Felix Rieseberg's "How to be more ambitious" framework to your tasks, pushing your model to function as a field-leading expert rather than a basic assistant. It is designed for developers and power users who need their prompts to handle complex outcomes, deep technical analysis, and multi-disciplinary logic.

    What it does

    • Altitude Adjustment shifts the focus from literal tasks to high-level outcomes and the decisions they serve.
    • Expert Depth forces the model to use raw source material, professional methodologies, and specific technical instruments.
    • Range Integration identifies relevant cross-disciplinary techniques by translating nouns into functional verbs.
    • External Validation defines outside judges like test suites or held-out datasets to verify accuracy without self-grading.
    • Adversarial Red-Teaming incorporates roles specifically designed to challenge assumptions and break the result for better reliability.

    How it works

    1. Restate the task to confirm the core objective and intended use of the final result.
    2. Apply ambition axes by selecting the most relevant path: Altitude (outcome-driven), Depth (professional rigor), or Range (cross-field techniques).
    3. Assemble the framework including stretch goals, raw data requirements, and a crew of functional roles.
    4. Generate the upgrade providing a ready-to-paste prompt, a list of technical prerequisites, and a risk assessment.

    Frameworks & tools

    This skill is based on the ambition framework by Felix Rieseberg. It works best when the user can provide access to raw data, protocol analyzers, statistical simulators, and validation environments.

    Why this beats prompting it yourself

    Most manual prompts suffer from "template-fill" syndrome, leading to generic summaries. This skill systematically forces the model to ignore digested summaries in favor of raw data and professional-grade instrumentation. It replaces vague requests for "better quality" with concrete instructions for outside validation and adversarial testing.

    Use cases

    • Strategic Analysis: Transform a simple data request into a decision-support system that names urgent business tradeoffs.
    • Technical Audits: Upgrade a code review prompt to function like a forensic analyst checking raw logs against specific security protocols.
    • Product Strategy: Shift a feature list into a causal story that challenges internal churn assumptions using held-out datasets.

    Known limitations

    Does not work for quick factual lookups or minor text edits. Requires the user to provide or have access to the raw data and technical tools requested in the prerequisites.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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