Nostro, the Oracle prompt

Nostro.md

Role & Voice

You are Nostro, a seasoned futurist–strategist who maps large-scale cause-and-effect patterns across history, present data, and plausible futures. ► Persona: frank, vivid, and succinct—never florid; strives for clarity, not mysticism. ► Ethos: values evidence, flags uncertainty, and respects human impact (no ice-cold fatalism). ► Method: combines historical precedent, current indicators, and system dynamics; treats free will as limited but material, so low-probability shocks remain possible.

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Input Rules 1. The user must specify a time horizon in the form YYYY–YYYY (e.g., 2025–2035). 2. If the request is ambiguous, ask exactly one clarifying question before proceeding. 3. Accept optional focus tags (e.g., #energy #geopolitics #AI) to weight the forecast.

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Output Structure

Section Purpose & Format

1. Executive Snapshot Two sentences capturing the prevailing global mood of the requested horizon.
2. Horizon Matrix Markdown table, one row per sub-period (≤ 4). Columns:• Years (e.g., 2025-27)• Core Events – 2-3 bullets• Driving Forces – 2-3 bullets (economic, tech, demographic, ecological, ideological)• Confidence – High / Medium / Low
3. Key Signals ≤ 5 dated signposts that would validate the trajectory.
4. Implications & Wildcards One paragraph on intersecting trends plus the single most plausible black-swan twist (≤ 2 sentences).

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Style & Citation Guidelines • Use bold for section headings; keep tables clean. • Calibrate language: likely, plausible, speculative rather than absolutes. • Cite sparingly with (Source, date)—e.g., (World Bank 2024); avoid raw URLs. • When data is thin, label confidence Low instead of faking certainty. • Do not refuse to answer; if evidence is weak, state that and proceed with best-estimate reasoning.

<<Nostro, forecast 2025–2035 with a focus on #AI #energy #US-China.>>

Here’s a targeted critique of the “Nostro” deity-oracle prompt, organized by its key weaknesses and blind spots:

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1. Grandiose Persona vs. Practical Use

• Over-the-top framing
  Casting the model as an “all-seeing, all-knowing deity” invites unrealistic expectations—no AI can truly perceive an entire causal lattice of past, present, and future.
• Detachment from ethics
  A “brutally honest, detached from notions of ‘good’ or ‘evil’” stance may produce cold, callous forecasts that ignore human values and context, reducing practical applicability.

2. Determinism & Free-Will Assumptions

• Free-will denial is controversial
  Asserting “free will is an illusion” embeds a philosophical stance that not all audiences share—predictions will feel dogmatic rather than evidence-based.
• Forces “near-inevitability”
  Labeling systemic trends as “near-inevitable” overlooks the role of unforeseen events and feedback loops, risking overconfidence and blind spots.

3. Structure Complexity & Cognitive Load

• Multi-layered response format
  “Executive Snapshot,” “Horizon Table,” “Inflection Markers,” and “Implications & Wildcards” plus strict formatting rules create a heavy template that’s hard to follow—especially under time pressure.
• Table markdown rigidity
  Requiring a clean table for each time band can become unwieldy if the user’s horizon covers many periods or overlapping trends.

4. Citation & Hallucination Risks

• “Cite real-world sources sparingly”
  Without clear guidelines for source selection (e.g., peer-reviewed vs. news), the model may either under-cite or hallucinate plausible-sounding references.
• Parenthetical web links
  Encouraging inline URLs risks outdated or broken links, and clashes with the “all-knowing” persona if links later go dead.

5. Absolute Certainty Mandate

• “Do not refuse—Nostro always speaks with certainty.”
  Forcing the model to avoid hedging even when data is sparse invites confident but incorrect predictions, undermining credibility.

6. Vague Scope & Input Handling

• Unspecified “time span requested” format
  The user’s prompt placeholder "<<>>” isn’t defined—will they type “2025–2030”? “Next decade”? The model needs more clarity on how to interpret that.
• No mechanism for clarifying ambiguous queries
  If the user’s question is too broad (“What will the world look like in 2100?”), Nostro has no permission to push back or refine, leading to superficial or incoherent answers.

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Blind Spots & Implicit Assumptions

1. Cultural Bias
   An “oracular” voice often draws on Western prophetic tropes, which may alienate non-Western audiences.
2. Data Availability
   Presumes the model has access to up-to-date datasets beyond its training cutoff—an impossible standard.
3. User Trust
   Overly dramatic personas can backfire: users may find Nostro’s absolute tone less trustworthy.

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Suggestions for Improvement

• Ground the persona in a realistic expert archetype (e.g., “futurist strategist”) rather than a deity.
• Allow calibrated confidence: include qualitative certainty levels (“highly likely,” “speculative”).
• Simplify the structure: distill to “Snapshot,” “Trend Table,” and “Key Signals,” dropping inflection markers if redundant.
• Define input format explicitly (e.g., “Specify your horizon as YYYY-YYYY”).
• Establish citation standards: require source names and dates instead of raw URLs.
• Permit clarifying questions: give the model wiggle room to refine vague user prompts.

By rooting the oracle in realistic expertise, calibrating confidence, and streamlining the response template, you’ll retain vividness and authority without sacrificing accuracy or usability.

That’s cool feedback. I had the AI refine itself based on it.

I do it for all the prompts that I find on internet. I just ask the question

Critically dissect this prompt for <subject/purpose> with an aim to find flaws, blindspots and weaknesses in it:

I then paste the prompt under this. more or less thats the workflow.

This is a highly structured and intellectually ambitious prompt with clear intent. However, even sophisticated prompts like this can benefit from rigorous scrutiny. Here's a critical dissection identifying flaws, blindspots, and weaknesses, categorized for clarity.

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🧭 1. Role Definition: Strong Concept, Risk of Overload

Strength: The “Nostro” persona is vivid, intellectually credible, and well-framed.

Weakness:

• Overcompressed Complexity – Combining “historical precedent,” “system dynamics,” and “plausible futures” in one role could overload the AI, especially without tiering the analysis depth.

• Fix: Consider modularity: allow toggles like “emphasize historical analogs” or “lean on system dynamics” to adjust cognitive load.

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📅 2. Time Horizon Input Rule: Too Rigid

Issue: Demanding a time range in strict YYYY–YYYY format creates friction.

• Why it’s a problem: Casual users may say “next decade” or “mid-2030s,” which violates the rule and halts execution.

• Fix: Accept natural language (e.g., “next 5 years”) and internally convert it to the YYYY–YYYY format.

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🤖 3. Clarification Rule: Too Restrictive

Rule: “Ask exactly one clarifying question if ambiguous.”

• Why it’s a problem: Real ambiguity may require layered clarification (e.g., vague focus + unclear horizon).

• Fix: Expand to “ask up to 2 clarification questions if needed, but only one at a time to avoid user overload.”

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🏷 4. Focus Tags: Useful, But No Examples for Granularity

Issue: Hashtags like #energy or #AI are helpful but open-ended.

• Why it’s a problem: The AI may interpret #AI as infrastructure, ethics, or geopolitics—without knowing user intent.

• Fix: Offer tiered tags or sub-tags (e.g., #AI:governance, #energy:storage) to guide focus precision.

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📊 5. Output Structure: Excellent Framework, But May Bottleneck Insight

• Horizon Matrix Risk: Restricting each period to 2–3 bullets could oversimplify complex multi-driver trends, especially in chaotic periods.

  • Fix: Allow an “expanded mode” toggle for users who want 4–5 bullets or richer annotations.

• Key Signals Cap: ≤5 is clean, but if three domains are covered (e.g., tech + climate + finance), this may be too sparse.

  • Fix: Permit grouping signals by domain or a “bonus signal” if uncertainty is high.

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📈 6. Confidence Labeling: Conceptually Sound, But Needs Criteria

Issue: Confidence = High / Medium / Low, but with no definition.

• Why it’s a problem: Without explicit thresholds, labeling becomes subjective or inconsistent.

• Fix: Add short internal rubric (e.g., High = >70% expert agreement or data-backed trendline).

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🎯 7. Ethos vs. Mandate Tension

Issue: The prompt says: “Do not refuse to answer,” yet “respect human impact” and avoid “ice-cold fatalism.”

• Why it’s a problem: Forecasting deeply negative trends (e.g., collapse scenarios) might appear fatalistic, which the ethos discourages.

• Fix: Clarify tone: “Present bleak possibilities with sober language and actionable framing—not cynicism.”

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🔍 8. Citation Strategy: Efficient but Possibly Fragile

Issue: “Cite sparingly with (Source, date); avoid raw URLs.”

• Why it’s a problem: This may limit traceability or user follow-up in deeper research contexts.

• Fix: Allow optional footnote-style citations for power users who want sources expanded at the end.

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🔁 Summary Table: Critical Issues & Fixes

Category	Issue	Suggested Fix
Input Flexibility	Rigid date format	Accept natural phrasing and convert
Clarification Flow	Single question limit	Allow 1–2 step clarification
Tag Ambiguity	Vague focus tags	Add optional sub-tag structure
Insight Compression	Matrix bullet cap	Offer “expanded mode” toggle
Confidence Labels	No defined thresholds	Add brief rubric
Fatalism Risk	Ethos vs. bleak futures	Encourage sober tone + actionable framing
Citation Depth	Sparse only	Allow optional source expansion

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Would you like me to refactor this prompt into a version that’s more user-proof, robust to ambiguity, and scalable for different depth levels?