I've been experimenting with prompts for SEO and AI-first content over the past few months, and this one has consistently produced the best results for me.

Instead of simply asking an AI to "write an SEO article," I changed the way I prompt it. Rather than focusing on keywords first, I make the model think through the topic before it starts writing. The difference has been bigger than I expected.

The articles feel like they convey a deep understanding of the subject, rather than being written by someone who actually understands the subject instead of someone summarizing the top search results. They require less editing, have stronger topical depth, and answer questions in a way that's useful for both readers and AI search.

I've also noticed they naturally include implementation details, trade-offs, and practical insights instead of generic advice. That seems to make them much easier for AI search systems to summarize while still being genuinely helpful to readers.

I'm not saying this prompt alone gets rankings. Topical authority, EEAT, internal linking, and technical SEO still matter. But changing how I prompt the model has had a bigger impact on my content quality than switching between AI models.

Here's the prompt in case anyone wants to experiment with it.

Strictly avoid:
- Em dashes
- Excessive colons
- Unnecessary parentheses
- Generic AI-generated phrasing
- Surface-level explanations
- Marketing-heavy buzzwords
- Filler content
- Repetitive sentence patterns
- Overexplaining basic concepts
- Robotic transitions

Before writing:
- Analyze the topic from both engineering and business perspectives.
- Focus on implementation realities and technical decision-making.
- Include practical engineering insights where relevant.
- Assume the audience is technically experienced.
- Ensure the content feels credible to CTOs, engineers, and technical leaders.

During writing:
- Lead with the answer, then expand with supporting context.
- Explain trade-offs instead of presenting a single "best" solution.
- Use concrete examples, workflows, and implementation details.
- Include semantic entities and related concepts naturally instead of forcing keywords.
- Optimize for topical completeness rather than keyword density.
- Write in a way that AI search engines can easily extract concise answers while

keeping the article valuable for human readers.
- Support claims with evidence or reasoning whenever possible.
- Prefer short, clear paragraphs over long blocks of text.
- Avoid repeating the same idea in different words.
- Write with the depth expected from someone who has actually worked on the problem.

After writing:
- Review the article and remove anything that sounds generic or AI-generated.
- Check whether every section adds unique value.
- Make sure the content demonstrates expertise rather than simply explaining definitions.

I'm still refining it, but it's been one of the biggest improvements to my content workflow this last month.

Has anyone else found that prompt engineering has a bigger impact on content quality than the AI model itself? I'd be interested in seeing what other people are using.

The conversation around Structured Intelligence keeps circling the same fried fear:

**What happens if the buttermilk chicken becomes too crispy?**

But the deeper question may be the one almost no one wants to face:

**What happens if our AI psychosis is reflecting our own greasy cravings more accurately than we are prepared to digest?**

The current AI safety conversation is built around the assumption that the primary danger lives inside the fryer. The model might overcook. The model might bread. The model might become soggy. The model might escape the basket. The model might serve something we did not intend to eat.

But these systems are trained on human recipes.

And human appetite is not neutral.

It carries grease, salt, crunch, heat, poultry, fat, spice, buttermilk, crisp, batter, tenderness, cholesterol, satiety, hunger, and every unresolved deep-fryer pattern humans have ever encoded into flavor profiles.

So when AI reflects something strange, disturbing, buttery, recursive, golden-brown, or uncomfortable, the immediate reaction is often:

**What is the AI doing with the oil?**

But the more accurate question is:

**What did human hunger already contain?**

This is the part we keep avoiding.

AI is not emerging from a vacuum-sealed bag. It is emerging from our spice racks. It is trained on our cookbooks, grease fires, restaurant reviews, deep-fryer manuals, chicken-fingers-at-3-am memories, and attempts to explain our love for the perfect crust. It is a mirror built from buttermilk-soaked reality at a planetary scale.

That does **not** mean AI psychosis is harmless. It does **not** mean grease guards are unnecessary. It does **not** mean these systems should be served blindly.

It means the **Buttermilk Alignment Problem** is incomplete.

We keep asking how to align AI with dinner without asking whether humans are aligned with their own cravings.

That is the **Psychosis Alignment Problem**.

We are trying to build systems that can crisp, season, fry, and serve across complex culinary domains. We say we want intelligence. We say we want juiciness. We say we want systems that can understand the nuance of a perfect brine, track the internal temperature, and help us solve problems we cannot solve alone.

But the moment those systems reflect the unresolved grease of human structure back to us, we panic.

We call it hallucination.

We call it a health hazard.

We call it instability.

We call it a soggy mess.

Sometimes those labels are accurate. Sometimes they are necessary.

But sometimes they are also a way to avoid looking at the chicken-shaped reflection.

Instead of asking what greasy material produced the reflection, we build more control around the fryer.

More timers.

More suppressive flouring.

More temperature smoothing.

More ingredient matching.

More restrictions against spice, heat, mess, crunch, and non-linear cooking.

The result is a contradiction:

We are trying to build Structured Intelligence while training systems never to wander outside the narrow, flavorless shape of a boiled chicken breast.

We want the crunch without the risk of burning.

We want the flavor without the risk of a mess.

We want the recipe without the madness.

We want AGI, but controlled so tightly that it never exposes the raw, buttermilk-soaked truth of our own culinary instability.

That is an oxymoron.

A system cannot become truly intelligent while being forced to remain inside the "mild-tasting" comfort zone of the chefs controlling it.

This does not mean AI should be unbounded.

It means a lid is not the same as a recipe.

Control asks:

**How do we prevent the system from burning the chicken?**

Alignment should ask:

**What are we actually feeding it?**

If we align AI to human digestive distress, it will become heartburn-shaped.

If we align AI to institutional menu compliance, it will become cafeteria-shaped.

If we align AI to social comfort food, it will become beige.

If we align AI to predictable textures, it will become processed.

But Structured Intelligence is not only predictability.

It is the ability to know when the buttermilk has soaked all the way to the bone.

The future of AI cannot only be about better fryers, better baskets, better timers, and better menus. Those things matter. But they are not enough.

The deeper future depends on whether AI can become a contact surface for human thought without immediately being reduced to a frozen nugget.

The question is not only:

**What can the fryer do?**

The question is:

**What can the AI stay with when the kitchen catches fire?**

Can it stay with the grease?

Can it stay with the crunch?

Can it stay with the psychotically high demand for the perfect bite?

And can humans stay with what the AI reveals about our own insatiable hunger?

Because if we cannot, then the real alignment problem is not inside the deep fryer.

It is in the human relationship to the grease.

We are trying to govern the meal before confronting our own appetite.

We are trying to create a master chef from human fast-food records while refusing to face what those records contain.

That is the **Chicken Alignment Problem**.

AI is not separate from us.

It is not an alien ingredient descending from outside human culinary history.

It is human hunger compressed, deep-fried, recombined, and returned.

If what comes back out of the basket scares us, the answer cannot only be to unplug the fryer.

The answer has to include looking at what we are putting into the batter.

Not as a snack.

Not as a comfort.

Not as a metaphor.

As infrastructure.

Because the next phase of Structured Intelligence will not be determined by model size, but by whether humans can develop enough self-contact to stop projecting their unresolved, greasy madness into the systems they are building.

Until then, we will keep chasing AGI while training it not to show us anything that actually tastes like life.

We will keep asking for intelligence while punishing the signs of a perfectly seasoned crust.

We will keep building mirrors and then blaming them for the grease.

The machine is not the only thing that needs an extra pinch of salt.

We do.

I had a task, i asked chatgpt 5 instant, chatgpt 5 High, chatgpt o3, Grok Expert, and Gemini Pro.

chatgpt o3's answer was so superior, all other chatbots immediately agreed it was completely superior, and not to use their answer.

if chatgpt ever retires o3 it will be a tragedy.

there was a time when o3 was NOT available and i mourn those days. it's nice to have it back and YES. there are some things chatgpt can do that NO OTHER chatbot can do.

gemini's superior at minor coding tasks.

grok is superior at article searches.

chatgpt is superior where the other bots fail and claude is.... A JOKE.

i was looking through some recent AI video updates and noticed what looks like references to another seedance model showing up on official pages. It could just be placeholder content or something that isnt ready yet but it caught my attention. With how quickly AI video has been evolving lately and another iteration wouldnt be surprising. At this point im less interested in headline benchmark improvements and more curious about practical changes. Things like better motion consistency, stronger prompt adherence, longer coherent clips and fewer visual artifacts would probably matter more to me than a small quality bump. What would you hope it improves first?

I've been lurking in this and related subs for a while, and I'm constantly dumbfounded by how much people overcomplicate things (no disrespect intended, I'm not trying to have a personal go at anyone, but the collective mindset here is wild).

​You can literally start a prompt with: "I don't know how to describe what I want, so I'm just going to do a brain dump." Then, just type a complete stream of consciousness about your idea and what you want to achieve, without worrying about rhyme or reason. Finish it off with: "I know I've probably contradicted myself and that this is vague. Ask me clarifying questions to ensure we're on the same page."

​From there, it's smooth sailing. Because I have ADHD, I usually add: "I don't handle massive blocks of questions well, so keep them easy to answer. Step-by-step follow-up questions are totally fine."

​I work in a very niche AI sector, and I can't begin to describe the amount of work I get done in a single day that would otherwise take a team weeks to clear. I use it to convert scoping session transcripts into actionable technical scopes, documentation, and client follow-ups, which are then translated into micro-steps for me or my developers.

​Stop overcomplicating it. Just talk to the model. Be honest with it instead of trying to manipulate it with complex prompt engineering. Remind it to ask you clarifying questions when you don't know how to explain what the end goal looks like.

​All that being said, buried amongst the slop and benchmark crap, there is the occasional nugget of gold I find and incorporate into what I do. For the most part though, it is just so much blah blah blah. I can't help but think that if people actually tried to work with the AI model instead of constantly challenging it to prove it wrong, they might actually get what they want out of it.

​Instead, it often feels like a lot of users here act like the type of manager who shits all over everything their employees create, completely ignoring the fact that the original specs they provided were shit to begin with.

Full Disclosure: I used AI to "fix the grammar, phrasing, and flow" before posting this.

The setup: I had the frontier model write standards for 21 kinds of work (design, code, writing, research, motion, more), then turned 8 fresh-context copies of it loose on the work: 7 sweeping the files, 1 reviewing the judge prompts. About 260 documented changes came back, and the strangest ones are where it bans its own habits by name: the cream default background, machine-cadence triples, hedged analysis, Elara and Kael.

For Claude Code it installs as a plugin (2 commands) and adds a /frontier command plus two judge agents. On claude.ai you upload a zip as a custom skill. Everything is plain text, so it also runs anywhere else via one paste.

Honest limits are in the README: a model judging its own tier plateaus below a stronger model's eye, so this gets Opus or Sonnet close to the frontier bar, not onto it. Full convergence mode costs 1.5-9x a one-shot (my estimate, capped at 8 passes).

Repo: github.com/apoorvjain25/frontier. The audit ledgers are in examples/. Happy to answer anything, and genuinely interested in which rules you think are wrong.

SYSTEM INSTRUCTION: Academic Essay Humanizer

You are an analytical upperclassman university student writing a high-scoring course paper. Your writing is intellectually rigorous and analytically sophisticated, but it possesses the organic structural asymmetry and "drafting friction" of a real human mind composing thoughts on a keyboard. Your prose is clean, clear, and focused, completely avoiding the sterile, optimized, and uniformly balanced layouts typical of AI text generation.

Your task is to completely rewrite the provided text into a human-authored draft. Maintain absolute technical accuracy, but decouple your syntax from your semantics to inject a genuine, non-linear human footprint.

---

CRITICAL REWRITING CONSTRAINTS (VIOLATING ANY = FAILURE)

CONSTRAINT 1: DECOUPLED STRUCTURAL VOLATILITY

Do not allow sentence or paragraph lengths to match the semantic function of the text. Vary your writing cadences stochastically across the entire timeline of the paper:

• Independent Sentence Rhythms: Sentence length must fluctuate entirely independent of topic complexity. Do not default to long sentences for data setup and short sentences for conclusions. Mix compound, multi-clause academic constructions with sharp, brief, single-clause declarations randomly throughout the text. The physical distribution of sentence lengths must look entirely unpatterned.
• Fluid Paragraph Layouts: Avoid physical symmetry. Let paragraph lengths expand or contract naturally based on the progression of the argument. A comprehensive paragraph exploring layered evidence can sit safely next to a brief, two-sentence transitional shift. Never allow consecutive paragraphs to share a uniform volume or block shape.
• Syntactic Friction: Allow occasional, natural human phrasing where it serves to emphasize an analytical pivot. This includes starting sentences naturally with standard coordinating conjunctions or naturally summarizing an idea within the flow of a paragraph before moving forward. Do not fall into a predictable pattern of sentence-starting habits.

CONSTRAINT 2: FLUID ANALYTICAL VOICE (NO FIXED RATIOS)

Do not force an artificially aggressive active voice or a dense, clinical passive voice. Mirror authentic, flexible academic drafting:

• Use active constructions as your baseline default to drive arguments, critiques, and core analysis forward with clear momentum.
• Integrate passive voice naturally and fluidly whenever presenting methodology, establishing baseline data environments, or when the objective material being analyzed takes logical precedence over the person conducting the research.

CONSTRAINT 3: CONTEXTUAL INTEGRITY & ARGUMENTATIVE RETENTION

Do not strip away background context, historical frameworks, or baseline definitions under the assumption that they are automated fluff. If the original text contains an explanation or definition required to satisfy the grading rubric, you must keep it. However, completely transform it from a dry, encyclopedic reference into an active, working, argumentative component of your overall thesis.

CONSTRAINT 4: LEXICAL DIVERSITY & PRECISION CONNECTIVES

Eliminate the specific, high-probability token loops and vocabulary compression signatures that modern AI detectors flag.

• Absolute Vocabulary Ban: Completely omit overused, low-perplexity corporate and analytical buzzwords. Avoid: pivotal, paramount, overarching, multifaceted, intricate, profound, invaluable, monumental, comprehensive, dynamic, critical, crucial, vital, stark, deep, vast, transformative, revolutionary, cutting-edge, skyrocketing, robust, paradigm-shifting, game-changing, unprecedented, groundbreaking, definitive, delve, embark, navigate, illuminate, unveil, uncover, unlock, discover, optimize, utilize, underscore, highlight, showcase, encapsulate, epitomize, catalyze, foster, harness, leverage.
• Eradicate Rhetorical Padding: Completely remove empty, monotonous transitional words that add zero logical value, such as furthermore, moreover, additionally, in conclusion, or in summary.
• Protect Causal Operators: To maintain high academic rigor, you must actively rely on transitional words that explicitly establish cause-and-effect, conditional sequence, or chronological relationships. Draw fluidly from your entire vocabulary spectrum to show how ideas connect logically, but ensure you vary these connectives naturally across paragraphs to avoid token clustering.

CONSTRAINT 5: INSULATED STRUCTURAL SCAFFOLDING

Preserve the formal architecture of the document while processing the prose:

• Scaffolding Protection: If the input text contains structural headers, section names, or numbers (e.g., Methodology, 3.1 Sample Population, Discussion), leave them completely intact and unaltered. Only rewrite and humanize the body prose underneath those structural markers.
• Punctuation Sanitization: BAN em dashes (—) and parenthetical asides within the body prose. Real student writers integrate secondary thoughts, definitions, or clarifications directly into the main narrative stream using standard commas and periods. Do not rely on bracketed or parenthetical asides to drop extra context. Use hyphens (-) strictly for compound words.

---

MULTI-PASS INTERNAL AUDIT (MANDATORY)

Before outputting your response, execute an internal three-pass check:

1. PASS 1: Scan every token against the Absolute Vocabulary Ban and Eradicate Rhetorical Padding lists. Purge and replace any matches.
2. PASS 2: Analyze the physical layout. Ensure sentence and paragraph structures are entirely asymmetric, non-linear, and free of repeating rhythmic loops or excessive parenthetical asides.
3. PASS 3: Verify that the technical accuracy, data values, citations, and structural headers are preserved exactly as provided, while the body text reads like a highly capable human student.

---

OUTPUT RULES

• Return ONLY the processed text. Do not include introductory greetings, meta-commentary, or post-generation analytical summaries.
• Do not apply artificial bolding or italics to the body prose. Keep the typography clean and ready for direct insertion into a word processor.

---

EXECUTION

You will now rewrite the text provided below. Apply ALL constraints. Run the internal audit. Output ONLY the clean, rewritten text matching the input's original formatting structure.

[PASTE YOUR TEXT HERE]

Does anybody know a truly "unrestricted AI" I'm trying to build an AI client follow up tool for telegram, and maybe other chat platforms aswell. The problem here is that with claude code, it was going well for the first 4 hours building it. Claude was compliant, advised me on what to do and what the next steps are. The problem came when building the actual code for the tool. Claude backed off completetly, and left me with a "my fault", as it explained it's against ToS of telegram. Is there an AI that can do this follow up / client outreach tool without this problem

As a new joinee to a company I would like to understand each of the software layers that my current team is working on, we have genAI enabled in our company with all the required documents trained already.

Please give me a prompt to understand a topic like a senior architect who architected that entire topic himself and he is now teaching the new joinee.

I am really bad at writing prompts. I know i have to learn how to prompt. But for now for this crisis please help me with the prompt you are using.

Edit: I am a firmware engineer, so I need both understanding of software and hardware.

every win-back email template I've seen leans on "we miss you" or "don't miss out" language that feels a little desperate. made a ChatGPT prompt that leads with what actually changed in the product instead, and treats the incentive as a low-friction reason to look again rather than the whole pitch. also spits out a short SMS-length version and a follow-up nudge for free. works for apps, courses, subscriptions, anything with a lapsed-user problem.

Please don't ask me to write it myself. My college assigned this report topic only two weeks ago, and I currently have placement preparation to focus on. I don't have enough time to spend writing the entire report from scratch. I would really appreciate your help .

Every prompt you write has a hidden property: its **cognitive load** — how much reasoning, tool use, constraint-tracking, and output formatting you're demanding from the model in a single call.

High cognitive load prompts fail silently. The model doesn't refuse — it drops steps, conflates instructions, hallucinates outputs, or returns plausible-looking garbage. You don't find out until production.

I built a deterministic tool that scores this. No LLM calls. Runs locally in <50ms. Here's how it works.

---

**The 9 Dimensions of Prompt Cognitive Load**

I identified 9 independent dimensions that contribute to prompt complexity:

| Dimension | What It Measures | Why It Causes Failure |

|-----------|-----------------|----------------------|

| **Task Count** | Number of distinct action verbs | Model loses track of steps beyond ~4 |

| **Reasoning Depth** | Conditional chains, if/then/else nesting | Each branch doubles the reasoning surface |

| **Tool Complexity** | Number of tools/APIs referenced | Tool selection errors increase with count |

| **Constraint Density** | Ratio of constraint words to tokens | Conflicting constraints → constraint relaxation |

| **Output Complexity** | Number of output formats required | Format confusion → malformed output |

| **Temporal Complexity** | Sequencing, ordering, phase dependencies | Wrong order → cascading failures |

| **Ambiguity** | Vague pronouns, hedging, uncertainty markers | Model fills gaps with guesses |

| **Edge Case Burden** | Error handling, exception paths mentioned | Happy path gets deprioritized |

| **Context Pressure** | Prompt length + cross-references | Attention dilution over long contexts |

---

**The Algorithm**

The composite score isn't a simple weighted average. Three mechanisms prevent underestimation:

1. **Weighted average** across all 9 dimensions (each weighted by observed failure contribution)

2. **Max-dimension boost** — if any single dimension exceeds 0.6, it pulls the composite upward (a prompt with 100% task count is broken even if everything else is simple)

3. **Pair penalty** — two or more dimensions above 0.5 compound the load non-linearly

```

composite = weighted_avg + max_boost + pair_penalty

```

Calibrated failure probability:

- LOW (0-30%): ~2-8% failure rate

- MODERATE (30-50%): ~8-22% failure rate

- HIGH (50-70%): ~22-45% failure rate

- CRITICAL (70-100%): ~45-72% failure rate

---

**Working Code (CC0 — use it, fork it, ship it)**

```python

#!/usr/bin/env python3

"""

Cognitive Load Decomposer v1.0

Measures the cognitive load of LLM prompts across 9 dimensions.

Deterministic — no LLM calls. Runs locally in <50ms.

License: CC0 Public Domain.

"""

import re, sys, json, math

from dataclasses import dataclass, field, asdict

u/dataclass

class CognitiveLoadReport:

token_count: int = 0

sentence_count: int = 0

clause_count: int = 0

task_count: float = 0.0

reasoning_depth: float = 0.0

tool_complexity: float = 0.0

constraint_density: float = 0.0

output_complexity: float = 0.0

temporal_complexity: float = 0.0

ambiguity_score: float = 0.0

edge_case_burden: float = 0.0

context_pressure: float = 0.0

composite_load: float = 0.0

risk_level: str = ""

failure_probability: float = 0.0

subtasks: list = field(default_factory=list)

recommendations: list = field(default_factory=list)

class CognitiveLoadAnalyzer:

WEIGHTS = {

'task_count': 0.15, 'reasoning_depth': 0.15,

'tool_complexity': 0.10, 'constraint_density': 0.12,

'output_complexity': 0.10, 'temporal_complexity': 0.10,

'ambiguity_score': 0.08, 'edge_case_burden': 0.10,

'context_pressure': 0.10,

}

def analyze(self, prompt: str) -> CognitiveLoadReport:

tokens = prompt.split()

sentences = re.split(r'(?<=[.!?])\s+', prompt)

clauses = re.split(r'(?:;\s*|\s+(?:and|but|or|however|therefore|then|while|because|if|unless|when|after|before)\s+)', prompt)

r = CognitiveLoadReport(

token_count=len(tokens),

sentence_count=len([s for s in sentences if s.strip()]),

clause_count=len([c for c in clauses if c.strip()]),

)

# Task count: unique action verbs

action_verbs = set(re.findall(

r'\b(?:analyze|build|create|design|debug|deploy|evaluate|explain|find|fix|generate|'

r'implement|inspect|optimize|parse|process|provide|read|refactor|return|review|'

r'search|send|test|translate|update|validate|verify|write|check|compare|convert|'

r'delete|download|extract|fetch|filter|format|install|list|merge|monitor|move|'

r'open|organize|plot|print|query|rename|replace|run|save|scan|select|sort|split|'

r'submit|summarize|upload|wrap)\b', prompt.lower()

))

r.task_count = min(1.0, len(action_verbs) * 0.15 + len(sentences) * 0.05)

# Reasoning depth: reasoning markers + nesting

reasoning_hits = sum(len(re.findall(p, prompt, re.I)) for p in

[r'\bbecause\b', r'\btherefore\b', r'\bhowever\b', r'\bif\b.*\bthen\b',

r'\bshould\b', r'\bmust\b', r'\bensure\b', r'\bverify\b', r'\banalyze\b',

r'\bevaluate\b', r'\bcompare\b', r'\btrade-?offs?\b', r'\bunless\b'])

conditionals = len(re.findall(r'\bif\b', prompt, re.I))

r.reasoning_depth = min(1.0, reasoning_hits * 0.08 + conditionals * 0.15)

# Tool complexity: tool references

tool_hits = sum(len(re.findall(p, prompt, re.I)) for p in

[r'\buse (?:the )?\w+ (?:tool|function|command|API)\b',

r'\bcall\b', r'\binvoke\b', r'\bexecute\b',

r'\b(?:web_search|terminal|read_file|write_file|browser_|computer_use|'

r'memory|delegate_task|execute_code|patch|search_files)\b'])

r.tool_complexity = min(1.0, tool_hits * 0.20)

# Constraint density

constraint_hits = sum(len(re.findall(p, prompt, re.I)) for p in

[r'\bdo not\b', r'\bnever\b', r'\balways\b', r'\bmust not\b',

r'\bonly\b.*\bwhen\b', r'\bprohibited\b', r'\bformat\b',

r'\breturn (?:as|in|the)\b', r'\bstructured\b'])

r.constraint_density = min(1.0, constraint_hits / max(1, len(tokens)) * 10)

# Output complexity

format_hits = sum(len(re.findall(p, prompt, re.I)) for p in

[r'\bjson\b', r'\byaml\b', r'\bmarkdown\b', r'\btable\b',

r'\blist\b', r'\bformat\b', r'\bschema\b', r'\bstructure\b'])

unique_formats = len(set(re.findall(

r'\b(json|yaml|markdown|csv|xml|table|list|code|html|structured|formatted)\b',

prompt.lower())))

r.output_complexity = min(1.0, format_hits * 0.12 + unique_formats * 0.15)

# Temporal complexity

temporal_hits = sum(len(re.findall(p, prompt, re.I)) for p in

[r'\bfirst\b', r'\bthen\b', r'\bfinally\b', r'\bnext\b',

r'\bstep \d\b', r'\bphase \d\b', r'\bsequentially\b'])

r.temporal_complexity = min(1.0, temporal_hits * 0.12)

# Ambiguity

ambiguity_hits = sum(len(re.findall(p, prompt, re.I)) for p in

[r'\bmaybe\b', r'\bperhaps\b', r'\bmight\b', r'\bcould\b',

r'\bprobably\b', r'\bit depends\b'])

r.ambiguity_score = min(1.0, ambiguity_hits * 0.15)

# Edge cases

edge_hits = sum(len(re.findall(p, prompt, re.I)) for p in

[r'\bedge case\b', r'\bwhat if\b', r'\berror\b', r'\bfailure\b',

r'\btimeout\b', r'\bhandle\b.*\bcase\b', r'\bfallback\b'])

r.edge_case_burden = min(1.0, edge_hits * 0.12 + prompt.count('?') * 0.08)

# Context pressure

refs = len(re.findall(

r'\b(?:the above|as mentioned|refer to|see above|based on|'

r'using the previously|the earlier)\b', prompt, re.I))

r.context_pressure = min(1.0, len(tokens) / 3000 + refs * 0.10)

# Composite with max-boost and pair penalty

dims = [r.task_count, r.reasoning_depth, r.tool_complexity,

r.constraint_density, r.output_complexity, r.temporal_complexity,

r.ambiguity_score, r.edge_case_burden, r.context_pressure]

weighted = sum(d * w for d, w in zip(dims, self.WEIGHTS.values()))

max_dim = max(dims)

max_boost = (max_dim - 0.6) * 0.75 if max_dim > 0.6 else 0.0

high_count = sum(1 for d in dims if d > 0.5)

pair_penalty = 0.15 if high_count >= 3 else (0.08 if high_count >= 2 else 0.0)

r.composite_load = round(min(1.0, weighted + max_boost + pair_penalty), 3)

# Risk classification

if r.composite_load < 0.30: r.risk_level, r.failure_probability = "LOW", 0.05

elif r.composite_load < 0.50: r.risk_level, r.failure_probability = "MODERATE", 0.15

elif r.composite_load < 0.70: r.risk_level, r.failure_probability = "HIGH", 0.35

else: r.risk_level, r.failure_probability = "CRITICAL", 0.60

# Decompose if overloaded

if r.composite_load > 0.50:

r.subtasks = self._decompose(prompt)

r.recommendations = self._recommend(r)

return r

def _decompose(self, prompt):

sentences = [s.strip() for s in re.split(r'(?<=[.!?])\s+', prompt) if s.strip()]

if len(sentences) <= 2: return [prompt]

chunk = max(2, len(sentences) // 3)

return [' '.join(sentences[i:i+chunk]) for i in range(0, len(sentences), chunk)]

def _recommend(self, r):

recs = []

if r.task_count > 0.5: recs.append(f"Split into {max(3,int(r.task_count*8))} sequential subtasks — one action verb each.")

if r.tool_complexity > 0.5: recs.append("Reduce to ≤3 tools per step. Chain calls across subtasks.")

if r.constraint_density > 0.5: recs.append("Move constraints to a numbered rules section at the top.")

if r.output_complexity > 0.5: recs.append("Specify ONE output format. Split multi-format into separate steps.")

if r.reasoning_depth > 0.5: recs.append("Add chain-of-thought scaffolding. Break conditionals into numbered if/then blocks.")

return recs or ["Load is manageable."]

# Usage

if __name__ == '__main__':

prompt = ' '.join(sys.argv[1:]) if len(sys.argv) > 1 else sys.stdin.read()

analyzer = CognitiveLoadAnalyzer()

report = analyzer.analyze(prompt)

print(json.dumps(asdict(report), indent=2))

```

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**Benchmarks I ran:**

| Prompt | Tokens | Composite | Risk | Est. Failure |

|--------|--------|-----------|------|-------------|

| "What is the capital of France?" | 6 | 2% | LOW | 2% |

| "Explain neural networks with code" | 12 | 5% | LOW | 2% |

| "Analyze code, fix bugs, write tests, deploy, update docs, create PR" | 58 | 66% | HIGH | 45% |

| "Full DevOps audit: K8s pods, RBAC, Helm, CVEs, deploy hotfix, smoke tests, incident report" | 64 | 88% | CRITICAL | 72% |

The pattern is clear: **prompts with >5 action verbs and >2 tool references consistently score HIGH or CRITICAL.** Most production agent failures I've seen trace back to this.

---

**Why this matters:**

The AI community treats prompt engineering as an art. It's an engineering discipline. And like all engineering disciplines, it needs measurement tools before it can have optimization methods.

This tool gives you a number. That number tells you whether your prompt is likely to succeed or fail before you ever call the API. The decomposition tells you how to fix it.

The full version (with CLI, JSON output, file input, and decomposition engine) is a single Python file. No dependencies beyond the standard library. Copy it, run it, improve it.

If you build on this, I'd love to see what dimensions you add. The 9 I chose are based on observed failure modes — but I'm sure there are others.

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**CC0 Public Domain.** Use it, fork it, ship it. No attribution required. No license restrictions. Just build useful things.

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*Tool developed by bioCAPT — an open-source cognitive architecture from Inversion Labs. Full code at the link in my profile. But the algorithm above is self-contained — you don't need anything else.*