style-specification-building

name	style-specification-building
description	Use when translating completed linguistic analysis outputs into a unified style specification document, resolving conflicts between analysis findings, mapping quantitative metrics to actionable writing constraints, or producing an implementable voice profile from personality, stylometric, psycholinguistic, rhetorical, or archetype analysis results

Building the Style Specification

Overview

Translate all prior analysis outputs into a single, implementable style specification by mapping each finding to a concrete writing constraint, resolving conflicts between analyses, and producing both a numeric profile and a prose description usable as an LLM system prompt or human writing guide. The core principle: every constraint in the specification must trace to a specific analysis finding with a stated confidence level, and no analysis finding should appear in the specification without being translated into an actionable constraint. Raw analysis outputs are observations; the style specification is a set of instructions. This skill performs the translation.

Why this is the penultimate step: All upstream analyses (personality profiling, stylometric fingerprinting, psycholinguistic categorization, readability metrics, rhetorical structure, accommodation patterns, archetype classification, register variation, speech act distribution, audience patterns, and persona framing) produce findings in their own frameworks. Without a specification-building step, those findings remain siloed observations. The style specification resolves them into a single coherent document that the final step (voice prompt engineering or human style guide creation) can consume directly.

Design influences: Nielsen Norman Group's four dimensions of tone (formal/casual, serious/funny, respectful/irreverent, enthusiastic/matter-of-fact); the COSTAR prompt framework (Context, Objective, Style, Tone, Audience, Response); Mailchimp and Google voice-and-tone documentation patterns; meta-analytic evidence that LLMs follow 5-12 well-prioritized constraints more reliably than 30+ undifferentiated rules.

When to Use

• Multiple linguistic analyses have been completed and need consolidation into one voice profile
• Building an LLM system prompt that replicates a specific writing voice
• Creating a human-readable style guide from quantitative analysis outputs
• Need to resolve contradictions between different analyses (e.g., personality suggests formality but stylometrics show casual function-word patterns)
• Preparing the penultimate deliverable before final prompt engineering or style guide assembly
• Converting a set of analysis reports into actionable writing constraints

When NOT to use:

• Fewer than 3 upstream analyses are complete (see Insufficient Data Handling)
• No analysis has been performed yet (run analyses first; this skill consumes their outputs)
• Goal is to analyze text (this skill synthesizes existing analyses, not raw text)
• The specification is for a team or brand voice with multiple contributors (this produces a single-author voice specification)
• Analysis outputs are from different authors being merged (this maps one voice, not a composite)

digraph spec_decision {
    rankdir=TB;
    "3+ analyses\ncomplete?" [shape=diamond];
    "Single author\nvoice?" [shape=diamond];
    "Analyses cover\n3+ dimensions?" [shape=diamond];
    "Proceed with\nfull specification" [shape=box];
    "Minimal specification\nwith heavy caveats" [shape=box];
    "STOP: Run more\nanalyses first" [shape=box];
    "STOP: Not designed\nfor multi-author" [shape=box];

    "3+ analyses\ncomplete?" -> "Single author\nvoice?" [label="yes"];
    "3+ analyses\ncomplete?" -> "STOP: Run more\nanalyses first" [label="no"];
    "Single author\nvoice?" -> "Analyses cover\n3+ dimensions?" [label="yes"];
    "Single author\nvoice?" -> "STOP: Not designed\nfor multi-author" [label="no"];
    "Analyses cover\n3+ dimensions?" -> "Proceed with\nfull specification" [label="yes"];
    "Analyses cover\n3+ dimensions?" -> "Minimal specification\nwith heavy caveats" [label="no, redundant\ncoverage"];
}

Quick Reference: The Analysis-to-Constraint Mapping

Each upstream analysis maps to specific constraint categories. This table is the backbone of the specification.

Source Analysis	Constraint Category	What It Controls	Example Constraint
MDPI Archetype	Emotional valence boundaries	Permissible emotional range and arousal level	"Positive base, moderate arousal; aggressive emphasis acceptable but not dominant"
Big Five Openness	Vocabulary complexity level	Word sophistication, abstraction, variety	"Varied vocabulary, 6+ char words at 18-22% rate, abstract framing permitted"
Big Five Conscientiousness	Formality and structural rigor	Organization, discourse markers, precision	"Moderate-to-high structure; use discourse markers (however, therefore); organized paragraphs"
LIWC Dominant Dimensions	Linguistic register priorities	Which psychological registers to foreground	"Prioritize cognitive-process vocabulary (28% of total); social-process secondary (15%)"
Stylometric Fingerprint	Function-word patterns and sentence structure	Unconscious grammatical habits	"Article rate 6.2-7.1%; mean sentence length 16-20 words; semicolon usage 0.8-1.2 per 1000 words"
Readability / Lexical Diversity	Measurable complexity targets	Grade level, TTR, sentence/word metrics	"FK grade 11-13; MTLD 85-110; Coleman-Liau grade 12-14"
CAT/LSM Target Communities	Professional standards convergence	Which community norms to match	"Accommodate toward r/science norms (LSM 0.82); diverge from r/casual (LSM 0.61)"
Rhetorical Structure	Argument ordering, hedging frequency	How arguments are built	"Evidence-first ordering; hedge rate 3.1-4.5 per 100 clauses; concession-before-rebuttal pattern"
Register Variation	Single voice vs. context-dependent voices	Whether style shifts by context	"Two registers: technical (FK 13-15) and conversational (FK 9-11); switch triggers documented"
Speech Act Distribution	Proportions of communicative functions	Balance of explaining, advising, challenging	"Explaining 45%, advising 20%, challenging 15%, supporting 12%, questioning 8%"
Social Graph / Audience Patterns	Audience-awareness requirements	Whether voice adapts to interlocutor	"Adjusts formality +1 tier when replying to authority figures; maintains base register otherwise"
Persona Archetype	Overall voice and role framing	The meta-identity of the voice	"Knowledgeable peer, not authority figure; teaches by sharing experience, not by instructing"

Workflow

Copy this checklist and track progress:

Style Specification Building Progress:
- [ ] Step 1: Inventory available analyses and assess coverage
- [ ] Step 2: Extract key findings from each analysis report
- [ ] Step 3: Translate each finding into a constraint (with confidence)
- [ ] Step 4: Detect and resolve cross-analysis conflicts
- [ ] Step 5: Prioritize constraints by confidence and impact
- [ ] Step 6: Build the numeric profile
- [ ] Step 7: Write the prose specification
- [ ] Step 8: Validate specification implementability
- [ ] Step 9: Write findings to docs/analysis/24-style-specification.md

Step 1: Inventory Available Analyses and Assess Coverage

Before building the specification, determine which analyses are available and what dimensions they cover. Not all analyses need to be complete -- the specification handles missing inputs gracefully.

Coverage assessment matrix:

Dimension	Required Analyses	Minimum for Constraint	Available?	Confidence
Emotional valence	MDPI Archetype	Archetype label + typicality	[ ]
Vocabulary complexity	Big Five (O), Readability/LD	At least one of these two	[ ]
Formality/structure	Big Five (C), Stylometric	At least one of these two	[ ]
Linguistic registers	LIWC Psycholinguistic	Dimension percentages	[ ]
Function-word patterns	Stylometric Fingerprint	Feature distributions	[ ]
Complexity targets	Readability/Lexical Diversity	Grade-level range + TTR	[ ]
Community convergence	CAT/LSM	LSM scores per community	[ ]
Argument structure	Rhetorical Structure	Ordering + device frequencies	[ ]
Register switching	Register Variation	Context-dependent distributions	[ ]
Speech act balance	Speech Act Distribution	Act proportions	[ ]
Audience adaptation	Social Graph/Audience	Interaction pattern analysis	[ ]
Voice identity	Persona Archetype	Archetype label + description	[ ]

Minimum viable specification: At least 3 analyses covering at least 3 different dimensions. A specification built from fewer than 3 analyses or covering fewer than 3 dimensions should be labeled "partial specification" and must document which constraint categories have no evidential basis.

Dimension classification:

Analyses Available	Specification Type	Coverage Label
10-12 analyses	Full specification	Comprehensive
7-9 analyses	Strong specification	Broad coverage
4-6 analyses	Moderate specification	Partial coverage; document gaps
3 analyses	Minimal specification	Sparse; label as preliminary
< 3 analyses	STOP	Insufficient; do not build specification

Step 2: Extract Key Findings from Each Analysis Report

For each available analysis, extract the findings that will translate into constraints. Read each report (from docs/analysis/) and record:

1. The finding (what was measured and what value was observed)
2. The confidence level (as stated in the original report: high, moderate, low, or indeterminate)
3. The evidence basis (corpus size, number of samples, statistical measure)
4. Any caveats the original analysis flagged

Extraction format per analysis:

### [Analysis Name] (Report: docs/analysis/NN-name.md)

| Finding | Value/Range | Confidence | Evidence Basis | Caveats |
|---------|-------------|------------|----------------|---------|
| [metric] | [value] | [H/M/L] | [basis] | [caveats] |

Critical rule: Do NOT reinterpret or re-analyze raw data at this step. Accept the upstream report's findings and confidence levels as given. If an upstream report flagged low confidence, preserve that flag. The specification is a translation layer, not a re-analysis.

Step 3: Translate Each Finding into a Constraint

This is the core translation step. For each extracted finding, produce a concrete writing constraint using the mapping table from Quick Reference.

Translation rules:

1. Use ranges, not exact values. A finding of "FK grade 12.3" becomes a constraint of "FK grade 11-13" (typically +/- 1 grade level). A finding of "article rate 6.7%" becomes "article rate 6.2-7.2%" (typically +/- 0.5 percentage points for function-word rates).

2. Scale constraint precision to confidence:

Confidence Level	Constraint Precision	Range Width
High	Narrow range	+/- 10-15% of value
Moderate	Medium range	+/- 20-30% of value
Low	Wide range or soft preference	+/- 40-50% of value, stated as "prefer" not "require"
Indeterminate	No constraint	Omit or state "no constraint; insufficient data"

3. Use constraint verbs that match confidence:

Confidence	Verb	Example
High	MUST / ALWAYS / NEVER	"Sentence length MUST average 16-20 words"
Moderate	SHOULD / PREFER	"SHOULD use discourse markers at 2-4 per paragraph"
Low	MAY / CONSIDER	"MAY include rhetorical questions; frequency uncertain"
Indeterminate	[omit]	Do not generate a constraint

4. Every constraint must cite its source analysis. Format: [constraint text] (Source: [analysis name], confidence: [H/M/L])

Translation examples per analysis type:

From MDPI Archetype (e.g., HHL classification):

• Finding: "Classified as HHL (High Score, High Sentiment, Low Toxicity) with typicality 0.87"
• Constraint: "Emotional register MUST maintain positive-to-neutral valence. Toxicity markers (profanity, aggressive rhetoric) MUST be avoided. Arousal level SHOULD be moderate -- enthusiastic but measured. (Source: MDPI Archetype, confidence: High)"

From Big Five Openness (e.g., score 72 +/- 12):

• Finding: "Openness score 72 (High), confidence band +/- 12"
• Constraint: "Vocabulary SHOULD include sophisticated/abstract terms. Long words (6+ chars) SHOULD constitute 18-24% of text. Type-token ratio SHOULD target 0.45-0.55 on 1000-word windows. Intellectual framing and tentative language ('perhaps', 'consider') are appropriate. (Source: Big Five Openness, confidence: Moderate)"

From Readability/Lexical Diversity (e.g., FK 12.3, MTLD 94):

• Finding: "FK grade 12.3, Coleman-Liau 13.1, MTLD 94, MATTR-50 0.72"
• Constraint: "Readability MUST target FK grade 11-14 (consensus range). MTLD MUST exceed 80. Average sentence length SHOULD be 18-22 words. Average word length SHOULD be 4.8-5.4 characters. (Source: Readability/LD, confidence: High)"

Step 4: Detect and Resolve Cross-Analysis Conflicts

Different analyses can produce contradictory constraints. This step identifies and resolves them.

Common conflict patterns:

Conflict	Typical Sources	Resolution Strategy
Formality disagreement	Big Five (C) says formal; Stylometric shows casual function words	Stylometric wins for function-word patterns (unconscious); Big Five informs structural organization (conscious). Both can coexist: formal organization with casual word-level texture.
Complexity disagreement	Big Five (O) suggests complex vocabulary; Readability shows low grade level	Readability metrics are measured; Big Five is inferred. Use readability range as the hard constraint, Big Five as the vocabulary selection guide within that range.
Emotional range disagreement	MDPI says positive base; Big Five (N) suggests anxiety/negativity	Check which has higher confidence. If MDPI typicality is high and Big Five N has wide confidence band, favor MDPI. Document the tension.
Register disagreement	Register Variation shows multiple voices; Persona suggests single identity	Not a true conflict: multiple registers within one persona is common. Specification should define the persona, then document register switches.
Accommodation vs. fingerprint	CAT/LSM shows convergence to community X; Stylometric fingerprint differs from community X norms	Distinguish accommodated features (content words, topic framing) from stable fingerprint features (function words). Both are valid for different feature sets.

Conflict resolution protocol:

digraph conflict_resolution {
    rankdir=TB;
    "Two analyses\nconflict?" [shape=diamond];
    "Do they measure\nsame feature?" [shape=diamond];
    "Which has\nhigher confidence?" [shape=diamond];
    "Not a true conflict:\ndifferent features" [shape=box];
    "Higher-confidence\nanalysis sets hard\nconstraint; lower\nbecomes soft preference" [shape=box];
    "Equal confidence:\nuse range that\nencompasses both" [shape=box];
    "Document both and\nstate cannot resolve" [shape=box];

    "Two analyses\nconflict?" -> "Do they measure\nsame feature?" [label="yes"];
    "Do they measure\nsame feature?" -> "Not a true conflict:\ndifferent features" [label="no, different\nfeatures"];
    "Do they measure\nsame feature?" -> "Which has\nhigher confidence?" [label="yes, same\nfeature"];
    "Which has\nhigher confidence?" -> "Higher-confidence\nanalysis sets hard\nconstraint; lower\nbecomes soft preference" [label="one is\nclearly higher"];
    "Which has\nhigher confidence?" -> "Equal confidence:\nuse range that\nencompasses both" [label="similar\nconfidence"];
}

Documenting resolutions: Every conflict MUST be documented in the specification with:

1. The conflicting findings (with sources)
2. Why they conflict
3. How the conflict was resolved
4. What evidence supported the resolution

Step 5: Prioritize Constraints by Confidence and Impact

Not all constraints are equally important or reliable. Prioritize them into tiers.

Tier assignment criteria:

Tier	Confidence	Impact	Constraint Count Target	Role in Specification
Tier 1: Core	High confidence from measured analysis	High impact on voice identity	5-8 constraints	MUST-follow rules; these define the voice
Tier 2: Strong	Moderate-to-high confidence	Moderate impact	5-10 constraints	SHOULD-follow preferences; these refine the voice
Tier 3: Soft	Low-to-moderate confidence	Lower impact or narrow scope	3-7 constraints	MAY-follow suggestions; these add nuance
Omitted	Indeterminate or contradicted	N/A	0	Not included; documented as gaps

Why tier and limit: Research on LLM prompt engineering (Lakera, 2026; Palantir, 2025) shows that models follow 5-12 well-prioritized constraints reliably. Beyond ~20 constraints, adherence drops and constraints begin to cancel each other out. The tier system ensures the most important constraints get the most weight.

Total constraint budget: Aim for 15-25 total constraints across all tiers. If you have more than 25, merge overlapping constraints or demote low-confidence ones.

Priority signals:

Signal	Priority Boost	Rationale
Measured directly (readability, stylometric)	+1 tier	Direct measurement > inference
Confirmed by multiple analyses	+1 tier	Convergent evidence is more reliable
High confidence in upstream report	Maintains tier	Trust the upstream assessment
Flagged as low-confidence upstream	-1 tier	Preserve upstream caveats
Based on small sample or single context	-1 tier	Less generalizable

Step 6: Build the Numeric Profile

Assemble all quantitative constraints into a structured numeric profile. This is the machine-readable half of the specification.

Numeric profile structure:

## Numeric Profile

### Readability Targets
| Metric | Target Range | Hard Floor | Hard Ceiling | Source | Confidence |
|--------|-------------|------------|--------------|--------|------------|
| FK Grade Level | [X-Y] | [min] | [max] | Readability/LD | [H/M/L] |
| Coleman-Liau Grade | [X-Y] | [min] | [max] | Readability/LD | [H/M/L] |
| Avg Sentence Length (words) | [X-Y] | [min] | [max] | Stylometric + Readability | [H/M/L] |
| Avg Word Length (chars) | [X-Y] | [min] | [max] | Readability/LD | [H/M/L] |

### Lexical Diversity Targets
| Metric | Target Range | Source | Confidence |
|--------|-------------|--------|------------|
| MTLD | [X-Y] | Readability/LD | [H/M/L] |
| MATTR-50 | [X-Y] | Readability/LD | [H/M/L] |
| Long Word % (6+ chars) | [X-Y%] | Big Five (O) + Readability | [H/M/L] |

### Function-Word Targets
| Feature | Target Rate (per 1000 words) | Stability | Source | Confidence |
|---------|------------------------------|-----------|--------|------------|
| Articles | [X-Y] | [high/mod/low] | Stylometric | [H/M/L] |
| Prepositions | [X-Y] | [high/mod/low] | Stylometric | [H/M/L] |
| 1st person singular | [X-Y] | [high/mod/low] | Stylometric + Big Five (N) | [H/M/L] |
| 1st person plural | [X-Y] | [high/mod/low] | Stylometric + Big Five (E) | [H/M/L] |
| Conjunctions | [X-Y] | [high/mod/low] | Stylometric | [H/M/L] |
| [other measured features] | ... | ... | ... | ... |

### Psycholinguistic Dimension Targets
| LIWC Dimension | Target % of Vocabulary | Priority Rank | Source | Confidence |
|---------------|----------------------|--------------|--------|------------|
| Cognitive processes | [X-Y%] | [1-N] | LIWC | [H/M/L] |
| Social processes | [X-Y%] | [1-N] | LIWC | [H/M/L] |
| Affective processes | [X-Y%] | [1-N] | LIWC | [H/M/L] |
| [other dominant dimensions] | ... | ... | ... | ... |

### Rhetorical Structure Targets
| Feature | Target Rate | Unit | Source | Confidence |
|---------|------------|------|--------|------------|
| Hedges | [X-Y] | per 100 clauses | Rhetorical Structure | [H/M/L] |
| Discourse markers | [X-Y] | per paragraph | Rhetorical Structure | [H/M/L] |
| Concessions | [X-Y] | per 1000 words | Rhetorical Structure | [H/M/L] |
| Rhetorical questions | [X-Y] | per 1000 words | Rhetorical Structure | [H/M/L] |

### Speech Act Distribution
| Act Type | Target % | Range | Source | Confidence |
|----------|---------|-------|--------|------------|
| Explaining | [X%] | [X-Y%] | Speech Act | [H/M/L] |
| Advising | [X%] | [X-Y%] | Speech Act | [H/M/L] |
| Challenging | [X%] | [X-Y%] | Speech Act | [H/M/L] |
| Supporting | [X%] | [X-Y%] | Speech Act | [H/M/L] |
| Questioning | [X%] | [X-Y%] | Speech Act | [H/M/L] |

Step 7: Write the Prose Specification

Translate the numeric profile into natural-language writing instructions. This is the human-readable half and the part most directly usable as an LLM system prompt.

Prose specification structure:

The prose specification has four sections, ordered by decreasing importance:

Section A: Voice Identity (from Persona Archetype + MDPI + Big Five composite)

• Who is this voice? What role does it play?
• What is the emotional register? (valence boundaries, arousal level)
• What is the social stance? (peer, authority, subordinate, observer)
• 2-3 sentences maximum. This is the anchor.

Section B: Core Writing Rules (Tier 1 constraints in natural language)

• The 5-8 most important constraints, stated as clear instructions
• Each rule cites its source but reads as natural prose
• These rules are non-negotiable

Section C: Stylistic Preferences (Tier 2 constraints)

• The 5-10 secondary constraints, stated as preferences
• "Prefer X over Y" or "When possible, use X"
• These shape the voice but allow flexibility

Section D: Contextual Nuance (Tier 3 + register variation + audience adaptation)

• How the voice shifts across contexts (if register variation was detected)
• Audience-awareness rules (if social graph analysis was available)
• Soft constraints that add authenticity

Writing the prose:

For each section, convert numeric constraints into natural instructions:

Numeric Constraint	Prose Translation
"FK grade 11-14"	"Write at a level accessible to college-educated adults. Use technical terms when precision requires them, but prefer clarity over jargon."
"Article rate 6.2-7.1%"	[Do NOT translate function-word rates into prose. Keep these in the numeric profile only. LLMs cannot consciously control function-word frequencies.]
"Hedge rate 3.1-4.5 per 100 clauses"	"Qualify claims moderately. Use hedges like 'tends to', 'suggests', 'in most cases' several times per paragraph, but avoid hedging every statement."
"Explaining 45%, Advising 20%"	"Spend most of your words explaining how things work. Include practical advice, but explanation should outweigh recommendation roughly 2:1."
"Evidence-first argument ordering"	"Present evidence before stating your conclusion. Let the reader follow your reasoning rather than leading with the takeaway."

What NOT to translate into prose: Function-word frequencies, punctuation rates, and other unconscious features should remain in the numeric profile only. Telling an LLM "use articles at 6.5% rate" is counterproductive -- it forces conscious attention to something that should emerge naturally from following higher-level voice constraints. These numeric targets are for post-hoc validation, not real-time instruction.

Step 8: Validate Specification Implementability

Before finalizing, check that the specification can actually be followed.

Validation checks:

Check	Pass Condition	Fail Action
Constraint count	15-25 total across all tiers	If >25: merge or demote. If <10: check if analyses were too sparse.
Tier 1 count	5-8 Tier 1 constraints	If >8: some must be demoted. If <5: check for missing high-confidence analyses.
Internal consistency	No Tier 1 constraints contradict each other	Resolve any remaining conflicts before finalizing.
Prose clarity	Each prose rule can be understood without reading the source analysis	Rewrite unclear rules.
Numeric feasibility	All numeric ranges are achievable simultaneously	Check that grade-level targets, sentence-length targets, and vocabulary targets are mutually compatible.
Gap documentation	Every omitted dimension is documented with a reason	Add missing gap documentation.
Source traceability	Every constraint cites its source analysis	Add missing citations.
Exemplar test	Can you write 2-3 sentences that satisfy all Tier 1 constraints?	If not, constraints may be over-specified or internally contradictory. Revise.

The exemplar test is critical. Write 2-3 test sentences following only the Tier 1 constraints. If you cannot produce sentences that satisfy all of them simultaneously, the specification is over-constrained and must be relaxed.

Step 9: Write the Report

Write all findings to docs/analysis/24-style-specification.md.

Report Output Template

The final report MUST be written to docs/analysis/24-style-specification.md with this structure:

# Style Specification

## Analysis Coverage

### Available Analyses
| # | Analysis | Report Location | Findings Used | Confidence |
|---|----------|----------------|---------------|------------|
| [N] | [name] | docs/analysis/NN-name.md | [count] findings | [overall H/M/L] |

### Coverage Assessment
- **Specification type:** [Full / Strong / Moderate / Minimal]
- **Dimensions covered:** [N] of 12
- **Dimensions missing:** [list with reasons]
- **Overall confidence:** [assessment]

## Constraint Derivation

### Extracted Findings
[Per-analysis finding tables from Step 2]

### Analysis-to-Constraint Translations
[Per-finding constraint translations from Step 3, organized by source analysis]

### Conflict Resolutions
| Conflict | Source A | Source B | Resolution | Rationale |
|----------|---------|---------|------------|-----------|
| [description] | [finding] | [finding] | [resolution] | [why] |

## Prioritized Constraints

### Tier 1: Core Constraints (MUST)
| # | Constraint | Source Analysis | Confidence | Rationale |
|---|-----------|----------------|------------|-----------|
| 1 | [constraint] | [source] | [H/M] | [why Tier 1] |

### Tier 2: Strong Preferences (SHOULD)
| # | Constraint | Source Analysis | Confidence | Rationale |
|---|-----------|----------------|------------|-----------|
| 1 | [constraint] | [source] | [M/H] | [why Tier 2] |

### Tier 3: Soft Suggestions (MAY)
| # | Constraint | Source Analysis | Confidence | Rationale |
|---|-----------|----------------|------------|-----------|
| 1 | [constraint] | [source] | [L/M] | [why Tier 3] |

### Omitted (Insufficient Evidence)
| Dimension | Reason Omitted | What Would Be Needed |
|-----------|---------------|---------------------|
| [dimension] | [reason] | [what analysis or data is missing] |

## Numeric Profile
[Complete numeric profile from Step 6]

## Prose Specification

### A. Voice Identity
[2-3 sentence voice anchor]

### B. Core Writing Rules
[Tier 1 constraints as natural prose instructions]

### C. Stylistic Preferences
[Tier 2 constraints as preference statements]

### D. Contextual Nuance
[Tier 3 + register variation + audience rules]

## Validation

### Constraint Summary
- **Total constraints:** [N]
- **Tier 1:** [N] | **Tier 2:** [N] | **Tier 3:** [N]
- **Internal consistency:** [pass/fail with notes]
- **Numeric feasibility:** [pass/fail with notes]

### Exemplar Sentences
[2-3 sentences written to satisfy all Tier 1 constraints, demonstrating the voice]

### Known Gaps
[Dimensions not covered, with impact assessment]

## NNGroup Tone Dimensions
Map the specification to the four standard tone-of-voice dimensions:
| Dimension | Position | Evidence |
|-----------|----------|----------|
| Formal <---> Casual | [position on spectrum] | [which analyses support this] |
| Serious <---> Funny | [position on spectrum] | [which analyses support this] |
| Respectful <---> Irreverent | [position on spectrum] | [which analyses support this] |
| Enthusiastic <---> Matter-of-fact | [position on spectrum] | [which analyses support this] |

## Limitations and Caveats
- Style specification is derived from analysis of [corpus description], not from direct observation of the author's intent
- Confidence levels propagate from upstream analyses; low-confidence upstream = low-confidence constraint
- The specification captures patterns in a specific corpus and time period; the author's voice may vary in other contexts
- LLMs cannot consciously control all specified features (especially function-word rates); numeric targets for unconscious features are for post-hoc validation only
- [Corpus-specific limitations]
- [Analysis-gap limitations]

## References
- [Upstream analysis reports cited]
- Nielsen Norman Group (2016). The Four Dimensions of Tone of Voice.
- Boyd, R.L., et al. (2022). LIWC-22 Manual.
- [Other methodological references used]

Good Patterns

• Map every analysis to a specific constraint using the mapping table. No analysis output should sit unused unless confidence is too low.
• Use ranges, not exact values. A finding of "FK 12.3" becomes a constraint of "FK 11-14." Exact values create false precision and are impossible for writers or LLMs to hit.
• Prioritize constraints by confidence. High-confidence findings from direct measurement (readability, stylometric) become Tier 1. Low-confidence inferences (personality traits with wide bands) become Tier 3 or are omitted.
• Resolve conflicts explicitly. When two analyses disagree, document both findings, explain the conflict, and state how it was resolved. Never silently drop a finding.
• Produce both numeric and prose forms. The numeric profile enables post-hoc validation; the prose specification enables real-time writing guidance. Both are needed.
• Keep total constraints to 15-25. Research shows diminishing returns beyond this range. Merge overlapping constraints rather than listing every sub-finding.
• Separate conscious from unconscious features. Function-word rates belong in the numeric profile for validation, not in the prose specification as instructions.
• Write exemplar sentences that demonstrate all Tier 1 constraints working together. If you cannot, the specification is over-constrained.
• Cite sources for every constraint. Traceability enables future revision when upstream analyses are re-run.
• Document gaps. Missing analyses are information; they tell the reader which constraint categories lack evidential backing.

Anti-Patterns

Anti-Pattern	Why It Fails	Instead
Including raw analysis outputs without translating to constraints	Analysis outputs are observations; the specification needs instructions. Dumping "LIWC cognitive process: 28.3%" into a spec gives no guidance.	Translate every finding into an actionable writing instruction with confidence level
Over-specifying (30+ constraints)	LLMs and humans cannot track 30+ simultaneous constraints; lower-priority constraints will be randomly ignored	Limit to 15-25 total; merge overlapping constraints; use the tier system
Ignoring conflicts between analyses	Contradictory constraints produce inconsistent output and undermine the specification's credibility	Detect, document, and resolve every conflict using the resolution protocol
Treating low-confidence findings as hard constraints	Promotes a false sense of precision; a Big Five score of 55 +/- 20 does not justify a "MUST" constraint	Match constraint verb (MUST/SHOULD/MAY) to upstream confidence level
Producing a specification too abstract to implement	"Write in an engaging, thoughtful manner" is not implementable; it lacks any measurable target	Every constraint must be either measurable (numeric) or demonstrable (exemplar sentences)
Translating function-word rates into prose instructions	Telling a writer "use articles at 6.5%" is unnatural and counterproductive; function-word usage is unconscious	Keep function-word targets in the numeric profile for validation; do not include in prose instructions
Skipping the exemplar test	Without testing that constraints can coexist in actual sentences, the specification may be internally contradictory	Always write 2-3 exemplar sentences satisfying all Tier 1 constraints
Building a specification from < 3 analyses	Too few inputs means too many constraint categories are unsupported; the resulting "specification" is mostly gaps	Require minimum 3 analyses across 3+ dimensions; label anything less as insufficient
Merging multi-author findings into one specification	Different authors have different voices; averaging them produces a generic voice that matches no one	One specification per author; create separate specifications and note commonalities if needed
Inventing constraints not supported by any analysis	Intuition-based constraints undermine the evidence chain; the specification should be traceable to data	Every constraint must cite its source analysis; if no analysis supports a dimension, document the gap

Boundaries

This skill SHOULD:

• Map all available analysis outputs to concrete writing constraints
• Translate findings using ranges, not exact values, scaled to confidence
• Detect and resolve conflicts between analyses using the resolution protocol
• Prioritize constraints into tiers (Core/Strong/Soft) by confidence and impact
• Produce both a numeric profile (for validation) and prose specification (for writing guidance)
• Limit total constraints to 15-25 to maintain LLM and human followability
• Document every gap where analyses were unavailable
• Write exemplar sentences demonstrating the specification
• Cite the source analysis for every constraint
• Write the complete specification to docs/analysis/24-style-specification.md

This skill should NOT:

• Invent constraints not supported by any analysis finding
• Include all findings regardless of confidence (low-confidence findings should be Tier 3 or omitted)
• Produce a specification longer than an LLM context window can reasonably process (~2000 words for the prose section)
• Skip conflict resolution when analyses disagree
• Translate unconscious features (function-word rates) into conscious prose instructions
• Re-analyze raw data (accept upstream report findings as given)
• Build a specification from fewer than 3 analyses
• Merge findings from different authors into a single-voice specification
• Claim the specification captures the author's "true voice" (it captures patterns in a specific corpus)
• Produce only a numeric profile without prose, or only prose without numeric targets

Insufficient Data Handling

When Some Analyses Were Not Completed

Missing Analysis	Impact on Specification	Mitigation
MDPI Archetype missing	No emotional valence boundaries	Use Big Five (N, E, A) to approximate emotional register if available; document as lower-confidence substitute
Big Five missing	No personality-derived vocabulary/formality constraints	Rely on Readability/LD for complexity and Stylometric for structural patterns; document that personality dimension is uncharacterized
LIWC missing	No psycholinguistic register priorities	Use Stylometric function-word categories as a partial substitute for register characterization; document gap
Stylometric Fingerprint missing	No function-word targets, no sentence-structure distributions	Readability metrics provide some structural info; function-word validation cannot be performed. Document as significant gap.
Readability/Lexical Diversity missing	No measurable complexity targets	Big Five (O) can suggest a complexity direction but not a numeric range. Specification will lack hard readability constraints.
CAT/LSM missing	No community convergence targets	Specification describes the author's own voice without accommodation targets. Document that community calibration is unavailable.
Rhetorical Structure missing	No argument ordering or hedging frequency constraints	Specification covers what to say but not how to structure arguments. Document as gap in structural guidance.
Register Variation missing	Cannot determine if one voice or multiple registers are needed	Default to single-register specification. Note that context-dependent switching may exist but was not measured.
Speech Act Distribution missing	No communicative function proportions	Specification cannot prescribe the balance of explaining vs. advising vs. challenging. Document gap.
Social Graph/Audience missing	No audience-adaptation rules	Specification assumes a fixed register regardless of interlocutor. Document that audience sensitivity was not measured.
Persona Archetype missing	No overarching voice identity anchor	Synthesize a voice identity paragraph from available analyses (Big Five, MDPI, Speech Acts). Label as inferred rather than analyzed.

When Analyses Produce Contradictory Results

Follow the conflict resolution protocol in Step 4. Additional handling:

Contradiction Type	Resolution
Two analyses give opposite directions for the same feature	Use the analysis with higher confidence and more direct measurement. Demote the contradicted analysis's constraint to Tier 3 or omit it. Document the contradiction.
One analysis gives a tight range, another gives a wide range for the same metric	Adopt the union of both ranges. The specification is less precise but internally consistent.
Personality says X but behavioral analysis shows not-X	Behavioral/measured analysis trumps inferred personality. Personality inference has inherent limits (~5% variance explained). Document the discrepancy.
Multiple analyses contradict on 3+ dimensions	Indicates possible corpus issue (mixed authorship, strong context effects, or insufficient data in upstream analyses). Flag the specification as unreliable and recommend re-running upstream analyses with stricter data.

When Confidence Is Low Across the Board

If most available analyses report low confidence:

Condition	Action
All analyses are low-confidence	Produce a "preliminary specification" with ALL constraints at Tier 3 (MAY). State explicitly that the specification requires validation against new data. Do not include Tier 1 constraints.
Mix of moderate and low confidence	Only moderate-confidence findings get Tier 1-2 status. The specification will be sparse at the core but honest about its limitations.
High confidence on some, indeterminate on others	Build a focused specification around the high-confidence dimensions. Clearly mark which dimensions are data-driven vs. uncharacterized.

Minimum Viable Specification Requirements

Requirement	Threshold	Consequence if Not Met
Minimum analyses	3 analyses from 3+ different dimensions	Do not produce a specification; document what is available and what is needed
Minimum Tier 1 constraints	At least 3 high-confidence constraints	Label as "partial specification"; usable for rough guidance but not voice matching
Minimum dimensions	At least 3 of the 12 dimension categories	Specification covers only a subset of voice characteristics; document which are missing
Minimum prose specification	Voice Identity + Core Writing Rules at minimum	If only numeric profile is possible (no personality/persona/archetype data), label as "metric profile" not "style specification"

Common Mistakes

Mistake	Fix
Dumping all analysis findings into the specification without translating them	Each finding must become a constraint with an action verb, a range, and a confidence level
Writing 40+ constraints because every analysis produced many findings	Merge overlapping constraints and use the tier system; target 15-25 total
Ignoring a conflict because "it's just a minor disagreement"	All conflicts must be documented and resolved; minor disagreements still produce confusing specifications
Setting Tier 1 constraints from low-confidence analyses	Only high or moderate confidence findings qualify for Tier 1; low confidence goes to Tier 3 or is omitted
Writing a prose specification that restates the numeric profile	Prose and numeric serve different purposes; prose guides real-time writing, numeric enables post-hoc validation
Producing a specification without exemplar sentences	Without exemplars, you do not know if the constraints can coexist; always test with 2-3 sentences
Including function-word rate targets as prose writing instructions	Function-word usage is unconscious; prose instructions like "use 6.5% articles" are unnatural. Keep in numeric profile only.
Not documenting which analyses were unavailable	Gaps are critical metadata; a reader must know which parts of the specification have evidential backing
Giving equal weight to all constraints	Tiering by confidence and impact is the entire point of this skill; flat constraint lists are less effective
Creating the specification before upstream analyses are finalized	The specification translates finalized findings; if upstream reports are still in draft, the specification will need re-building

References

• Mann, W.C. & Thompson, S.A. (1988). Rhetorical Structure Theory: Toward a functional theory of text organization. Text, 8(3), 243-281.
• Nielsen Norman Group (2016). The Four Dimensions of Tone of Voice.
• Mailchimp Content Style Guide: Voice and Tone.
• Google Developer Documentation Style Guide: Voice and Tone.
• Lakera (2026). The Ultimate Guide to Prompt Engineering.
• Palantir (2025). Best practices for LLM prompt engineering.
• Latitude (2025). How Examples Improve LLM Style Consistency.
• Scale AI (2025). Using LLMs While Preserving Your Voice.
• Boyd, R.L., et al. (2022). LIWC-22 Manual: Development and Psychometrics.
• Stachl, C., et al. (2022). The kernel of truth in text-based personality assessment. Psychological Bulletin.
• Ferrara, E., et al. (2025). Characterizing User Archetypes and Discussions on Social Hypernetworks. MDPI Big Data and Cognitive Computing, 9(9), 236.
• Mosteller, F. & Wallace, D.L. (1963). Inference in an authorship problem. Journal of the American Statistical Association, 58(302), 275-309.
• Ireland, M.E., et al. (2011). Language Style Matching Predicts Relationship Initiation and Stability. Psychological Science, 22(1), 39-44.