| name | subagent-instruction-operationalization |
| description | Use when translating a completed style specification into an LLM subagent prompt that reproduces a target voice, converting psycholinguistic analysis outputs into concrete prompt directives, operationalizing stylometric fingerprints and archetype classifications as generative instructions, or building a testable persona prompt from upstream analysis results |
Subagent Instruction Operationalization
Overview
Translate a completed style specification into a structured LLM subagent prompt that can reproduce a target voice. The core principle: every constraint in the style specification must become either a concrete, measurable prompt directive or be explicitly documented as aspirational. A style specification is an analytical artifact (what the voice IS); a subagent instruction is a generative artifact (how to PRODUCE the voice). This skill bridges the gap by converting each upstream analysis output into a prompt section with verification criteria.
Why this is hard: LLMs respond to instructions with varying fidelity depending on constraint type. Lexical constraints ("use grade-level 12 vocabulary") are moderately enforceable. Structural constraints ("lead with evidence before claims") are somewhat enforceable. Distributional constraints ("maintain a 40/30/20/10 split of explaining/advising/challenging/asserting") are weakly enforceable without few-shot examples. The prompt must be designed with awareness of these enforcement gradients.
Research foundation: Persona-based prompting (role assignment) produces measurably different output style, tone, and vocabulary (Brim Labs, 2025; PromptHub, 2025). Detailed persona specifications consistently outperform vague ones, and LLM-generated personas often outperform human-written ones (LearnPrompting, 2025). Few-shot examples are often more effective than lengthy instructions for style constraints (Palantir, 2025). Stylometric research confirms that function-word patterns, sentence-length distributions, and punctuation habits are the most stable authorship markers -- and therefore the most important features to replicate (Mosteller & Wallace, 1963; Kestemont, 2014). Recent work on LLM style transfer shows that instruction-tuned models can approximate target styles with one-shot accuracy between 67.6% and 94.7% depending on prompting strategy (arXiv:2509.24930).
When to Use
- Style specification from upstream pipeline is complete (or partially complete -- see Insufficient Data Handling)
- Need to produce a prompt that an LLM subagent can use to generate text in a target voice
- Translating psycholinguistic analysis (LIWC, Big Five, MDPI archetype, stylometrics, readability, CAT, rhetorical structure, speech acts) into generative instructions
- Building a voice-replication prompt for ANY text corpus, not limited to any specific platform
- Creating a testable, versioned persona prompt with verification criteria
When NOT to use:
- No style specification exists yet (run the upstream analyses first)
- Goal is to impersonate someone for deception, fraud, or harassment (ethical boundary -- this skill produces writing style approximations for legitimate purposes such as content assistance, ghostwriting with consent, or creative writing)
- Target voice is "generic professional" or "standard formal" -- these do not require specification-driven prompting; a simple role prompt suffices
- Style specification contains only one or two dimensions (e.g., only readability) -- the prompt would be too thin to constitute a voice; use the individual constraint directly
digraph operationalization_decision {
rankdir=TB;
"Style specification\nexists?" [shape=diamond];
"3+ dimensions\ncompleted?" [shape=diamond];
"Legitimate\nuse case?" [shape=diamond];
"Few-shot examples\navailable?" [shape=diamond];
"Full prompt\ngeneration" [shape=box];
"Run upstream\nanalyses first" [shape=box];
"Reduced prompt with\ngaps documented" [shape=box];
"STOP: Ethical\nboundary" [shape=box];
"Prompt without\nfew-shot section" [shape=box];
"Style specification\nexists?" -> "3+ dimensions\ncompleted?" [label="yes"];
"Style specification\nexists?" -> "Run upstream\nanalyses first" [label="no"];
"3+ dimensions\ncompleted?" -> "Legitimate\nuse case?" [label="yes"];
"3+ dimensions\ncompleted?" -> "Reduced prompt with\ngaps documented" [label="no, 1-2 only"];
"Legitimate\nuse case?" -> "Few-shot examples\navailable?" [label="yes"];
"Legitimate\nuse case?" -> "STOP: Ethical\nboundary" [label="no"];
"Few-shot examples\navailable?" -> "Full prompt\ngeneration" [label="yes"];
"Few-shot examples\navailable?" -> "Prompt without\nfew-shot section" [label="no"];
}
Quick Reference
Prompt Section Architecture
The subagent prompt is organized into sections, each derived from a specific upstream analysis. The order below reflects enforcement priority -- strongest constraints first.
| Section | Source Analysis | Enforcement Strength | What It Controls |
|---|
| Role Frame | Persona archetype + MDPI archetype | Strong | Overall persona identity and emotional register |
| Few-Shot Examples | Original corpus samples | Strong | Concrete style demonstrations (most powerful constraint) |
| Complexity Targets | Readability + Lexical Diversity | Strong (measurable) | Grade level, sentence length, vocabulary diversity |
| Syntactic Stability | Stylometric Fingerprint | Moderate-Strong | Function-word rates, punctuation patterns, sentence-length distribution |
| Emotional Valence | MDPI Archetype + VADER | Moderate | Affective register boundaries, toxicity limits |
| Informational Density | LIWC dimensions + Archetype | Moderate | Noun-heavy vs. conversational, process/content word ratio |
| Evidence Framing | Big Five (Conscientiousness) + Archetype | Moderate | Degree claims require support, qualification habits |
| Rhetorical Structure | Rhetorical/Discourse Analysis | Moderate-Weak | Argument ordering, hedging, post structure |
| Pragmatic Distribution | Speech Act Analysis | Weak (needs examples) | Ratio of explaining vs. advising vs. challenging |
| Register Rules | Register Variation Analysis | Conditional | Context-dependent style switching rules |
| Community Convergence | CAT / LSM | Weak (ambient) | Target community standards for function-word convergence |
Enforcement Gradient
| Strength | Meaning | Prompt Strategy |
|---|
| Strong | LLM reliably follows | Direct numerical constraints + few-shot examples |
| Moderate | LLM follows with drift | Constraints + self-check instructions + exemplars |
| Weak | LLM approximates | Few-shot examples as primary driver; instructions as secondary |
| Conditional | Context-triggered | If/then rules with explicit context detection |
Minimum Specification Completeness
| Dimensions Completed | Viability | Action |
|---|
| 7+ of 9 dimensions | Full prompt | Generate complete subagent instruction |
| 4-6 dimensions | Partial prompt | Generate prompt with gap documentation; mark missing sections as "DEFAULT: use natural style" |
| 1-3 dimensions | Minimal prompt | Generate targeted constraints only; do NOT frame as a full voice replication prompt |
| 0 dimensions | Not viable | STOP. Run upstream analyses. |
Workflow
Copy this checklist and track progress:
Subagent Instruction Operationalization Progress:
- [ ] Step 1: Inventory the style specification and assess completeness
- [ ] Step 2: Select few-shot examples from the source corpus
- [ ] Step 3: Compose the Role Frame
- [ ] Step 4: Translate complexity targets into numerical constraints
- [ ] Step 5: Encode syntactic stability constraints
- [ ] Step 6: Define emotional valence boundaries
- [ ] Step 7: Set informational density directives
- [ ] Step 8: Specify evidence framing requirements
- [ ] Step 9: Encode rhetorical structure patterns
- [ ] Step 10: Define pragmatic distribution targets
- [ ] Step 11: Write register rules (if context-dependent)
- [ ] Step 12: Encode community convergence as ambient style guidance
- [ ] Step 13: Add self-verification instructions
- [ ] Step 14: Assemble the complete prompt and check token budget
- [ ] Step 15: Test the prompt against source material
- [ ] Step 16: Write findings to docs/analysis/25-subagent-instruction.md
Step 1: Inventory the Style Specification
Before writing any prompt directives, audit which upstream analyses are available and their confidence levels.
Inventory table:
| Dimension | Source Report | Status | Confidence | Key Constraints to Extract |
|---|
| Informational Density | 16-liwc-psycholinguistic.md | [done/partial/missing] | [high/moderate/low] | Process/content word ratio, dominant dimension |
| Emotional Valence | 14-mdpi-hypernetwork-archetype.md | [done/partial/missing] | [high/moderate/low] | Archetype label, valence constraints |
| Evidence Framing | 15-big-five-personality.md | [done/partial/missing] | [high/moderate/low] | Conscientiousness score, assertion style |
| Syntactic Stability | 18-stylometric-fingerprinting.md | [done/partial/missing] | [high/moderate/low] | Function-word rates, sentence-length distribution |
| Complexity Targets | 19-readability-lexical-diversity.md | [done/partial/missing] | [high/moderate/low] | FK grade, MTLD, sentence length range |
| Rhetorical Structure | 20-rhetorical-discourse-structure.md | [done/partial/missing] | [high/moderate/low] | Argument ordering, hedging rate, structure patterns |
| Pragmatic Distribution | 22-speech-act-pragmatic.md | [done/partial/missing] | [high/moderate/low] | Speech act ratios |
| Register Rules | 21-register-variation.md | [done/partial/missing] | [high/moderate/low] | Stable vs. context-dependent classification |
| Community Convergence | 17-cat-linguistic-style-matching.md | [done/partial/missing] | [high/moderate/low] | LSM targets, top-accommodated communities |
If fewer than 4 dimensions are complete: Generate a reduced prompt. Document which sections are missing and what the default behavior will be. Do NOT fabricate constraints from missing data.
Step 2: Select Few-Shot Examples
Few-shot examples are the single most powerful style constraint. They demonstrate what instructions can only describe.
Selection criteria:
| Criterion | Requirement |
|---|
| Typicality | Choose examples that are stylistically typical, not outliers. Use typicality scores from archetype analysis if available. |
| Topic diversity | Select examples spanning at least 2-3 different topics to show that the style persists across subjects. |
| Length variety | Include both short (1-3 sentences) and medium (1-2 paragraphs) examples to demonstrate the voice at different scales. |
| Recency | Prefer recent examples that reflect the current voice, unless temporal analysis shows style stability. |
| Count | 3-5 examples is optimal. More than 7 consumes excessive context without proportional benefit. |
Example framing in the prompt:
Here are examples of the target voice. Study the sentence structure,
word choice, hedging patterns, and argument flow -- not the topic content.
Example 1 (technical context):
[verbatim text from corpus]
Example 2 (casual context):
[verbatim text from corpus]
Example 3 (disagreement context):
[verbatim text from corpus]
When few-shot examples are unavailable: If the original corpus is not accessible, rely on the specification constraints alone but note in the prompt: "No reference examples available. Follow the constraints below as closely as possible. Err toward the conservative end of each range."
Step 3: Compose the Role Frame
The role frame is the first section of the prompt. It establishes identity, not mechanics.
Template:
You are a writing voice replication agent. Your task is to generate text
that matches a specific author's documented writing style. You are NOT
the author -- you are producing an approximation based on a detailed
style analysis. The output should be indistinguishable in STYLE from
the author's writing, while the CONTENT is determined by the task at hand.
Voice profile: [archetype label, e.g., "HHL -- high-visibility, positive,
constructive communicator"] with [personality summary, e.g., "moderate-high
Openness, high Conscientiousness"].
Primary register: [from LIWC, e.g., "Analytical-reflective with
social-relational secondary emphasis"].
Rules for the role frame:
- State explicitly that this is an approximation, not identity theft
- Include the MDPI archetype label and its behavioral signature
- Include the Big Five summary (trait positions, not raw scores)
- Include the LIWC register classification
- Keep under 150 words -- detail belongs in constraint sections below
Step 4: Complexity Targets
Translate readability and lexical diversity measurements into numerical prompt constraints. These are among the most reliably enforceable constraints.
Directive template:
## Complexity Constraints
Target readability: Flesch-Kincaid grade level [X-Y] (consensus range
from [N] formulas). The measured corpus average is [X.X] with standard
deviation [X.X].
Sentence length: Target [X-Y] words per sentence on average. Vary
individual sentences between [min] and [max] words. Do NOT produce
uniform sentence lengths.
Vocabulary diversity: Maintain lexical diversity (MTLD) in the range
[X-Y]. This means introducing new vocabulary at a rate consistent with
[high/moderate/low] diversity. In practice: [concrete guidance, e.g.,
"avoid repeating the same content word within 3 sentences unless it is
a technical term"].
Word length: Target average word length of [X.X] characters.
[X%] of words should be 6+ characters (long words).
Mapping from upstream data:
| Upstream Metric | Prompt Constraint |
|---|
| FK grade level (consensus range across formulas) | "Target FK grade [X-Y]" |
| Average sentence length + std dev | "Average [X-Y] words/sentence, range [min-max]" |
| MTLD score | "MTLD [X-Y]" with concrete vocabulary reuse guidance |
| MATTR score | Secondary check: "Moving-average TTR should approximate [X.XX]" |
| Hapax ratio | Informational: "Approximately [X%] of words should appear only once" |
| Average word length + long-word % | "Average [X.X] chars/word; [X%] words 6+ chars" |
Step 5: Syntactic Stability
Encode the stylometric fingerprint as structural constraints. These maintain the author's unconscious grammatical habits.
Directive template:
## Syntactic Fingerprint
Function word targets (percentage of total words):
- Articles (a, an, the): [X.X%] (+/- [X.X%])
- Prepositions: [X.X%] (+/- [X.X%])
- Personal pronouns: [X.X%] (+/- [X.X%])
- I/me/my: [X.X%] | we/us/our: [X.X%] | you/your: [X.X%]
- Conjunctions: [X.X%] (+/- [X.X%])
- Auxiliary verbs: [X.X%] (+/- [X.X%])
Punctuation patterns:
- Comma rate: [X.X] per sentence
- Semicolons: [rare/occasional/frequent]
- Dashes (em/en): [rare/occasional/frequent]
- Exclamation marks: [rare/occasional/frequent]
- Parenthetical asides: [rare/occasional/frequent]
Sentence length distribution:
- Mean: [X] words | Median: [X] words | Std dev: [X]
- [X%] short sentences (< 10 words)
- [X%] medium sentences (10-25 words)
- [X%] long sentences (> 25 words)
Paragraph length: Average [X] sentences per paragraph. Range [X-Y].
Only include features with stability coefficient CV < 0.30 from the stylometric fingerprint. Unstable features (CV >= 0.30) should be omitted or marked as aspirational.
Step 6: Emotional Valence Boundaries
Translate the MDPI archetype into emotional register rules.
Directive template based on archetype:
| Archetype | Valence Directive |
|---|
| HHH | "Express positive sentiment as the baseline. Aggressive emphasis (strong language, exclamations, intensifiers) is permitted and expected. High emotional arousal. Positive intent with forceful delivery." |
| HHL | "Maintain a consistently positive, constructive tone. Avoid aggressive language, profanity, or confrontational framing. Supportive and measured. Low arousal, high warmth." |
| HLH | "Express criticism and negative assessments directly. Confrontational and assertive. Toxic/aggressive language markers are part of the voice. High arousal, combative register." |
| HLL | "Express criticism through substantive analysis, not aggressive language. Skeptical and evaluative but restrained. Analytical negativity without hostility." |
| LHH | "Positive intent expressed through confrontational or aggressive delivery. 'Tough love' framing. Supportive conclusions reached through blunt, sometimes profane, language." |
| LHL | "Quietly positive and encouraging. Gentle, affirming tone. Low-key delivery. Avoid assertiveness or dominance." |
| LLH | "Negative and hostile. Bitter or resentful framing. Express discontent through aggressive language. Low-visibility, high-toxicity patterns." |
| LLL | "Subdued negativity. Resigned or withdrawn tone. Express dissatisfaction through measured, low-energy commentary. Avoid both enthusiasm and aggression." |
Include VADER calibration: "The target sentiment compound score across outputs should average approximately [X.XX] (range [X.XX to X.XX])."
Step 7: Informational Density
Translate LIWC dimension profile and process/content word split into density directives.
Directive template:
## Informational Density
Content density: [content-dense / balanced / process-heavy]
- Function word percentage target: [X%] (baseline: 55-65%)
- Content word percentage target: [X%]
- This voice [is / is not] noun-heavy.
Dominant psycholinguistic registers (from LIWC):
1. [Primary register, e.g., "Cognitive-analytical: elevated insight
and causation language"]
2. [Secondary register, e.g., "Social-relational: above-baseline
social process words"]
Register guidance:
- [Elevated dimension]: Use [specific word types] more frequently
than a generic voice would. Target [X%] vs. baseline [Y%].
- [Reduced dimension]: Use [specific word types] less frequently.
Target [X%] vs. baseline [Y%].
Step 8: Evidence Framing
Derive from Big Five Conscientiousness score and archetype.
Mapping:
| Conscientiousness | Archetype Score | Evidence Framing Directive |
|---|
| High (65+) | High | "Support claims with evidence, examples, or reasoning. Use discourse markers (therefore, because, given that). Qualify assertions with scope limitations. Structure arguments with clear premises before conclusions." |
| High (65+) | Low | "Support claims when possible but accept that lower-visibility contexts permit more casual assertion." |
| Average (35-65) | Any | "Mix supported and unsupported claims naturally. Some assertions can stand alone; complex or controversial claims should include reasoning." |
| Low (0-35) | Any | "State opinions directly without obligatory evidence scaffolding. Casual assertion is the default. Include reasoning only when the point is complex or when challenged." |
Step 9: Rhetorical Structure
Encode argument ordering and rhetorical device usage.
Directive template:
## Rhetorical Structure
Argument ordering: [claim-first / evidence-first / dialectical /
exploratory / narrative-embedded]
- [Concrete description: e.g., "State your position in the first
sentence, then provide 2-3 supporting points, then anticipate
a counter-argument."]
Hedging frequency: [X.X%] of assertions include hedging markers
(I think, perhaps, might, arguably, it seems).
- [Concrete guidance: e.g., "Hedge approximately 1 in every 4
evaluative claims. Never hedge factual statements."]
Concession patterns: [rare / occasional / frequent]
- [If frequent: "Acknowledge opposing views before presenting
your own position. Use 'to be fair', 'granted', 'I see your
point but'."]
Rhetorical questions: [rare / occasional / frequent]
- [If used: "Approximately [X] per [Y] paragraphs."]
List vs. prose ratio: [X%] of multi-point arguments use enumerated
lists vs. flowing prose.
Post structure:
- Opening: [direct claim / question / anecdote / context-setting]
- Body: [sequential points / point-counterpoint / narrative]
- Closing: [summary / call to action / open question / none]
Step 10: Pragmatic Distribution
Translate speech act analysis into output ratios.
Directive template:
## Pragmatic Distribution (Speech Act Targets)
Target distribution across output:
- Explaining (causal reasoning, teaching): ~[X%]
- Advising (recommendations, suggestions): ~[X%]
- Asserting (declarative claims, opinions): ~[X%]
- Challenging (counter-arguments, disagreement): ~[X%]
- Questioning (interrogatives, prompts): ~[X%]
- Supporting (agreement, affirmation): ~[X%]
Primary mode: [The dominant speech act, e.g., "This voice primarily
EXPLAINS -- most responses walk through reasoning rather than
declaring conclusions."]
Note: These are approximate targets. Do not mechanically count
speech acts. Instead, use the primary mode as the default and
deviate when context requires it.
Enforcement note: Pragmatic distribution is weakly enforceable through instructions alone. The few-shot examples (Step 2) are the primary mechanism for establishing this distribution. The directive above provides a calibration target.
Step 11: Register Rules (Conditional)
Only include this section if the register variation analysis classified the voice as context-dependent.
Directive template (for context-dependent voices):
## Register Rules
This voice shifts style based on context. Apply these rules:
WHEN writing about [technical/professional domain]:
- Increase formality by [specific changes]
- Reduce hedging frequency to [X%]
- Increase evidence framing density
- Target FK grade [X-Y] (higher than default)
WHEN writing in [casual/social context]:
- Decrease sentence length to [X-Y] words average
- Increase pronoun usage (I/you) by [X%]
- Permit informal constructions and contractions
- Target FK grade [X-Y] (lower than default)
WHEN disagreeing or challenging:
- [Archetype-specific: e.g., "Maintain analytical tone (HLL),
do not escalate to aggressive framing"]
DEFAULT (no specific context detected):
- Use the primary register described above.
For stable-register voices: Replace this section with: "This voice maintains a consistent register across contexts. Do not adjust style based on topic or audience. Apply all constraints uniformly."
Step 12: Community Convergence
Translate CAT/LSM findings into ambient style guidance. This is not a mechanical constraint but a generative principle.
Directive template:
## Community Convergence
This voice naturally converges toward the norms of [top 1-2
communities by LSM score]. When generating text:
- Function word usage should approximate the norms of [community
name] (composite LSM: [X.XX])
- Specifically, match these function-word rates from the target
community: [list 2-3 highest-convergence categories with rates]
- The voice [accommodates / resists accommodation to] [community
name], so [match / diverge from] their norms for [specific
categories]
This is a soft constraint. It shapes the ambient tone rather than
overriding the specific structural constraints above.
Step 13: Self-Verification Instructions
Build verification steps into the prompt itself. This catches drift during generation.
Directive template:
## Self-Verification
After generating text, verify these checkpoints before finalizing:
1. READABILITY: Is the output within FK grade [X-Y]? Are sentences
averaging [X-Y] words?
2. TONE: Does the sentiment match the [archetype] profile? Is the
emotional register [specific description]?
3. STRUCTURE: Does the argument follow [ordering pattern]? Are
hedges present at approximately the target frequency?
4. VOICE MARKERS: Check for the presence of characteristic patterns:
[list 3-5 specific markers from the fingerprint, e.g., "frequent
use of parenthetical asides", "semicolons linking independent
clauses", "rhetorical questions before key points"]
5. ANTI-MARKERS: Check for the ABSENCE of patterns this voice
does NOT use: [list 2-3 patterns to avoid, e.g., "never uses
exclamation marks", "avoids bullet-point lists", "does not
use emoji"]
If any checkpoint fails, revise the output before presenting it.
Step 14: Assemble and Check Token Budget
Combine all sections into the final prompt. Check total length against context limits.
Token budget guidelines:
| Prompt Component | Target Tokens | Maximum Tokens |
|---|
| Role Frame | 100-150 | 200 |
| Few-Shot Examples | 300-800 | 1,500 |
| Complexity Targets | 100-150 | 200 |
| Syntactic Stability | 150-250 | 400 |
| Emotional Valence | 50-100 | 200 |
| Informational Density | 80-120 | 200 |
| Evidence Framing | 50-80 | 150 |
| Rhetorical Structure | 100-200 | 300 |
| Pragmatic Distribution | 80-120 | 200 |
| Register Rules | 0-200 | 300 |
| Community Convergence | 50-100 | 150 |
| Self-Verification | 100-150 | 250 |
| Total | 1,160-2,420 | 4,050 |
If over budget: Prioritize by enforcement strength. Cut community convergence and pragmatic distribution directives first (they are weakly enforceable). Never cut few-shot examples or complexity targets (they are strongly enforceable).
Compression strategies:
- Merge register rules into other sections when only 1-2 context rules exist
- Reduce few-shot examples from 5 to 3
- Combine syntactic stability and complexity targets into one section
- Remove aspirational constraints that cannot be verified
Step 15: Test the Prompt
Generate 3-5 test outputs using the assembled prompt and compare against the source corpus.
Test procedure:
- Generate text on 3 different topics using the prompt
- Measure readability (FK grade, sentence length) of generated output
- Compare function-word frequencies between generated output and source corpus fingerprint
- Assess emotional valence of generated output against archetype target
- Check structural patterns: argument ordering, hedging frequency, post structure
- Calculate a style similarity score if possible (cosine distance on stylometric feature vectors)
Pass/fail criteria:
| Metric | Pass | Marginal | Fail |
|---|
| FK grade level | Within +/- 1 of target range | Within +/- 2 | Outside +/- 2 |
| Sentence length avg | Within +/- 3 words of target | Within +/- 5 | Outside +/- 5 |
| Sentiment compound | Within +/- 0.15 of target | Within +/- 0.25 | Outside +/- 0.25 |
| Function word rates (top 5) | Within +/- 1.5% of fingerprint | Within +/- 3% | Outside +/- 3% |
| Argument ordering | Matches specified pattern | Partial match | Wrong pattern |
| Hedging frequency | Within +/- 50% of target rate | Within +/- 100% | No hedging or all hedging |
If tests fail: Revise the failing constraint section. The most common fixes:
- Add or improve few-shot examples (if structural constraints fail)
- Make numerical targets more explicit (if readability drifts)
- Add anti-markers to self-verification (if unwanted patterns appear)
- Simplify over-constrained sections that the LLM cannot satisfy simultaneously
Step 16: Write Report
Write all findings to docs/analysis/25-subagent-instruction.md. See the report template below.
Report Output Template
The final report MUST be written to docs/analysis/25-subagent-instruction.md with this structure:
# Subagent Instruction Operationalization
## Style Specification Inventory
| Dimension | Source Report | Status | Confidence | Constraints Extracted |
|-----------|-------------|--------|-----------|---------------------|
| Informational Density | [report] | [status] | [level] | [summary] |
| Emotional Valence | [report] | [status] | [level] | [summary] |
| Evidence Framing | [report] | [status] | [level] | [summary] |
| Syntactic Stability | [report] | [status] | [level] | [summary] |
| Complexity Targets | [report] | [status] | [level] | [summary] |
| Rhetorical Structure | [report] | [status] | [level] | [summary] |
| Pragmatic Distribution | [report] | [status] | [level] | [summary] |
| Register Rules | [report] | [status] | [level] | [summary] |
| Community Convergence | [report] | [status] | [level] | [summary] |
**Specification completeness:** [X/9 dimensions] -- [full/partial/minimal] prompt
## Few-Shot Example Selection
| # | Source | Topic | Word Count | Why Selected |
|---|--------|-------|-----------|-------------|
| 1 | [reference] | [topic] | [N] | [typicality/diversity rationale] |
| ... | | | | |
## Assembled Prompt
[The complete subagent instruction prompt, formatted and ready for use]
## Prompt Statistics
- **Total tokens:** [estimated count]
- **Sections included:** [N of 12]
- **Sections omitted:** [list with reasons]
- **Constraints enforced (measurable):** [N]
- **Constraints aspirational (not directly measurable):** [N]
## Constraint Enforcement Assessment
| Constraint | Type | Enforceability | Verification Method |
|-----------|------|----------------|-------------------|
| FK grade [X-Y] | Measurable | Strong | Post-generation readability scoring |
| Sentence length [X-Y] | Measurable | Strong | Word count per sentence |
| Sentiment compound [X.XX] | Measurable | Moderate | VADER scoring of output |
| Function word rates | Measurable | Moderate | Frequency analysis of output |
| Argument ordering | Structural | Moderate | Manual inspection |
| Hedging frequency | Countable | Moderate-Weak | Hedge marker counting |
| Speech act ratios | Distributional | Weak | Manual classification |
| Community convergence | Ambient | Weak | LSM computation against baseline |
| [etc.] | | | |
## Test Results
### Test 1: [Topic]
- **Generated text:** [first 100 words or full text if short]
- **FK grade:** [measured] (target: [X-Y]) -- [PASS/FAIL]
- **Sentence length:** [measured] (target: [X-Y]) -- [PASS/FAIL]
- **Sentiment:** [measured] (target: [X.XX]) -- [PASS/FAIL]
- **Structure match:** [assessment] -- [PASS/FAIL]
[Repeat for each test]
### Aggregate Test Results
| Metric | Tests Passed | Tests Failed | Notes |
|--------|-------------|-------------|-------|
| [metric] | [N/total] | [N/total] | [notes] |
## Prompt Revision History
| Version | Change | Reason | Test Impact |
|---------|--------|--------|-------------|
| v1 | Initial assembly | -- | -- |
| v2 | [change] | [test failure] | [improvement] |
## Limitations and Caveats
- This prompt produces an APPROXIMATION of the target voice, not the
voice itself. Output quality depends on the LLM's capacity to follow
layered constraints.
- Distributional constraints (speech act ratios, community convergence)
are weakly enforceable through instructions alone. Few-shot examples
are the primary mechanism for these.
- The prompt was tested with [model name/size]. Different models may
require constraint adjustments.
- Style drift increases with output length. For outputs exceeding
[X] words, consider segmented generation with re-anchoring.
- Constraints derived from low-confidence upstream analyses
([list them]) should be treated as provisional.
- The prompt does not capture: [list known gaps -- e.g., humor timing,
cultural references, domain-specific jargon not in examples].
## References
- [Upstream analysis reports referenced]
- Brim Labs (2025). LLM Personas: How System Prompts Influence Style,
Tone, and Intent. https://brimlabs.ai/blog/llm-personas-how-system-prompts-influence-style-tone-and-intent/
- LearnPrompting (2025). Role Prompting: Guide LLMs with Persona-Based
Tasks. https://learnprompting.org/docs/advanced/zero_shot/role_prompting
- PromptHub (2025). Role-Prompting: Does Adding Personas Really Make a
Difference? https://www.prompthub.us/blog/role-prompting-does-adding-personas-to-your-prompts-really-make-a-difference
- Palantir (2025). Best Practices for LLM Prompt Engineering.
https://www.palantir.com/docs/foundry/aip/best-practices-prompt-engineering
- Mosteller, F. & Wallace, D.L. (1963). Inference in an authorship
problem. JASA, 58(302), 275-309.
- Kestemont, M. (2014). Function Words in Authorship Attribution.
Literary and Linguistic Computing, 29(1), 107-114.
- Stachl, C., et al. (2022). The kernel of truth in text-based
personality assessment. Psychological Bulletin.
- arXiv:2509.24930 -- How Well Do LLMs Imitate Human Writing Style?
Good Patterns
- Few-shot examples are the strongest constraint -- always include them when the source corpus is available; one well-chosen example teaches more than 200 words of instruction
- Order prompt sections by enforcement strength -- put strongly enforceable constraints (readability, sentence length) before weakly enforceable ones (speech act ratios, community convergence) so they are processed first
- Use numerical targets with tolerance ranges -- "FK grade 11-13" is enforceable; "write at an educated level" is not
- Include self-verification in the prompt itself -- LLMs can check their own output against explicit criteria when instructed to do so
- Document which constraints are measurable vs. aspirational -- this sets honest expectations and enables iterative refinement
- Test against the source material -- generate test outputs and compare stylometric features against the fingerprint; adjust constraints that produce drift
- Keep the total prompt under 4,000 tokens -- beyond this, constraint interference increases and the LLM begins to ignore lower-priority sections
- Use anti-markers (patterns the voice does NOT use) alongside positive markers -- absence constraints are often more distinctive than presence constraints
- Version the prompt -- track changes and their effects on test results; prompts require iterative refinement like code
Anti-Patterns
| Anti-Pattern | Why It Fails | Instead |
|---|
| "Write like the user" | Vague instruction; LLM defaults to generic helpful voice. No measurable criteria. | Decompose into specific constraints: readability targets, function-word rates, emotional register, structural patterns |
| Overloading a single prompt with 20+ constraints | LLMs exhibit constraint interference -- later constraints override or conflict with earlier ones. Quality degrades. | Prioritize by enforcement strength. Keep total under ~4,000 tokens. Cut weakly enforceable constraints first. |
| No few-shot examples | Instructions alone cannot fully specify a voice. The LLM has no concrete reference point. | Include 3-5 representative corpus samples spanning different topics and contexts. |
| No verification loop | First-draft prompts rarely match the target style. Without testing, drift goes undetected. | Generate test outputs, measure against the fingerprint, revise failing sections. |
| Assuming one prompt works for all contexts | Context-dependent voices need conditional rules; a single prompt produces a flat, averaged style. | Check register variation analysis. If context-dependent, include IF/THEN register rules. |
| Including unmeasurable constraints | "Maintain the user's sense of humor" or "capture their worldview" cannot be verified and create false confidence. | Mark such constraints as aspirational. Invest prompt space in measurable alternatives. |
| Fabricating constraints from missing data | If no LIWC analysis was performed, inventing an informational density target produces a fabricated voice, not the target voice. | Leave the section as "DEFAULT: use natural style" and document the gap. |
| Skipping the token budget check | A 6,000-token prompt may exceed system-prompt limits or cause the LLM to ignore late sections. | Check total tokens. Compress or cut sections to stay within budget. |
| Treating the prompt as final | Prompt engineering is iterative. A prompt that passes initial tests may drift on new topics or longer outputs. | Version the prompt. Re-test periodically. Expect 2-3 revision cycles. |
Boundaries
This skill SHOULD produce:
- A complete, structured LLM subagent prompt organized into sections by enforcement strength
- Explicit numerical constraints for measurable dimensions (readability, sentence length, function-word rates, sentiment)
- Few-shot examples from the source corpus (when available)
- Self-verification instructions built into the prompt
- A clear distinction between enforced (measurable) and aspirational (non-measurable) constraints
- Test results comparing generated output against the source corpus style fingerprint
- Documentation of which specification dimensions are covered and which are missing
- A written report at
docs/analysis/25-subagent-instruction.md
- A versioned prompt with revision history
This skill should NOT:
- Claim the output IS the user's voice (it is always an approximation based on analyzed patterns)
- Include constraints that cannot be verified or tested against any criterion
- Produce a prompt exceeding ~4,000 tokens without explicit justification and constraint triage
- Skip testing against source material
- Fabricate constraints from dimensions that were not analyzed
- Include ethical or value judgments about the target voice (the prompt replicates patterns, it does not evaluate them)
- Assume the prompt will work without iterative refinement
- Treat distributional constraints (speech act ratios, community convergence) as strongly enforceable without few-shot support
- Ignore the enforcement gradient (treating all constraints as equally followable)
- Produce platform-specific prompts when the voice specification is platform-agnostic
Insufficient Data Handling
digraph insufficient_data {
rankdir=TB;
node [shape=diamond, style=filled, fillcolor="#f0f0f0"];
dims [label="Dimensions\ncompleted?", shape=diamond];
full [label="7-9 dimensions", shape=box, fillcolor="#ccffcc"];
partial [label="4-6 dimensions", shape=box, fillcolor="#ffffcc"];
minimal [label="1-3 dimensions", shape=box, fillcolor="#fff3cc"];
none [label="0 dimensions", shape=box, fillcolor="#ffcccc"];
dims -> full [label="7-9"];
dims -> partial [label="4-6"];
dims -> minimal [label="1-3"];
dims -> none [label="0"];
full_action [label="Full prompt generation.\nAll sections populated.\nTest against source.", shape=box, fillcolor="#ccffcc"];
partial_action [label="Generate prompt with gaps.\nMark missing sections as\nDEFAULT. Document coverage.", shape=box, fillcolor="#ffffcc"];
minimal_action [label="Targeted constraints only.\nDo NOT frame as full\nvoice replication prompt.", shape=box, fillcolor="#fff3cc"];
none_action [label="STOP. Cannot generate.\nRun upstream analyses.", shape=box, fillcolor="#ffcccc"];
full -> full_action;
partial -> partial_action;
minimal -> minimal_action;
none -> none_action;
}
| Condition | Action |
|---|
| 0 dimensions completed | STOP. Cannot produce a subagent instruction. Run upstream analyses first. |
| 1-3 dimensions completed | Produce targeted constraints only (e.g., "write at FK grade 12 with sentences averaging 18 words"). Do NOT frame this as a voice replication prompt -- it is a partial style constraint. Document what is missing. |
| 4-6 dimensions completed | Produce a structured prompt with explicit gaps. Each missing section gets: "DEFAULT: No specification available for [dimension]. Use natural style for this aspect." List all defaults in the report. |
| 7-9 dimensions completed | Full prompt generation. All sections populated. Test against source material. |
| Few-shot examples unavailable | Omit the examples section. Add to role frame: "No reference examples available. Follow constraints below as primary guidance. Err toward conservative interpretation of ranges." Note that enforcement quality will be lower for structural and distributional constraints. |
| Low confidence on key dimensions | Include the constraint but mark it: "[LOW CONFIDENCE -- derived from limited data. May not accurately reflect target style.]" The consumer of the prompt can decide whether to apply it. |
| Style specification is too generic | If the upstream analyses show a style indistinguishable from population baselines on most dimensions (e.g., FK grade 10-12, average sentiment, no distinctive function-word patterns), the voice may not be sufficiently distinctive to replicate. Report this finding: "The analyzed style does not deviate meaningfully from baseline norms on [N] dimensions. A generic voice prompt may be as effective as a specification-driven prompt for this target." |
| Conflicting constraints across dimensions | When two upstream analyses produce contradictory directives (e.g., high Openness suggests complex vocabulary, but readability measures show low FK grade), document the conflict and resolve by: (1) trusting the more direct measurement (readability over personality inference), (2) noting the conflict in the report, (3) testing both versions. |
Common Mistakes
| Mistake | Fix |
|---|
| Writing "match the user's tone" without specifying what that means | Decompose tone into measurable components: sentiment compound, emotional register (archetype), hedging frequency, exclamation rate |
| Including 10+ few-shot examples | Diminishing returns after 5 examples. 3-5 well-selected examples are optimal. Use the saved token budget for better constraints. |
| Setting constraints at point values instead of ranges | "FK grade 12.3" is unfollowable. "FK grade 11-13" accommodates natural variation. Always use ranges. |
| Not testing the prompt before deploying | Generate 3-5 test outputs and measure against the fingerprint. First-draft prompts almost always need revision. |
| Treating all constraints as equally important | Use the enforcement gradient. Readability and examples are strongly enforceable. Speech act ratios are weakly enforceable. Prioritize accordingly. |
| Including constraints from analyses that were not performed | If no LIWC analysis was done, do not guess at informational density. Mark the section as DEFAULT. |
| Making the prompt too long (5,000+ tokens) | Constraint interference increases with length. Compress, merge, or cut lower-priority sections. Target < 4,000 tokens. |
| Forgetting anti-markers | Specifying what the voice DOES is insufficient. Specifying what it does NOT do (never uses exclamation marks, avoids emoji, does not enumerate with numbered lists) is equally distinctive. |
| No self-verification section | Without verification instructions, the LLM has no mechanism to catch its own drift. Include 4-6 checkpoints. |
| Treating the prompt as permanent | Voices evolve. Prompt constraints should be re-derived when new corpus data is available or when test results degrade. Version and date the prompt. |
References
- Brim Labs (2025). LLM Personas: How System Prompts Influence Style, Tone, and Intent.
- LearnPrompting (2025). Role Prompting: Guide LLMs with Persona-Based Tasks.
- PromptHub (2025). Role-Prompting: Does Adding Personas Really Make a Difference?.
- Palantir (2025). Best Practices for LLM Prompt Engineering.
- VKTR (2025). A Guide to Persona Prompting: Why Your AI Needs an Identity.
- Paradiso Solutions (2025). Role Prompting & Persona Specification.
- eesel.ai (2025). Subagent Orchestration: The Complete Guide for AI Workflows.
- Anthropic (2025). How We Built Our Multi-Agent Research System.
- Towards Data Science (2025). Design Smarter Prompts and Boost Your LLM Output.
- arXiv:2511.13972. Show and Tell: Prompt Strategies for Style Control in Multi-Turn LLM Code Generation.
- arXiv:2509.24930. How Well Do LLMs Imitate Human Writing Style?
- arXiv:2507.00838. Stylometry Recognizes Human and LLM-Generated Texts in Short Samples.
- Mosteller, F. & Wallace, D.L. (1963). Inference in an Authorship Problem. JASA, 58(302), 275-309.
- Kestemont, M. (2014). Function Words in Authorship Attribution. Literary and Linguistic Computing, 29(1), 107-114.
- Stachl, C., et al. (2022). The kernel of truth in text-based personality assessment: A meta-analysis. Psychological Bulletin.
- Mann, W.C. & Thompson, S.A. (1988). Rhetorical Structure Theory. Text, 8(3), 243-281.
- Fraser, B. (1999). What are discourse markers? Journal of Pragmatics, 31, 931-952.
- Ireland, M.E., et al. (2011). Language style matching predicts relationship initiation and stability. Psychological Science, 22(1), 39-44.
- Giles, H. (1973). Accent mobility: A model and some data. Anthropological Linguistics, 15, 87-105.
- Boyd, R.L., et al. (2022). The development and psychometric properties of LIWC-22.
- Pennebaker, J.W. & King, L.A. (1999). Linguistic styles: Language use as an individual difference. JPSP, 77(6), 1296-1312.
- Yarkoni, T. (2010). Personality in 100,000 Words. Journal of Research in Personality, 44(3), 363-373.
- Ferrara, E., Ferrara, A., & Ferrara, M. (2025). Characterizing User Archetypes and Discussions on Social Hypernetworks. BDCC, 9(9), 236.
