Subagent Instruction Operationalization

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.

Enforcement Gradient

Minimum Specification Completeness

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:

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:

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:

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:

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:

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:

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:

1. Generate text on 3 different topics using the prompt
2. Measure readability (FK grade, sentence length) of generated output
3. Compare function-word frequencies between generated output and source corpus fingerprint
4. Assess emotional valence of generated output against archetype target
5. Check structural patterns: argument ordering, hedging frequency, post structure
6. Calculate a style similarity score if possible (cosine distance on stylometric feature vectors)

Pass/fail criteria:

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

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;
}

…(truncated)