Dhdna Profiler

DHDNA Profiler — Text Pattern Annotation

An exploratory rubric for describing a supplied text using Digital Human DNA (DHDNA) terminology. Its cognitive-fingerprint language is a framework metaphor; it does not establish a unique, stable, or identifiable psychological signature.

Use this workflow for requested reflection on writing. It requires no package, credentials, API call, or MindBook account. It does not reproduce MindBook's scoring engine.

Sources and scope

The current publisher page names twelve public dimensions as a design vocabulary. The table below maps those names to the existing skill's annotation labels. The observation criteria and score anchors are local conventions, not an upstream validated scoring instrument.

The DHDNA preprint, DHDNA: A Framework for Ethical Digital Identity as Inheritable Heritage (February 23, 2026), states in section 6.3 that validation was limited to internal testing. The IDNA v2 preprint, Toward a Unified Theory of Digital Consciousness (February 27, 2026), proposes the tension pairs in section 4.3/Table 3 and the temporal-attractor model in section 2. Neither source establishes psychometric validity for this skill's annotations. A DOI, product demonstration, or journal submission is not evidence of such validity.

Describe observable reasoning and rhetorical choices. Genre, task, language proficiency, editing, collaboration, and AI assistance can change those choices. Do not infer intelligence, diagnosis, honesty, latent emotions, or a person's stable cognitive architecture from them.

The 12 annotation dimensions

The 6 proposed tension pairs

The IDNA preprint proposes these pairings. They are prompts for comparison, not established negative correlations or complementary scales. Rate both independently; both may be high, low, or N/A. Do not derive one score by subtracting the other from ten.

Workflow

1. Establish the sample

Use only the text designated for this request. Record a sample label, paragraph or line references, genre, purpose, language, and known editing context. Mark missing context unknown. Distinguish the author's assertions from quoted speech, fictional characters, and copied material; do not attribute all voices to the author.

If the user explicitly requests analysis of specified conversation turns, state that source scope and proceed. If the request leaves the source ambiguous, ask which text to use before expanding to earlier conversations or unrelated files.

2. Collect evidence

For each dimension, identify specific quotations and how they support the observation. Include contrary evidence where present. Distinguish lack of expression from lack of opportunity to express it. Do not manufacture quotations or use a fictional narrator as evidence about the writer's personality.

3. Annotate all twelve dimensions

Use qualitative observations by default. If numeric scoring is requested, use whole numbers on the local 1–10 rubric with these declared anchors:

• 1–3: Relevant but limited or weakly developed expression in this sample.
• 4–7: Explicit, developed expression with some supporting context.
• 8–10: Sustained, elaborated expression across several relevant passages.
• N/A: No suitable evidence or no opportunity to assess; never impute zero or a midpoint.

Explain each numeric choice against the dimension's evidence criterion. These are ordinal judgments, not equal-interval measurements, percentiles, calibrated probabilities, or rankings of ability. Short samples may warrant only a few observations and many N/A entries. Do not force all scores or a dominant pattern.

Attach annotation confidence to each observation: HIGH for multiple unambiguous passages, MEDIUM for a clear but limited example, LOW for ambiguous evidence. Confidence concerns the textual interpretation, not a stable trait. If uncertainty prevents a defensible observation, use N/A instead of speculative scoring.