fix(dictation): @copilot everywhere, NLP histogram step, 256-term glossary

[Copilot]

The dictation skill and its generator workflow had ~1000 undifferentiated terms, no data-driven term selection, and used bare copilot instead of @copilot.

skills/dictation/SKILL.md

• Glossary trimmed from ~1000 → exactly 256 high-frequency, domain-curated terms
• Standalone copilot entry removed; @copilot was already present
• Speech-to-text rule corrected: "co-pilot" → @copilot

.github/workflows/dictation-prompt.md

• New Step 1 — precomputed Python NLP word-frequency histogram; agent uses it as the primary source for term selection rather than guessing:

  python3 - <<'EOF'
  import re
  from pathlib import Path
  from collections import Counter
  docs = Path("docs/src/content/docs")
  tokens = Counter()
  for md_file in docs.rglob("*.md"):
      text = md_file.read_text(errors="replace")
      tokens.update(re.findall(r'`([^`\n]+)`', text))
      tokens.update(re.findall(r'\b([\w][\w\-\.]{2,}[\w])\b', text))
  for tok, n in tokens.most_common(500):
      if len(tok) > 2:
          print(f"  {n:5d}  {tok}")
  EOF

• Old steps renumbered (1→2, 2→3, 3→4)
• Engine reference copilot → @copilot in focus-area list
• Term target updated to 256 (240–270 acceptable) throughout mission, guidelines, and success criteria

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[Copilot]

Pull request overview

Note

Copilot was unable to run its full agentic suite in this review.

This PR updates the dictation skill guidance and glossary to use @copilot consistently, shrink the glossary to a curated 256 terms, and introduce a data-driven (histogram-based) term selection step for the generator workflow.

Changes:

• Trim skills/dictation/SKILL.md glossary to 256 terms and correct "co-pilot" → @copilot.
• Add an NLP word-frequency histogram step to .github/workflows/dictation-prompt.md to guide term selection.
• Update workflow guidance to target 256 terms and switch engine mention to @copilot.

Show a summary per file

File	Description
skills/dictation/SKILL.md	Reduces/curates glossary entries and updates speech-to-text correction to @copilot.
.github/workflows/dictation-prompt.md	Adds histogram step and updates mission/guidelines to use @copilot and the 256-term target.

Copilot's findings

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• Files reviewed: 2/2 changed files
• Comments generated: 7

[Copilot]

The workflow prompt is internally inconsistent: the mission requires 'exactly 256' terms, but the guidelines/success criteria allow a 240–270 range. Please make these consistent (either enforce exactly 256 everywhere, or change the mission text to reflect the acceptable range) so agents and reviewers have a single clear target.

Suggested change

	1. Contains a glossary of exactly 256 project-specific terms extracted from documentation
	1. Contains a glossary of 240–270 project-specific terms extracted from documentation

[Copilot]

The workflow prompt is internally inconsistent: the mission requires 'exactly 256' terms, but the guidelines/success criteria allow a 240–270 range. Please make these consistent (either enforce exactly 256 everywhere, or change the mission text to reflect the acceptable range) so agents and reviewers have a single clear target.

[Copilot]

The workflow prompt is internally inconsistent: the mission requires 'exactly 256' terms, but the guidelines/success criteria allow a 240–270 range. Please make these consistent (either enforce exactly 256 everywhere, or change the mission text to reflect the acceptable range) so agents and reviewers have a single clear target.

[Copilot]

The text says the histogram is of 'code-formatted tokens', but the script also counts non-backticked words via the second regex (which will pull in many generic English words). Either (mandatory) update the description to reflect that it includes non-code tokens, or (preferred) change the script to only count code-formatted identifiers (e.g., backticks and/or fenced code blocks) to better match the stated intent and reduce noise.

[Copilot]

The text says the histogram is of 'code-formatted tokens', but the script also counts non-backticked words via the second regex (which will pull in many generic English words). Either (mandatory) update the description to reflect that it includes non-code tokens, or (preferred) change the script to only count code-formatted identifiers (e.g., backticks and/or fenced code blocks) to better match the stated intent and reduce noise.

Suggested change

	# Collect backtick-quoted technical tokens
	tokens.update(re.findall(r'`([^`\n]+)`', text))
	# Also collect hyphenated/dotted/underscored identifiers
	tokens.update(re.findall(r'\b([\w][\w\-\.]{2,}[\w])\b', text))
	# Collect inline code tokens
	tokens.update(re.findall(r'`([^`\n]+)`', text))
	# Collect identifier-like tokens from fenced code blocks
	for block in re.findall(r'```[^\n]*\n(.*?)```', text, flags=re.DOTALL):
	tokens.update(re.findall(r'\b([A-Za-z_][\w\-\.]{1,}[A-Za-z0-9_])\b', block))

[Copilot]

For deterministic results across environments, it’s better to specify an explicit encoding when reading documentation files (e.g., UTF-8). Relying on the platform default encoding can change tokenization/histogram output between runners.

Suggested change

	text = md_file.read_text(errors="replace")
	text = md_file.read_text(encoding="utf-8", errors="replace")

[Copilot]

The len(tok) > 2 filter is redundant given the second regex enforces a minimum token length already, and the backtick regex will include multi-word phrases that may not be desired. Consider making the filtering criteria explicit (e.g., filter out whitespace-containing backtick matches, or consolidate filtering in one place) so the histogram output better matches the stated goal of 'project tokens'.

Suggested change

	# Collect backtick-quoted technical tokens
	tokens.update(re.findall(r'`([^`\n]+)`', text))
	# Also collect hyphenated/dotted/underscored identifiers
	tokens.update(re.findall(r'\b([\w][\w\-\.]{2,}[\w])\b', text))
	print("Frequency histogram — top 500 project tokens:")
	for tok, n in tokens.most_common(500):
	if len(tok) > 2:
	print(f" {n:5d} {tok}")
	# Collect backtick-quoted technical tokens, excluding multi-word phrases
	tokens.update(
	tok
	for tok in re.findall(r'`([^`\n]+)`', text)
	if len(tok) > 2 and not re.search(r'\s', tok)
	)
	# Also collect hyphenated/dotted/underscored identifiers
	tokens.update(re.findall(r'\b([\w][\w\-\.]{2,}[\w])\b', text))
	print("Frequency histogram — top 500 project tokens:")
	for tok, n in tokens.most_common(500):
	print(f" {n:5d} {tok}")