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create-sentence-markup

Grammarly-like sentence decomposition with NVIS confidence colors. Decomposes question text into annotated fragments showing which entities resolved, which are misspelled, which are fabricated, and which are unknown. Composes /extract-entities for grounding data and /interview for "Did you mean?" clarify prompts.

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grahama1970/agent-stack-public
Dernière activité de la source
24 septembre 2026 à 15:51
Langue détectée de SKILL.md
anglais
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SKILL.md
Instructions source · Aperçu en lecture seule
name
create-sentence-markup
description
Grammarly-like sentence decomposition with NVIS confidence colors. Decomposes question text into annotated fragments showing which entities resolved, which are misspelled, which are fabricated, and which are unknown. Composes /extract-entities for grounding data and /interview for "Did you mean?" clarify prompts.
allowed-tools
["Bash","Read"]
triggers
["markup sentence","annotate sentence","sentence markup","create sentence markup","grammarly","highlight entities"]
metadata
{"short-description":"NVIS-colored sentence annotation from entity grounding","author":"Claude","version":"0.1.0"}
provides
["sentence-markup"]
composes
["extract-entities","interview","agentic-evals"]
disciplines
["extraction","ui-design-engineering"]
# /create-sentence-markup Grammarly-like sentence decomposition with NVIS confidence colors (MIL-STD-3009). ## Usage ```bash # Annotate a question — returns JSON annotations ./run.sh annotate "How does SPARTA control X23-MUSTARD mitigate spoofing?" # Annotate with rendered markdown output ./run.sh annotate "How does the SPRTA framework work?" --format markdown # Annotate with HTML output (NVIS colors) ./run.sh annotate "What countermesures protect firmware?" --format html # Pipe from /extract-entities (skip redundant extraction) ./run.sh annotate --entities-json entities.json "How does X23-MUSTARD work?" ``` ## Output (JSON default) ```json { "text": "How does SPARTA control X23-MUSTARD mitigate spoofing?", "annotations": [ { "term": "X23-MUSTARD", "level": "RED", "label": "fabricated ID — not in corpus", "action": "reject", "closest_match": "CM0028" }, { "term": "SPARTA", "level": "GREEN", "label": "confirmed framework", "action": null } ], "summary": { "total_annotations": 2, "red": 1, "amber": 0, "yellow": 0, "green": 1, "needs_clarify": 0, "needs_reject": 1 } } ``` ## NVIS Color System (MIL-STD-3009) | Level | Color | RGB | Meaning | Action | |-------|-------|-----|---------|--------| | GREEN | Green | (0,255,136) | Exact match — confirmed in corpus | None | | AMBER | Amber | (255,170,0) | Fuzzy match or misspelling | /memory clarify via /interview | | RED | Red | (255,68,68) | Fabricated ID — not in corpus | Reject | | YELLOW | Yellow | (255,230,0) | Term not found anywhere | Investigate | ## Composition - **Input**: Question text (string) - **Depends on**: `/extract-entities` → `get_annotations()` for grounding data - **Triggers**: `/interview` for AMBER "Did you mean?" clarify prompts - **Consumed by**: `/create-evidence-case` (report grounding), `/ask` (inline annotations), `/lean4-prove` (proof obligations from RED/YELLOW annotations) ## Output Formats | Format | Flag | Use case | |--------|------|----------| | JSON | `--format json` (default) | Machine-readable, piping to other skills | | Markdown | `--format markdown` | Agent/human readable in terminal | | HTML | `--format html` | Rich rendering with NVIS colors |
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