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Skill engine: weighted scoring, negative patterns, context guards, max 3 cap
Replace binary keyword matching with confidence-scored suggestions.
Phrases carry weights (0.0-1.0), negative patterns suppress false
positives, and context guards require confirming words for low-confidence
matches. Results ranked by score and capped at 3 per prompt.
Copy file name to clipboardExpand all lines: .devcontainer/CHANGELOG.md
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### Changed
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#### Skill Engine: Auto-Suggestion
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-**Weighted scoring** — Skill suggestion phrases now carry confidence weights (0.0–1.0) instead of binary match/no-match. Specific phrases like "build a fastapi app" score 1.0; ambiguous phrases like "start building" score 0.2
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-**Negative patterns** — Skills can define substrings that instantly disqualify them. Prevents `fastapi` from triggering when discussing `pydantic-ai`, and `docker` from triggering for `docker-py` prompts
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-**Context guards** — Low-confidence matches (score < 0.6) require a confirming context word elsewhere in the prompt. "health check" only suggests `docker` if "docker", "container", or "compose" also appears
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-**Ranked results, capped at 3** — Suggestions are sorted by score (then priority tier), and only the top 3 are returned. Eliminates 6+ skill suggestion floods
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-**Priority tiers** — Explicit commands (priority 10) outrank technology skills (7), which outrank patterns (5) and generic skills (3) when scores tie
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#### Claude Code Installation
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-**Claude Code now installs as a native binary** — uses Anthropic's official installer (`https://claude.ai/install.sh`) via new `./features/claude-code-native` feature, replacing the npm-based `ghcr.io/anthropics/devcontainer-features/claude-code:1.0.5`
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-**In-session auto-updater now works without root** — native binary at `~/.local/bin/claude` is owned by the container user, so `claude update` succeeds without permission issues
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### Auto-Suggestion
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The `skill-suggester.py` hook matches user prompts against keyword maps defined in each skill. When a match is found, it injects a suggestion via `additionalContext` so Claude knows a relevant skill is available.
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The `skill-suggester.py` hook scores user prompts against keyword maps for each skill using weighted matching. Suggestions are ranked by confidence and capped at **3 skills maximum** per prompt.
-**Terms** — Single keywords (e.g., "FastAPI", "pytest")
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Each skill defines:
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-**Phrases** — `(substring, weight)` tuples. Weight 0.0–1.0 reflects specificity (e.g., `("build a fastapi app", 1.0)` vs `("pydantic model", 0.3)`)
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-**Terms** — Whole-word regex patterns, all scored at 0.6
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-**Negative patterns** — Substrings that instantly disqualify a skill (e.g., `"pydanticai"` suppresses `fastapi`)
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-**Context guards** — Required co-occurring words for low-confidence matches. When the best score is below 0.6, at least one guard word must appear in the prompt or the match is dropped
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