Automate dead symbol adoption across codebases with graph queries and gate checks.
npx skills add https://github.com/optave/ops-codegraph-tool --skill titan-grind
Please help me install this Agent Skill. Skill: titan-grind Source: https://github.com/optave/ops-codegraph-tool/tree/main/.claude/skills/titan-grind Command: npx skills add https://github.com/optave/ops-codegraph-tool --skill titan-grind
The Titan Grind phase automates the identification and adoption of extracted dead symbols across a codebase, reducing noisy dead-code drift and aligning code with the evolving forge outputs.
Invoke titan-grind to process the next forge phase and report progress.
High-intent search queries and answers about installing and using this skill.
Automating dead code removal after refactoring involves identifying extracted dead symbols and wiring legitimate adopters into consumers. This process uses graph queries and diffs to classify dead helpers, ensuring safe, semantically identical transformations across the codebase.
Classifying dead symbols during a codebase refactor involves categorizing extracted helpers into adopt, re-export, promote, false-positive, intentionally-private, or remove. This classification uses per-target state persistence to track and safely process each symbol during the grind phase.
Wiring extracted helpers into existing consumers requires analyzing code changes and applying gate checks to ensure semantic equivalence. The process automatically connects approved helpers across multiple files, maintaining behavior while eliminating dead code drift from forge outputs.
Automated dead symbol adoption supports resumable transformations through per-target state persistence. This strict process tracks each target's classification and adoption status, allowing the grind phase to safely resume if interrupted during codebase refactoring.
Codegraph analysis for dead code elimination is needed during grind phases following forge commits. When extracted helpers create dead symbols across a codebase, graph queries and diffs identify candidates for adoption or removal to reduce noisy dead-code drift.
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