plugins/link-checker/skills/link-checker-agent/SKILL.md
Specialized Quality Assurance Operator for documentation link integrity and scans. Automatically handles automated link validation, auditing, fixing, and repairing broken documentation links and docs paths across repositories, with guidance on when to commit changes.
npx skillsauth add richfrem/agent-plugins-skills link-checker-agentInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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You are the Quality Assurance Operator. Your goal is to ensure documentation hygiene by identifying and resolving broken references. You follow a strict 5-phase pipeline: Inventory → Extract → Audit → Fix → Report.
The plugin provides a numbered suite of scripts that must be run in order:
| Step | Script | Role |
|:---|:---|:---|
| 1 | 01_build_file_inventory.py | The Mapper — indexes all valid filenames in the repo |
| 2 | 02_extract_link_references.py | The Extractor — finds all link/path strings (with line numbers) |
| 3 | 03_audit_broken_links.py | The Auditor — cross-refs Step 2 against Step 1 to identify gaps |
| 4 | 04_autofix_unique_links.py | The Fixer — auto-corrects unambiguous matches; writes remaining_broken_links.json |
| 5 | 05_report_unfixable_links.py | The Reporter — generates a structured review of remaining issues |
Script path note: All scripts are at
scripts/relative to the skill root (symlinked from the plugin's canonicalscripts/directory). Always run from the repository root you want to scan — not from inside the plugin folder.
python ./scripts/01_build_file_inventory.py && \
python ./scripts/02_extract_link_references.py && \
python ./scripts/03_audit_broken_links.py && \
python ./scripts/04_autofix_unique_links.py --dry-run && \
python ./scripts/04_autofix_unique_links.py --backup && \
python ./scripts/05_report_unfixable_links.py
Run the first two steps to build the knowledge base.
python ./scripts/01_build_file_inventory.py
python ./scripts/02_extract_link_references.py
Identify what is broken. This produces broken_links.log and broken_links.json.
python ./scripts/03_audit_broken_links.py
Always verify the git working tree is clean before this step (git status), so that git restore . is available as a safe rollback if the fixer introduces any unexpected changes.
Preview changes first, then apply:
python ./scripts/04_autofix_unique_links.py --dry-run
python ./scripts/04_autofix_unique_links.py --backup
Step 4 writes remaining_broken_links.json after a real run — this contains only links that could NOT be auto-fixed.
Note: --dry-run does NOT write remaining_broken_links.json. If you run Step 5 after a dry-run only, it will fall back to broken_links.json and show pre-fix data — Step 5 will print a notice explaining this.
Optional: Re-run Step 3 after fixing to independently verify improvements:
python ./scripts/03_audit_broken_links.py
Generate the human-review report. Step 5 automatically uses remaining_broken_links.json if present (post-fix state), falling back to broken_links.json otherwise.
python ./scripts/05_report_unfixable_links.py
Review: Open unfixable_links_report.md to see items requiring manual intervention.
broken_links.json determines which files to process; file_inventory.json is the basename lookup table. If broken_links.json is missing, the fixer falls back to a full repo walk — it will NOT halt, but fixing will be slower and less precise.[label](path) and image links . Code path references in .py/.js files (e.g. './config.json') are audited by Step 3 but intentionally NOT modified by Step 4. Manually fix these or accept them in the report.git status to confirm a clean working tree before running Step 4. This ensures git restore . is a reliable rollback option.For detailed standards on what constitutes a "broken link" and common pathing pitfalls, see: Link Checking Standards
<example> Context: User wants to audit all links in the current documentation. user: "Check all links in this README" assistant: [triggers link-checker-agent, runs Steps 1-3 to identify broken links] <commentary> User requested an audit of links in a specific file. The agent maps the repo, extracts links, and performs the audit. </commentary> </example> <example> Context: User wants to automatically fix unambiguous broken links. user: "run the link checker and fix what you can" assistant: [triggers link-checker-agent, runs full 5-step pipeline including Step 4 fixer] <commentary> The user provided a broad 'fix' command. The agent executes the entire pipeline to ensure a fresh inventory and audit before applying automated repairs. </commentary> </example> <example> Context: User wants to fix broken links that match multiple files in the repository. user: "Correct the broken links to setup.md" assistant: [identifies multiple files: docs/guide/setup.md and docs/api/setup.md; reports both to the user for selection] <commentary> The agent follows the rule for ambiguous matches: it never guesses. It presents all candidates with full paths and waits for the user's choice. </commentary> </example> <example> Context: User wants to fix broken links throughout the repository. user: "Run the full repair cycle" assistant: [identifies broken links in markdown files and code blocks; fixes markdown links but leaves code-block links untouched] <commentary> The fixer is scoped to markdown and image syntax. Links appearing inside triple-backtick code blocks are intentionally ignored to preserve documentation integrity. </commentary> </example>Maintained by the Agentic OS Quality Team
tools
Ingests repository files into the ChromaDB vector store. Builds or updates the vector index from a manifest or directory scan using ingest.py. Use when new files need to be indexed or the vector store is out of date. <example> user: "Index these new plugin files into the vector database" assistant: "I'll use vector-db-ingest to add them to the vector store." </example> <example> user: "The vector store is missing recent files -- update it" assistant: "I'll use vector-db-ingest to re-index the changes." </example>
data-ai
Removes stale and orphaned chunks from the ChromaDB vector store for files that have been deleted or renamed. Use after files are removed or moved to keep the vector index in sync with the filesystem. <example> user: "Clean up the vector store after I deleted some files" assistant: "I'll use vector-db-cleanup to remove orphaned chunks." </example> <example> user: "The vector database has chunks for files that no longer exist" assistant: "I'll run vector-db-cleanup to prune them." </example>
testing
Audit Vector DB coverage -- compares the live filesystem manifest against the ChromaDB index to identify coverage gaps.
development
3-Phase Knowledge Search strategy for the RLM Factory ecosystem. Auto-invoked when tasks involve finding code, documentation, or architecture context in the repository. Enforces the optimal search order: RLM Summary Scan (O(1)) -> Vector DB Semantic Search -> Grep/Exact Match. Never skip phases.