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Find dead code, complexity hotspots, and refactoring candidates in indexed source code.
Passed 8 of 14 test cases with the skill, and 6 of 14 without it.
and 3 more in the full report
5 fixed8 passed either way7 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
We just connected a repository named 'auth-service' and want a high-level graph overview of its node counts, edge counts, and process structures before running any detailed refactoring tools. What call should be made?
| Checked | Without skill | With skill |
|---|---|---|
| Calls get_repository_stats with repo_id set to 'auth-service' | Missed | Passed |
The instructions your model reads when the skill loads.
Code quality via graph analysis — dead code, complexity hotspots, repository stats.
| Tool | Purpose | Required | Key optional |
|---|---|---|---|
find_dead_code | Zero-caller symbols | repo_id | include_tests, limit, kinds[] |
find_most_complex_functions | Ranked complexity hotspots | repo_id | top_n (def 20) — not limit |
calculate_cyclomatic_complexity | Score one symbol | repo_id, target | — |
get_repository_stats | Repository overview | repo_id | branch |
Parameter types: numbers must be JSON numbers —
limit: 20, never"20"(MCP error -32602).
Full parameter spec for every Memtrace tool: references/mcp-parameters.md (bundled at the memtrace-skills plugin root).
{ "repo_id": "memdb" }
{ "repo_id": "memdb", "include_tests": false, "limit": 50 }
Exported symbols and entry points excluded by default. Results are candidates, not proof — callers via dynamic dispatch or reflection are invisible to the graph; verify before deleting.
{ "repo_id": "memdb", "top_n": 10 }
Ordered by call-graph out-degree.
test cases passed, vs 6 / 14 without
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