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Use at the start of any coding session in a repository to scan the structure, save a baseline, and materialize splayed-table memory for fast follow-up queries. Establishes the "before" reference point that the verify skill diffs against later.
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Basado en la clasificación ocupacional SOC
Use after a rescan to surface structural regressions across a time window. Diffs the latest scan against the saved baseline AND the trend history, ranking dimensions that worsened, files newly hot, and rules newly tripped. Configurable window (7d, 30d, 90d). Use periodically (daily, weekly, or pre-PR).
Use when the user explicitly asks for a structural audit, architecture review, dead-code report, test-gap analysis, or evolution hotspot scan. Heavier and noisier than the other raysense skills — only run on demand, not as part of the routine edit loop.
Use before refactoring, deleting, moving, or substantially modifying a file to compute its blast radius, coupling profile, and cycle exposure. Lets the agent edit with awareness of downstream effects rather than discovering them after the fact.
Use when the agent has a structural question about the saved baseline that the typed MCP tools (health, hotspots, rules, blast radius, coupling, cycles, evolution) do not directly answer. Runs Rayfall expressions against splayed baseline tables via raysense_baseline_query. Three modes are available - select queries for filter/project/aggregate (most common), .graph.* algorithms (PageRank, Louvain, topsort, shortest-path, betweenness, closeness, MST, k-shortest, BFS expand) for centrality and reachability over the call graph, and Datalog rules with transitive closure for declarative reachability ("reaches", "depends-on", "tainted-by"). Reach for this when the question shape is "files where X and Y," "most-central callers," "what does X transitively reach," or any custom slice across the 18 baseline tables.
Use after completing a logical chunk of edits to rescan the project and detect new rule violations or health regressions against the session baseline. Catches structural regressions before they reach review.
| name | bootstrap |
| description | Use at the start of any coding session in a repository to scan the structure, save a baseline, and materialize splayed-table memory for fast follow-up queries. Establishes the "before" reference point that the verify skill diffs against later. |
Run this once per session, before any non-trivial edits. It produces a
persisted baseline plus live splayed-table memory that the other
raysense skills (impact, verify,
audit) read against.
All tools take a path argument. Always pass the current working
directory as an absolute path so per-project state stays inside the
repo (<repo>/.raysense/).
raysense_health with path: <cwd>.
Note the overall grade and the worst dimension.raysense_baseline_save with
path: <cwd>. This writes <cwd>/.raysense/baseline/manifest.json
plus splayed tables under <cwd>/.raysense/baseline/tables/.raysense_memory_summary with
path: <cwd>. Report the row/column counts so the user can see the
memory is materialized.raysense_hotspots and
raysense_rules. List the three highest-traffic files and any
already-failing rules. These are the spots most likely to bite
during the session.After bootstrap, the agent should remember (briefly):
audit instead if
structural context is needed.git log -1 --since='1 hour ago' shows nothing).
Re-using the previous baseline is fine; just call raysense_health
for a fresh grade and skip the rest.The baseline this skill produces is queryable, not just a dump. After bootstrap, the agent has access to:
raysense_baseline_query -- run any Rayfall expression against a
saved table. Syntax and worked examples in the query
skill (select / .graph.* / Datalog / vector search).raysense_policy_check -- evaluate .rfl policy packs in
<repo>/.raysense/policies/; ships its own exit-code semantics for
CI gating.raysense_baseline_import_csv -- bring external CSVs (coverage,
lint counts, embeddings) into the baseline as queryable tables.