Run the Solutions Analyzer scripts: mapper, documentation generator, ASIM browser. Use when: running solution analyzer, generating CSVs, generating docs, refreshing caches, force-refresh, invalidating caches, generating ASIM browser, running mapper, running collect_table_info, running collect_asim_fields.
npx skills add https://github.com/Azure/Azure-Sentinel --skill run-solution-analyzer
This skill covers running the Solutions Analyzer locally against this clone of
Azure-Sentinel. Each script writes CSVs or docs to a directory you choose; the
scripts themselves are agnostic to where that directory lives.
If your workflow includes publishing the generated CSVs/docs to additional
repositories, GitHub Pages, or Kusto, those targets are user-specific and
should be recorded in agent memory (workspace or user scope) rather than in
this skill.
| Script | Purpose | Invocation |
| ------------------------------------ | ------------------------------------------------------------------ | ------------------------------------------------- |
| map_solutions_connectors_tables.py | Mapper: generates all CSV data | Run directly |
| generate_connector_docs.py | Doc generator: markdown + HTML pages, static + interactive indexes | Run directly |
| generate_asim_browser.py | ASIM browser: interactive HTML schema browser | Run directly |
| upload_to_kusto.py | Upload CSVs to Azure Data Explorer (Kusto) cluster | Run directly |
| collect_table_info.py | Fetch table reference data from Microsoft docs | Auto-invoked by mapper (--force-refresh=tables) |
| collect_asim_fields.py | Fetch ASIM field/schema definitions from Microsoft docs | Auto-invoked by mapper (--force-refresh=asim) |
| generate_interactive_docs.py | Generate index.html and HTML entity pages | Auto-invoked by generate_connector_docs.py |
All scripts run from Tools/Solutions Analyzer.
By default the mapper writes CSVs into Tools/Solutions Analyzer/ in the
current branch. The doc generator and ASIM browser require an explicit
--output-dir so they never pollute the source tree.
origin/masterAlways do this before any analyzer run. A working branch that has drifted
from master makes the mapper analyze a stale source tree — every connector
merged to master after the divergence point can appear as a new "added"
connector in subsequent diffs, even though no mapper logic changed.
git fetch origin master
git merge origin/master --no-edit
git merge, not git rebase, so subsequentgit log --first-parent origin/master analysis (used by compare_runs.py
and friends) still reflects merge dates correctly.
may have moved.
After merging, verify the working tree is clean of unintended changes:
git status --short | Select-Object -First 20
The mapper's analysis cache is keyed by (analysis_type, file_path) and
stores the source file mtime. When a source file is modified (including via
git merge), its mtime changes and the entry is automatically re-analyzed on
the next run. Source-content changes do NOT require --force-refresh.
--force-refresh exists for two distinct reasons:
results for the affected analysis types so they get recomputed from current
source. This is the local-only case.
schema fields from external sources. This is the network case and is
reserved for explicit full refresh.
| Type | Triggers network fetch? | When to refresh |
| ------------- | ---------------------------------------------------------- | ------------------------------------------------------ |
| parsers | No | Mapper parser-analysis logic changed |
| solutions | No | Mapper solution-analysis logic changed |
| standalone | No | Mapper standalone-content logic changed |
| asim | Yes — re-runs collect_asim_fields.py against MS docs | ASIM analyzer logic changed AND network refresh wanted |
| marketplace | Yes — Azure Marketplace API | Marketplace data is stale |
| tables | Yes — re-runs collect_table_info.py against MS docs | Table reference info is stale |
Note: asim is hybrid — it invalidates the local ASIM-parser analysis cache
and re-runs collect_asim_fields.py. There is currently no flag to do
only the local half.
git merge from masterDefault: just run the mapper with no flags. Mtime-based invalidation re-analyzes
every file that changed during the merge:
python map_solutions_connectors_tables.py
Network-fetched CSVs (tables_reference.csv, asim_fields.csv,
asim_entity_fields.csv, asim_logical_types.csv,
asim_vendors_products.csv, marketplace cache) will be reused from cache.
That's expected — they shouldn't drift just because master moved forward.
Use only when explicitly asked to do a full/clean run, when "invalidate all
caches" is requested, or when analyzer logic was modified across multiple
analysis types.
cd "Tools/Solutions Analyzer"
python map_solutions_connectors_tables.py --force-refresh=all
--force-refresh=all clears all 6 cache types (asim, parsers, solutions,standalone, marketplace, tables) AND re-runs collect_asim_fields.py and
collect_table_info.py against the network.
--force-refresh=all-offline clears parsers, solutions, standalone,AND asim (which still re-runs collect_asim_fields.py despite the name;
only marketplace/tables are skipped).
--force-refresh=parsers,solutions,standalone (purelylocal, no network calls).
isBackground: false and timeout: 0.Select-Object piping).Known issue (as of 2026-04-28): collect_asim_fields.py fails with HTTP
404 because the Microsoft Sentinel docs were migrated out of
MicrosoftDocs/azure-docs. The mapper logs a warning and proceeds, leaving
asim_fields.csv, asim_entity_fields.csv, asim_logical_types.csv, and
asim_vendors_products.csv empty/stale. Until the script is updated to fetch
from the new source, avoid --force-refresh=all and --force-refresh=asim
unless you accept stale ASIM schema CSVs.
python generate_connector_docs.py --output-dir <DOCS_DIR> [--skip-input-generation] [--html-output-dir <HTML_DIR>] [--html-docs-path <REL_PATH>] [--html-index-url <URL>]
--output-dir — mandatory, never omit.--skip-input-generation — skip the embedded mapper invocation when CSVsare already fresh.
--html-output-dir, --html-docs-path, --html-index-url — control whereindex.html and the interactive entity pages are written and how they
link back to the index. These are typically only set when publishing to a
static-site / GitHub Pages location; defaults are sensible for a local
preview.
isBackground: false and timeout: 0. Do NOT truncate output.python generate_asim_browser.py --output-dir <DIR> [--docs-base-path <REL_PATH>] [--index-url <URL>] [--link-extension <EXT>]
--docs-base-path and --index-url make sense only when the browser is
deployed alongside an interactive docs index. For a standalone local
preview, --output-dir alone is enough.
Run mapper before generating docs if:
overrides/ folder was modified.Otherwise, just run the doc generator with --skip-input-generation.
Tools/Solutions Analyzer/.cache/Tools/Solutions Analyzer/.logs/Tools/Solutions Analyzer/.logs/map_solutions_connectors_tables.logThe mapper writes timestamped progress lines ([ N.Ns] ...) to its own log
file in real time. To see live status during a run, tail that file directly:
Get-Content "Tools/Solutions Analyzer/.logs/map_solutions_connectors_tables.log" -Tail 12
**Do NOT rely on ... | Tee-Object -FilePath <log> | Select-Object -Last N
for live progress.** PowerShell pipelines buffer until the upstream command
finishes, so the Tee'd file stays empty until the entire run completes
(including the post-processing/CSV-write phase, which can be many minutes
after the per-parser progress lines were emitted). The mapper's own
.logs/map_solutions_connectors_tables.log is unbuffered and updates within
seconds.
python upload_to_kusto.py -c <cluster-url> -d <database> --solution-analyzer --source-dir ./
--solution-analyzer uploads the standard set of CSVs defined inSOLUTION_ANALYZER_FILES.
--source-dir ./ uses local CSVs instead of downloading from GitHub.--source-dir, downloads CSVs from the public Azure-SentinelGitHub repo.
--dry-run shows what would be uploaded without making changes.az login).The cluster URL and database name are environment-specific and not part of
this skill.
If you maintain a publishing pipeline (e.g. CSV output worktree, separate
docs repository, GitHub Pages site, specific Kusto cluster), record the
concrete paths, URLs, and the order of copy/commit/push steps in agent
memory — for example, in /memories/repo/ for a workspace-scoped note or in
/memories/ for a cross-workspace one. The agent should consult that memory
to resolve any placeholders above before running.
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
Take azure/run-solution-analyzer from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.