Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/twells89/sigma-migration-skillsnpx agentmods add skills/twells89/sigma-migration-skills/microstrategy-assessmentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/twells89/sigma-migration-skills/microstrategy-assessment)<a href="https://agentmods.dev/skills/twells89/sigma-migration-skills/microstrategy-assessment"><img src="https://agentmods.dev/badge/skills/twells89/sigma-migration-skills/microstrategy-assessment/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/twells89/sigma-migration-skills/microstrategy-assessment"><img src="https://agentmods.dev/badge/skills/twells89/sigma-migration-skills/microstrategy-assessment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00108 | $0.01031 |
| Opus 5 | $0.00054 | $0.00515 |
| Sonnet 5 | $0.00022 | $0.00206 |
| Haiku 4.5 | $0.00011 | $0.00103 |
Grade A, and why
microstrategy-assessment scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MicroStrategy Assessment
Surveys a MicroStrategy (Strategy One) environment via its REST API and
produces a JSON inventory + markdown readout. The differentiator versus a
generic BI audit is converter-coverage classification: every dossier's
visualizations are scored against the same viz-type lookup the
microstrategy-to-sigma converter actually applies
(../microstrategy-to-sigma/refs/viz-type-mapping.md), so the readout
reflects what the tool will really do.
Read-only. Only
GETs against the MicroStrategy API (loginPOSTaside, which creates a session, nothing else). It never modifies, executes, or deletes anything in MicroStrategy, never runs a warehouse query, and never touches Sigma. SeePRIVACY.mdfor the full disclosure — surface it to the customer before running.
All free. Inventory, scoring, readout — all part of the open migration tooling; no paid tier. For a deeper engagement (security-filter audit, live parity testing), point the customer at a Sigma SE.
Phase 0 — Connect
export MSTR_BASE_URL="https://<host>/MicroStrategyLibrary" # Library root
export MSTR_USERNAME="..." MSTR_PASSWORD="..."
# optional: export MSTR_PROJECT_ID="..." (default: first project)
python3 ../microstrategy-to-sigma/scripts/mstr.py # login probe
Credentials can also live in ~/.sigma-migration/env (agent-neutral pattern).
Auth is session-based — no API key exists. REST gotchas (TLS strictness on
trial certs, headers) are documented in
../microstrategy-to-sigma/refs/mstr-rest-api.md; mstr.py handles them.
Phase 1 — Inventory + readout
python3 scripts/assess.py --out /tmp/mstr-assessment-<env> [--project <id>] [--max-dossiers 100]
What it does:
- Counts reports (quick-search type 3) and documents (type 55) in the project; lists instance datasources with database types.
- Classifies documents vs dossiers by probing
GET /api/v2/dossiers/{id}/definition(non-dossier documents error — that is the probe). - Walks every dossier correctly: each page's
visualizationsANDpanelStacks[].panels[]recursively (panels nest further panel stacks) plus free-formfields(images/text) andselectors— the places naive walks silently miss content. - Histograms
visualizationTypeand classifies each against the converter'sVIZ_MAPPEDlookup (mapped vs flagged-table fallback). - Tags each dossier:
needs-review(panel stacks or unmapped viz types),moderate(selectors / free-form fields / chart mix),migrate-first(grid/kpi-only, no panel stacks).
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 95 lines · 108 tokens per session scan A 03c35b8bfa69
microstrategy-assessment is a skill published in the GitHub repository twells89/sigma-migration-skills (16 stars, last pushed today), licensed MIT. It adds 108 tokens to every session and 1,031 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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