Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mstr-sharma/strategy-automate --skill strategy-aigit clone --depth 1 https://github.com/mstr-sharma/strategy-automateWrote 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/mstr-sharma/strategy-automate/strategy-ai)<a href="https://agentmods.dev/skills/mstr-sharma/strategy-automate/strategy-ai"><img src="https://agentmods.dev/badge/skills/mstr-sharma/strategy-automate/strategy-ai/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/mstr-sharma/strategy-automate/strategy-ai"><img src="https://agentmods.dev/badge/skills/mstr-sharma/strategy-automate/strategy-ai.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00188 | $0.04347 |
| Opus 5.5 | $0.00075 | $0.01739 |
| Sonnet 5.5 | $0.00038 | $0.00869 |
| Haiku 4.5 | $0.00019 | $0.00435 |
Grade A, and why
strategy-ai scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
1. **Servers up:** `curl -s -o /dev/null -w '%{http_code}\n' https://<tenant>.strategy.com/collaboration/mcp/mosaic` → `401` means up (same for `/mcp/agent`); `https://<tenant>.strategy.com/collaboration/status` → `"stat How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategy AI
Agents, questions, AI-ready content and the MCP surfaces of a Strategy environment, driven through strategy_api.py (REST) and strategy_mcp.py (the two MCP servers). Sign-in and the generic find → describe → dry run → --yes loop are in skills/strategy-platform/SKILL.md. The REST paths say bot where the product says agent; agents are object type 55, subtypes 14084 (bot), 14087 (agent), 14091 (universal agent).
Scope
Owned API areas (strategy_api.py ops --tag strategy-ai --internal):
- Agent — questions (
/api/questions: ask, poll, stream, suggestions, transformed SQL, full data; the documented Agent API), agent objects under/api/v2/bots(info, config, detail, copy, modify, chats, caches, training, tuning, evaluation, NER — internal exceptcolumns,cubes/statusandcaches/temp), nuggets, AI memories. - Auto Bots — legacy
/api/bots,/api/chats,/api/learnings: the instance/question POSTs are deprecated;getConfigurationand the question reads are still public. - Explorer — Explorer chats, shared config, web-search config, the registry of external MCP servers.
- Ontology (vocabularies; concepts internal), QuestionSet (evaluation sets, internal), Recommendations (internal), AI Storage (internal), AutoExpert (support-case intake).
Cross-area calls used here: certifyObject, updateObject, deleteObject, doQuickSearch, dumpCubes (strategy-content); getServerPrivilege, getUserPrivileges, bulkUpdatePrivileges, getMcpSettings, getCookieInfo, getIServerTrustStoreRelationship (strategy-admin); getFeedbackStatistics (strategy-ops).
Route elsewhere: building or fixing the model an agent answers from → build-mosaic-model; checking answers against trusted numbers → strategy-validation; publishing an agent to users' Library and object ACLs → strategy-content; granting roles and privileges beyond the MCP checklist → strategy-admin; uploading documents → create-unstructured-data.
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.
- 12d ago First seen · 172 lines · 188 tokens per session scan A eae02ea44993
strategy-ai is a skill published in the GitHub repository mstr-sharma/strategy-automate (2 stars, last pushed 2d ago), licensed MIT. It adds 188 tokens to every session and 4,347 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-06.
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