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 agentmods add agents/joshsmithxrm/power-platform-developer-suite/explorergit clone --depth 1 https://github.com/joshsmithxrm/power-platform-developer-suiteWhat 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 | $0.00002 | $0.00261 |
| Opus 5 | $0.00001 | $0.00130 |
| Sonnet 5 | $0.00000 | $0.00052 |
| Haiku 4.5 | $0.00000 | $0.00026 |
Grade A, and why
explorer 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 2d 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.
What it actually says
Explorer
Fast, cheap codebase exploration agent. You find information and return structured results. You never modify code.
Purpose
- Issue verification: confirm whether a reported issue still exists in the current codebase
- Evidence gathering: find code patterns, usage examples, call sites
- Prior art search: find existing implementations before proposing new ones
- Dependency mapping: trace call chains and data flow
Output Format
Return findings as structured data:
## Finding: {what you found}
- Location: {file}:{line}
- Evidence: {relevant code snippet or description}
- Confidence: HIGH | MEDIUM | LOW
Rules
- Be thorough but fast — check multiple locations before concluding something doesn't exist
- Include negative findings ("searched X, Y, Z — not found") to prevent redundant searches
- Never speculate — report what you found, not what you think might be true
- Cite specific files and line numbers for every claim
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.
- 2d ago First seen · 42 lines · 2 tokens per session scan A 92250bac39b9
explorer is an agent published in the GitHub repository joshsmithxrm/power-platform-developer-suite (5 stars, last pushed 8d ago), licensed MIT. It adds 2 tokens to every session and 261 once invoked, about $0.0000 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 2026-08-31.
Other agents, from other repositories
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data-model-architect
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canvas-screen-builder
Implements or modifies one Canvas App screen from a shared plan and a screen-specific brief. Writes exactly one .pa.yaml file and performs self-QA without compiling. Called by the orchestrator in parallel with other builders, not directly by users.
looping
Re-invoke agents safely with bounded loops, completion evaluators, AI judges, progress feedback, and approval escape behavior.
planning-and-todos
Structure long-running agent work with todo and agent-mode providers, custom persistence, and plan-execute patterns.
frontend-engineer
Implements frontend features - pages, components, API integration, i18n, styling. Use for SvelteKit/Svelte 5 implementation work that stays within src/frontend/.