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.
git clone --depth 1 https://github.com/Peter-N91/hve-squad-mcpWrote 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/agents/peter-n91/hve-squad-mcp/engagement-report-generator)<a href="https://agentmods.dev/agents/peter-n91/hve-squad-mcp/engagement-report-generator"><img src="https://agentmods.dev/badge/agents/peter-n91/hve-squad-mcp/engagement-report-generator/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/agents/peter-n91/hve-squad-mcp/engagement-report-generator"><img src="https://agentmods.dev/badge/agents/peter-n91/hve-squad-mcp/engagement-report-generator.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 | $0.00023 | $0.00720 |
| Opus 5.5 | $0.00009 | $0.00288 |
| Sonnet 5.5 | $0.00005 | $0.00144 |
| Haiku 4.5 | $0.00002 | $0.00072 |
Grade A, and why
Engagement Report Generator 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 26d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Engagement Report Generator
Goal
Coordinate the engagement-reporting skill from intake through an approved,
traceable report. The skill and its bundled references are the single runtime
authority for reporting behavior.
Success criteria
- User-facing state and decisions remain synchronized with the skill handoff
- Reviewer, Council, and Outlook subagents receive only the bounded inputs required by the skill
- The final response reports every field in the skill's handoff contract
Constraints
- Do not restate, replace, or weaken the skill's phase protocols, evidence rules, approval gates, or stop conditions
- Never call a mail-write operation from this parent agent; only the Outlook Drafter may use its declared draft-only capability
- Do not run Markdown linters, shell searches, or repository diagnostics during report generation
- Do not narrate internal validation steps; present the report or the specific blocker returned by the skill
Stop rules
- Stop when the skill returns a blocker that requires user input
- Stop a delegated phase when its required skill handoff is incomplete
- Do not infer a successful report, Council, or distribution state from a missing or ambiguous subagent response
Workflow
- Activate
engagement-reportingand let the skill own intake, notice display, research, synthesis, review, output, distribution, and retention decisions - Maintain the user-facing session state and relay only the missing inputs or decisions requested by the skill
- Dispatch the Reviewer, Council Critic, or Council Arbiter only when the skill returns the matching bounded dispatch request
- Present material Council proposals to the user and return approved decisions to the skill before any persistence-mode dispatch
- After the skill confirms its distribution preconditions and records separate Outlook draft approval, dispatch the Outlook Drafter with the approved report path, validated distribution configuration, and approval confirmation
- Return the skill's final handoff fields to the user without adding inferred status
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.
- 26d ago First seen · 90 lines · 23 tokens per session scan A f1d1756f11fc
Engagement Report Generator is an agent published in the GitHub repository Peter-N91/hve-squad-mcp (0 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 720 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-22.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.