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/WYRE-AI/msp-claude-pluginsWrote 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/commands/wyre-ai/msp-claude-plugins/secure-score-report)<a href="https://agentmods.dev/commands/wyre-ai/msp-claude-plugins/secure-score-report"><img src="https://agentmods.dev/badge/commands/wyre-ai/msp-claude-plugins/secure-score-report.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.00351 |
| Opus 5 | $0.00014 | $0.00176 |
| Sonnet 5 | $0.00006 | $0.00070 |
| Haiku 4.5 | $0.00003 | $0.00035 |
Grade A, and why
secure-score-report 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 4d 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
CIPP Secure Score Report
Portfolio-level security posture report across the MSP's managed tenants. Designed for monthly internal reviews, quarterly business reviews, and as the source data for client-facing security summaries.
Arguments
format(optional) — scorecard (default) — tenant-by-tenant table sorted by risk; trend — compare to previous run; executive — client-deliverable summarytenants(optional) — Comma-separated list of tenants to include (defaults to all)
What it produces
For each tenant in scope:
| Metric | Source |
|---|---|
| BPA fail count (Identity / Mail / Security) | cipp_list_bpa |
| Enabled CA policies (count + presence of MFA-for-all-apps) | cipp_list_conditional_access_policies |
| MFA enrollment % | cipp_list_mfa_users |
| Domain health (DMARC enforced, SPF valid, DKIM enabled) | cipp_list_domain_health |
| Standards baseline coverage | cipp_list_standards |
| Computed risk band | High / Medium / Low |
Tenants are sorted with high-risk first.
Use the agent for
Cross-tenant change analysis ("which tenants regressed since last month?") goes to security-posture-reviewer, which can compare against a stored baseline and recommend remediation paths. This command produces the data; the agent produces the narrative.
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.
- 4d ago First seen · 34 lines · 28 tokens per session scan A 193cbf39f40d
secure-score-report is a command published in the GitHub repository WYRE-AI/msp-claude-plugins (45 stars, last pushed 5d ago), licensed Apache-2.0. It adds 28 tokens to every session and 351 once invoked, about $0.0001 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 2026-09-04.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.