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 zero-lab-ai/skills --skill engagement-report-assemblygit clone --depth 1 https://github.com/zero-lab-ai/skillsWrote 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/zero-lab-ai/skills/engagement-report-assembly)<a href="https://agentmods.dev/skills/zero-lab-ai/skills/engagement-report-assembly"><img src="https://agentmods.dev/badge/skills/zero-lab-ai/skills/engagement-report-assembly/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/zero-lab-ai/skills/engagement-report-assembly"><img src="https://agentmods.dev/badge/skills/zero-lab-ai/skills/engagement-report-assembly.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 | $0.00052 | $0.01106 |
| Opus 5.5 | $0.00021 | $0.00442 |
| Sonnet 5.5 | $0.00010 | $0.00221 |
| Haiku 4.5 | $0.00005 | $0.00111 |
Grade A, and why
engagement-report-assembly 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 yesterday.
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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Engagement report assembly
Use this skill to answer one question: does the package faithfully and completely represent the declared engagement universe and every eligible supplied artifact?
When to Use
- Assemble evidence-backed engagement reports.
- Use only within explicit authorization and the stated rules of engagement.
When NOT to Use
- Do not treat this skill as permission to access, test, or change a target.
- Do not convert missing evidence into a clean result or a stronger claim.
Hold the boundary
- Accept only supplied validated specialist artifacts and administrative receipts.
- Do not scan, test, reinterpret, rewrite, rescore, merge, split, close, suppress, rank, publish, deliver, notify, sign, or contact.
- Keep raw evidence, secrets, personal data, payloads, commands, paths, live identifiers, and report prose outside the ledger.
Declare the universe
- Record the immutable expected source universe, authority-bound admissions, engagement authority, policy, cutoff, and strict window. Obtain the expected authority-manifest digest independently of the package; never derive or replace it while validating the package.
- Record supplied scope units, objectives, methods, and limitations without adding or improving them.
- Give every expected input exactly one disposition: eligible current, eligible historical, boundary-only, excluded invalid, excluded out of scope, or conflicted.
Preserve source meaning
- Preserve each external assessment result. Bind its exact capability/version, artifact format, artifact, exit contract, disposition, inclusion, policy, assessment window, manifests, scope, origin, custody, and presentation approval in a separate authority admission; use the registry assembly digest only for packaging.
- Include every eligible claim and finding exactly once.
- Preserve claim polarity, state, confidence, applicable status, freshness, and limitations. Never strengthen or reverse them. Represent not-applicable only as a policy-backed coverage cell, never as a claim.
- Preserve every immutable finding field digest and ordinal. Never retitle, paraphrase, rescore, reorder, merge, split, suppress, or change remediation state.
- Count shared origin lineage once; duplicate transformations are not corroboration.
- Keep stale or disposed sources historical. They cannot support current coverage.
What ships with it
2 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.
- yesterday First seen · 86 lines · 52 tokens per session scan A f9959b39cbbd
engagement-report-assembly is a skill published in the GitHub repository zero-lab-ai/skills (283 stars, last pushed 12d ago), licensed Apache-2.0. It adds 52 tokens to every session and 1,106 once invoked, about $0.0002 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 2026-10-26.
Other skills, from other repositories
performing-ai-assisted-vulnerability-discovery
Using LLMs to accelerate vulnerability research and pentest workflows — generating syntax-valid fuzzing seeds and evolving grammars, fine-tuned mutation dictionaries, parallel agent-based proof-of-vulnerability generation, and evidence-driven passive analysis of real HTTP traffic via the Burp MCP server. Covers…
testing-llm-prompt-injection-and-jailbreaks
Testing LLM-backed applications, chatbots, and AI agents for direct and indirect prompt injection, jailbreaks, system-prompt leakage, and tool/agent abuse during authorized penetration tests, using structured payload families and reliable confirmation signals.
bypassing-authentication-with-forced-browsing
Discovering and accessing unprotected pages, APIs, and administrative interfaces by enumerating URLs and bypassing authentication controls during authorized security assessments.
analyzing-ios-app-security-with-objection
Performs runtime mobile security exploration of iOS applications using Objection, a Frida-powered toolkit that enables security testers to interact with app internals without jailbreaking. Use when assessing iOS app security posture, bypassing client-side protections, dumping keychain items, inspecting filesystem…
conducting-full-scope-red-team-engagement
Plan and execute a comprehensive red team engagement covering reconnaissance through post-exploitation using MITRE ATT&CK-aligned TTPs to evaluate an organization's detection and response capabilities.
exploiting-ai-model-file-rce
Testing machine-learning model files and model-loading services for remote code execution caused by insecure deserialization (pickle/PyTorch), unsafe config instantiation (Hydra), archive path traversal, and dangerous layer types during authorized penetration tests of AI/ML pipelines.