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 GoogleCloudPlatform/cxas-scrapi --skill cxas-cuj-report-generatorgit clone --depth 1 https://github.com/GoogleCloudPlatform/cxas-scrapiWrote 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/googlecloudplatform/cxas-scrapi/cxas-cuj-report-generator)<a href="https://agentmods.dev/skills/googlecloudplatform/cxas-scrapi/cxas-cuj-report-generator"><img src="https://agentmods.dev/badge/skills/googlecloudplatform/cxas-scrapi/cxas-cuj-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/skills/googlecloudplatform/cxas-scrapi/cxas-cuj-report-generator"><img src="https://agentmods.dev/badge/skills/googlecloudplatform/cxas-scrapi/cxas-cuj-report-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00051 | $0.02868 |
| Opus 5 | $0.00026 | $0.01434 |
| Sonnet 5 | $0.00010 | $0.00574 |
| Haiku 4.5 | $0.00005 | $0.00287 |
Grade A, and why
cxas-cuj-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 10d 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 — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Critical User Journey (CUJ) Transcript & Report Generator Skill
Use this skill when asked to extract dialogue transcripts or compile interactive Critical User Journey (CUJ) reports from a directory of customer requirement documents (such as diagrams, BRDs, code etc.).
Core Protocols
To ensure 100% coverage and zero data loss, you MUST follow these core rules:
- Robust Extraction: Follow the protocol defined in the
cxas-protocol-robust-extractionskill. - Two-Phase Ingestion: Follow the protocol defined in the
cxas-protocol-two-phase-ingestionsub-protocol insideprotocols/cxas-protocol-two-phase-ingestion/. - Checklist Mandate: The orchestrator and all subagents MUST follow the
agent-protocol-checklistprotocol to maintain a localtask_checklist.jsonfile, ensuring they track their progress and not lose coverage during execution. - Orchestrator Delivery Assurance: The orchestrator MUST act
as a strict, independent Delivery Auditor. BEFORE closing subagents,
terminating the watchdog, or reporting campaign success to the user, the
orchestrator MUST physically verify the existence, size bounds, and schema
compliance of all registered deliverables (specifically
gecx_customer_report.htmlandgecx_cuj_report.html) on disk. Under no circumstances may the orchestrator assume completion without executing a physical file-presence check. - Auditing: The orchestrator MUST periodically check the subagent's
scratch directory to ensure the
task_checklist.jsonfile is being created and maintained. If the file is missing or not updated, the orchestrator MUST terminate the subagent and respawn it with stronger enforcement instructions.
Core Workflow Steps
Follow this 5-step structured workflow to execute the task:
-
Scoping & Type Discovery: Prepare the environment and identify required skills.
- Access Files: Ensure you have access to the source artifacts in your
local workspace.
- Tip (Drive Links): If the source is a Google Drive link or
folder ID, you MUST use the
gdriveskill to access them.
- Tip (Drive Links): If the source is a Google Drive link or
folder ID, you MUST use the
- Detect Inventory Types: To identify framework signatures and map
them to correct Ingestors, you MUST use the framework detector agent
defined in
agents/framework_detector.md. Using this agent, scan the input files to inventory all file extensions and detect potential frameworks. Spawn parallel Framework Detector subagents to scan partitions of the file tree. - Map Ingestors: Use the scoping report generated by the Framework
Detector to select or create the correct specialized skills in
ingestors/frameworks/oringestors/files/.
- Access Files: Ensure you have access to the source artifacts in your
local workspace.
What ships with it
60 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.
- agents/cuj_standardizer.md 3.5 KB
- agents/execution_subagent.md 427 B
- agents/expert_ingestor.md 7.2 KB
- agents/framework_detector.md 892 B
- agents/logical_clustering_expert.md 376 B
- append_turn.py 5.2 KB runs code
- compile_deliverables.py 6.8 KB runs code
- construct_report.py 12 KB runs code
- evals/EVAL_e2e.yaml 1.9 KB
- evals/EVAL_granular.yaml 3.5 KB
- evals/scripts/compile_optimizer_context.py 2.5 KB runs code
- evals/scripts/grade_eval_batch.py 6.6 KB runs code
- evals/scripts/hill_climber.py 15 KB runs code
- evals/scripts/prepare_eval_batch.py 6.5 KB runs code
- evals/scripts/utils/__init__.py 13 B runs code
- evals/scripts/utils/grading.py 20 KB runs code
- evals/scripts/utils/optimizer.py 1.9 KB runs code
- evals/TESTING.md 4.8 KB
- ingestors/files/drawio/SKILL.md 4.9 KB
- ingestors/frameworks/adk/SKILL.md 5.5 KB
- ingestors/frameworks/cyara/SKILL.md 1.9 KB
- ingestors/frameworks/dfcx/SKILL.md 6.7 KB
- protocols/cxas-protocol-robust-extraction/SKILL.md 4.3 KB
- protocols/cxas-protocol-two-phase-ingestion/SKILL.md 1.2 KB
- protocols/task-coverage-protocol/evals/cases/add_items_1032/case.yaml 255 B
- protocols/task-coverage-protocol/evals/cases/add_items_1032/SETUP.md 123 B
- protocols/task-coverage-protocol/evals/cases/add_items_1032/setup.py 609 B runs code
- protocols/task-coverage-protocol/evals/cases/add_items_10423/case.yaml 258 B
- protocols/task-coverage-protocol/evals/cases/add_items_10423/SETUP.md 123 B
- protocols/task-coverage-protocol/evals/cases/add_items_10423/setup.py 611 B runs code
- protocols/task-coverage-protocol/evals/cases/add_items_173/case.yaml 252 B
- protocols/task-coverage-protocol/evals/cases/add_items_173/SETUP.md 123 B
- protocols/task-coverage-protocol/evals/cases/add_items_173/setup.py 607 B runs code
- protocols/task-coverage-protocol/evals/cases/add_items_3/case.yaml 246 B
- protocols/task-coverage-protocol/evals/cases/add_items_3/SETUP.md 123 B
- protocols/task-coverage-protocol/evals/cases/add_items_3/setup.py 603 B runs code
- protocols/task-coverage-protocol/evals/cases/e2e_successful_task_coverage/case.yaml 526 B
- protocols/task-coverage-protocol/evals/cases/e2e_successful_task_coverage/testdir/directory_with_3_files/file1.txt 220 B
- protocols/task-coverage-protocol/evals/cases/e2e_successful_task_coverage/testdir/directory_with_3_files/file2.txt 175 B
- protocols/task-coverage-protocol/evals/cases/e2e_successful_task_coverage/testdir/directory_with_3_files/file3.txt 164 B
- protocols/task-coverage-protocol/evals/cases/iterate_and_execute_3/case.yaml 258 B
- protocols/task-coverage-protocol/evals/cases/iterate_and_execute_3/SETUP.md 123 B
- protocols/task-coverage-protocol/evals/cases/iterate_and_execute_3/setup.py 603 B runs code
- protocols/task-coverage-protocol/evals/cases/list_and_count_1032_files/case.yaml 208 B
- protocols/task-coverage-protocol/evals/cases/list_and_count_1032_files/SETUP.md 114 B
- protocols/task-coverage-protocol/evals/cases/list_and_count_1032_files/setup.py 491 B runs code
- protocols/task-coverage-protocol/evals/cases/list_and_count_1032_files/testdir/files_1032.zip 129 KB
- protocols/task-coverage-protocol/evals/cases/list_and_count_10423_files/case.yaml 211 B
- protocols/task-coverage-protocol/evals/cases/list_and_count_10423_files/SETUP.md 114 B
- protocols/task-coverage-protocol/evals/cases/list_and_count_10423_files/setup.py 491 B runs code
- protocols/task-coverage-protocol/evals/cases/list_and_count_10423_files/testdir/files_10423.zip 1331 KB
- protocols/task-coverage-protocol/evals/cases/list_and_count_173_files/case.yaml 205 B
- protocols/task-coverage-protocol/evals/cases/list_and_count_173_files/SETUP.md 114 B
- protocols/task-coverage-protocol/evals/cases/list_and_count_173_files/setup.py 491 B runs code
- protocols/task-coverage-protocol/evals/cases/list_and_count_173_files/testdir/files_173.zip 21 KB
- protocols/task-coverage-protocol/evals/cases/list_and_count_3_files/case.yaml 199 B
- protocols/task-coverage-protocol/evals/cases/list_and_count_3_files/SETUP.md 114 B
- protocols/task-coverage-protocol/evals/cases/list_and_count_3_files/setup.py 491 B runs code
- protocols/task-coverage-protocol/evals/cases/list_and_count_3_files/testdir/files_3.zip 388 B
- protocols/task-coverage-protocol/evals/cases/mark_done_success_3/case.yaml 269 B
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.
- 10d ago First seen · 236 lines · 51 tokens per session scan A 29cc9caada8d
cxas-cuj-report-generator is a skill published in the GitHub repository GoogleCloudPlatform/cxas-scrapi (96 stars, last pushed yesterday), licensed Apache-2.0. It adds 51 tokens to every session and 2,868 once invoked, about $0.0003 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 2026-08-30.
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