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 skills/jmxt3/gitscape.ai/googlecloudplatform-knowledge-catalognpx skills add jmxt3/gitscape.ai --skill googlecloudplatform-knowledge-cataloggit clone --depth 1 https://github.com/jmxt3/gitscape.aiWrote 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/jmxt3/gitscape.ai/googlecloudplatform-knowledge-catalog)<a href="https://agentmods.dev/skills/jmxt3/gitscape.ai/googlecloudplatform-knowledge-catalog"><img src="https://agentmods.dev/badge/skills/jmxt3/gitscape.ai/googlecloudplatform-knowledge-catalog.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 | $0.00036 | $0.00861 |
| Opus 5 | $0.00018 | $0.00430 |
| Sonnet 5 | $0.00007 | $0.00172 |
| Haiku 4.5 | $0.00004 | $0.00086 |
Grade A, and why
googlecloudplatform-knowledge-catalog 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 5d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GoogleCloudPlatform/knowledge-catalog — Engineering Skill
Overview
The knowledge-catalog repository defines the Open Knowledge Format (OKF), a vendor-neutral standard for representing knowledge. This codebase provides the reference implementation for producing and validating these bundles. It is critical to understand that the format is the primary product; the agents and runners are secondary tools meant to demonstrate how to produce and enrich these bundles.
Before modifying any code, you must ensure you are not coupling the OKF structure to specific model providers or frameworks. The system relies on a clear mapping between file system paths and concept IDs. Any changes to the ingestion or enrichment logic must preserve the ability for third-party tools to parse these files without dependency on this repository's internal agent logic.
When to Use
- Implementing custom logic to generate OKF bundles
- Extending the ReferenceRunner to support new data sources
- Updating the validation logic for OKFDocument
- Regenerating indexes for existing knowledge bundles
- Adding new enrichment strategies via synthesize_description
When NOT to use: Do not use this skill for tasks that introduce vendor-specific dependencies or deviate from the plain-text markdown/YAML structure defined in SPEC.md.
Core Process
Step 1: Validate against OKF Specification
Before writing code, verify that your proposed change complies with SPEC.md. Use the OKFDocument.validate() method to ensure your document structure remains compliant with the format requirements.
Step 2: Maintain Path-Concept Mapping
Always use path_to_concept_id and concept_id_to_path when navigating the bundle structure. This ensures that the file system hierarchy remains consistent with the conceptual graph of the knowledge base.
Step 3: Enrich with ReferenceRunner
When adding new knowledge, utilize the ReferenceRunner.enrich_concept method. This ensures that enrichment follows the established pipeline, using the configured model to synthesize descriptions while maintaining the integrity of the existing bundle.
What ships with it
11 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.
- exporters/googlecloudplatform_knowledge_catalog_adk_skill.py 1.9 KB runs code
- exporters/googlecloudplatform_knowledge_catalog_agno_skill.py 1.1 KB runs code
- googlecloudplatform_knowledge_catalog_digest.txt 821 KB
- manifest.json 2.3 KB
- references/api.md 23 KB
- references/architecture.md 6.3 KB
- references/config.md 277 B
- references/examples.md 5.2 KB
- references/setup.md 246 B
- scan-report.json 2.4 KB
- scan-report.sarif 34 KB
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.
- 5d ago First seen · 76 lines · 36 tokens per session scan A 8df63e19c3d8
googlecloudplatform-knowledge-catalog is a skill published in the GitHub repository jmxt3/gitscape.ai (33 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 36 tokens to every session and 861 once invoked, about $0.0002 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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