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 collibra/chip --skill contextgit clone --depth 1 https://github.com/collibra/chipWrote 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/collibra/chip/context)<a href="https://agentmods.dev/skills/collibra/chip/context"><img src="https://agentmods.dev/badge/skills/collibra/chip/context/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/collibra/chip/context"><img src="https://agentmods.dev/badge/skills/collibra/chip/context.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 | $0.00053 | $0.02786 |
| Opus 5.5 | $0.00021 | $0.01114 |
| Sonnet 5.5 | $0.00011 | $0.00557 |
| Haiku 4.5 | $0.00005 | $0.00279 |
Grade A, and why
context 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 2mo 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 — 253 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context generation: extract shaped metadata for any target system
A Context Specification is a blueprint that defines a governed subset of Collibra's Knowledge Graph. Starting from an asset (e.g., a Table), it specifies which relations to traverse, what fields to pull, and what shape to return for any target system: Snowflake, Databricks, a data product UI, or an AI agent's reasoning process.
In other words: Context Specifications define which slice of the Knowledge Graph matters for a specific use case, and what shape that slice should take when delivered to downstream consumers.
This skill explains the three-tool workflow: discover which specs are available, inspect a spec if needed, and execute it to get the shaped output.
The three tools
| Tool | Purpose | Returns | When required |
|---|---|---|---|
list_context_specifications |
Discover which Context Specifications (Knowledge Graph blueprints) are available for an asset or asset type | List of spec names, descriptions, and IDs | Always, entry point |
get_context_specification |
Inspect a spec's blueprint: which relations it defines, what fields it extracts from the Knowledge Graph, what transforms it applies | Complete YAML mapping and spec metadata | Optional, only when user asks what a spec covers |
get_asset_details (with contextSpecificationId) |
Execute a spec's blueprint against an asset to extract and shape its governed metadata subset; returns full asset details alongside the generated context | Asset details + structured YAML context shaped for the target system | Always, output step |
Decision rule: which tool to call first
Always start with list_context_specifications. It takes one of two parameters:
Option A: You have an asset UUID
Call list_context_specifications(assetId=<UUID>) to find specs whose source asset type matches that asset's type.
Use when: You've already resolved a user's request to a specific Collibra asset (e.g., "the Orders table" → resolved to UUID abc-123).
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.
- 2mo ago First seen · 253 lines · 53 tokens per session scan A 22f2f9a91832
context is a skill published in the GitHub repository collibra/chip (37 stars, last pushed yesterday), licensed Apache-2.0. It adds 53 tokens to every session and 2,786 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 2026-08-30.
context compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| contextcollibra/chip | Skill | 37 | yesterday | Apache-2.0 | A | 53 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |