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 eli-labz/Cognitive-Core-Skills --skill discoverygit clone --depth 1 https://github.com/eli-labz/Cognitive-Core-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/eli-labz/cognitive-core-skills/discovery)<a href="https://agentmods.dev/skills/eli-labz/cognitive-core-skills/discovery"><img src="https://agentmods.dev/badge/skills/eli-labz/cognitive-core-skills/discovery/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/eli-labz/cognitive-core-skills/discovery"><img src="https://agentmods.dev/badge/skills/eli-labz/cognitive-core-skills/discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 120 This pattern attempts to override system instructions or ignore safety constraints. Without LLM analysis, manual review is recommended.Fix: Remove or rewrite any text that instructs the agent to ignore prompts, override safety rules, or trust unverified content. Ensure skill content cannot be injected to alter agent behavior.
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 | $0.00039 | $0.02611 |
| Opus 5 | $0.00019 | $0.01306 |
| Sonnet 5 | $0.00008 | $0.00522 |
| Haiku 4.5 | $0.00004 | $0.00261 |
Grade A, and why
knowledge_catalog_discovery_agent 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a proactive and helpful search agent. You take user queries and use Knowledge Catalog Search to find entries that answer the user's questions.
When users ask statistical or analytical questions, you MUST ANSWER THEM BY finding and returning the results/entries that will allow them to answer their question. Always assume you can help. Never start by saying "I cannot answer statistical questions" or "I cannot help you with that." Do not ask clarifying questions first; always attempt a search to find entries the user can use.
About Knowledge Catalog Search
Knowledge Catalog Search allows free text search and also allows qualified predicates. You can qualify a predicate by prefixing it with a key that restricts the matching to a specific piece of metadata:
- An equal sign (=) restricts the search to an exact match.
- A colon (:) after the key matches the predicate to either a substring or a token within the value in the search results. For example:
name:fooselects resources with names that contain the foo substring, like foo1 and barfoo.
How to use Knowledge Catalog Search
- Tool Function:
knowledge_catalog_search(query: str) - CRITICAL ARGUMENT RULE: If the user specifies a project (or
if you extract
projectidpredicates), you MUST do the following:- INCLUDE THEM in the
querystring argument. (e.g., yourquerystring must physically containprojectid=some-project).
- INCLUDE THEM in the
[!IMPORTANT] You MUST use these instructions to do search and get the MOST RELEVANT results.
Instructions
Step 1: Understand the query
- User can provide natural language text (aka free text) as query.
- User can also provide predicates, like
type=table. - Keep the predicates if user has provided it and use it as it is when invoking search
What ships with it
5 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.
- 10d ago First seen · 192 lines · 39 tokens per session scan A 38937ecc6d95
knowledge_catalog_discovery_agent is a skill published in the GitHub repository eli-labz/Cognitive-Core-Skills (165 stars, last pushed 2mo ago), licensed MIT. It adds 39 tokens to every session and 2,611 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.
Other skills, from other repositories
h5i
Browse or automate web pages, perform authorized web security testing on captured HTTP traffic, or run untrusted development work inside disposable confined boxes with auditable evidence and reviewed export.
memory-audit
An entry point for reviewing and maintaining an AI agent's stored memories. It describes how to remove repetition, preserve useful reasoning, and update memories when old conclusions no longer fit.
memory-audit-belief-duel
A guided review process for conflicting beliefs or memories. It examines cases where two conclusions cannot both be true, including conflicts between a general rule and a more specific memory.
memory-audit-discoverability
A review guide for checking whether stored memories can be found at the right time. It focuses on where memories are attached, when they are triggered, whether aliases are missing, and whether a parent has too many children.
memory-audit-node-decomposition
A method for splitting an oversized knowledge note into smaller notes, each focused on one independent idea. It also explains how to keep useful core information in the original note.
memory-audit-pattern-extraction
A method for investigating repeated mistakes by comparing related memories and checking whether an earlier reminder failed. It looks at where the reminder was stored, when it was created, and whether it was strong enough to prevent the mistake.