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/adityavg13/cortex/storenpx skills add AdityaVG13/cortex --skill storegit clone --depth 1 https://github.com/AdityaVG13/cortexWhat 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 | $0.00034 | $0.00402 |
| Opus 5 | $0.00017 | $0.00201 |
| Sonnet 5 | $0.00007 | $0.00080 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
store 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 2d 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.
What it actually says
Cortex Store
Save a decision or lesson to your persistent memory.
Usage
/cortex:store <decision>
The $ARGUMENTS placeholder contains the decision/lesson text.
What to Store
Good candidates for storage:
- Architecture decisions: "Chose SQLite for local-first, embedded use case"
- Coding conventions: "Use conventional commits: feat:, fix:, docs:"
- Lessons learned: "NTFS file locking requires LockFileEx on Windows"
- Project knowledge: "API rate limit is 100 req/min, use exponential backoff"
- Team agreements: "All PRs require at least one review"
Confidence Score Guidance
When storing, include confidence:
| Score | Meaning |
|---|---|
| 1.0 | Definitive - confirmed by testing/production |
| 0.9 | High confidence - validated approach |
| 0.8 | Solid - good evidence |
| 0.7 | Reasonable - working assumption |
| 0.5 | Tentative - needs more validation |
Context
Always include context - the "why" behind the decision:
- What problem did it solve?
- What alternatives were considered?
- What tradeoffs were made?
Example
Input:
/cortex:store "Use path.join() for all file paths - hardcoded / or \\ breaks Windows compatibility"
Stored as:
- Decision: Use path.join() for all file paths
- Context: Hardcoded / or \ breaks Windows compatibility
- Confidence: 0.9
Integration
This skill uses the cortex_store MCP tool with your $ARGUMENTS as the decision text.
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.
- 2d ago First seen · 63 lines · 34 tokens per session scan A 11d58fd09229
store is a skill published in the GitHub repository AdityaVG13/cortex (9 stars, last pushed 2d ago), licensed MIT. It adds 34 tokens to every session and 402 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 2026-08-31.
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