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.
git clone --depth 1 https://github.com/dkothule/ai-contextWrote 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/rules/dkothule/ai-context/main)<a href="https://agentmods.dev/rules/dkothule/ai-context/main"><img src="https://agentmods.dev/badge/rules/dkothule/ai-context/main/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/rules/dkothule/ai-context/main"><img src="https://agentmods.dev/badge/rules/dkothule/ai-context/main.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00776 | $0.00776 |
| Opus 5.5 | $0.00310 | $0.00310 |
| Sonnet 5.5 | $0.00155 | $0.00155 |
| Haiku 4.5 | $0.00078 | $0.00078 |
Grade A, and why
main 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 1mo 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
description: Bootstrap Cursor to shared .ai-context instructions globs: alwaysApply: true
Cursor Adapter Rule
Use .ai-context/ as the single source of truth for project workflow, standards, and session continuity. This rule is intentionally thin.
Session Start
All paths below are relative to .ai-context/.
Always read:
project.overview.md— what this project isproject.tasks.md— what is in flight, blocked, next- Session logs in
sessions/— never_archive/, never_template.md: a. Newest by filename date. If several share that date, order by thetime:frontmatter field; where absent, fall back to filename order. b. Scan the# Session:heading of the other same-date logs and read any whose topic relates to the current task. c. If that log's "Next Steps" or "Notes For Next Agent" points at earlier work you are continuing, follow the reference. Read at most 3 logs unless continuation references in (c) require more.
Then read based on task:
- Writing/modifying code →
standards/project.rules.base.md,standards/project.rules.md - Release/history context →
project.changelog.md - Understanding layout →
project.structure.md - Planning non-trivial work →
project.tasks.md,plans/ - Language/testing → files in
standards/
Planning
Before non-trivial work (multi-session, architectural change, external dependency), write a plan to .ai-context/plans/YYYY-MM-DD-<topic>.md using _template.md. Reference it from project.tasks.md.
During Work
- Follow
.ai-context/standards/project.rules.base.mdandproject.rules.md. - One logical change per commit; tests run before commit.
- Route state changes to the right
.ai-context/file: decision →project.decisions.md, user-visible change →project.changelog.md, task transition →project.tasks.md, plan →plans/, session close →sessions/.
Session End (Mandatory)
- Write
.ai-context/sessions/YYYY-MM-DD-<topic>.mdfrom_template.md. Multiple logs per day are fine — one per topic. - Update
project.tasks.md,project.decisions.md,project.changelog.mdas applicable.
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.
- 1mo ago Changed · +8 lines · +142 tokens per session 0dd66bd1e8fa
- 2mo ago First seen · 48 lines · 634 tokens per session scan A 9a29466737d7
main is a cursor rule published in the GitHub repository dkothule/ai-context (11 stars, last pushed 1mo ago), licensed MIT. It adds 776 tokens to every session, about $0.0031 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.
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angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
prefer-assertions-over-defensive-checks
Prefer assertions over defensive checks when data is guaranteed to be valid.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.
config-validation-and-normalization
Config validation and normalization responsibilities (defineConfig normalizes, the orm section schema validates and resolves paths, the loader reports per-section diagnostics).