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 rules/exboys/skilllite/spec-injection-indexgit clone --depth 1 https://github.com/EXboys/skillliteWhat 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 | $0.00374 | $0.00374 |
| Opus 5 | $0.00187 | $0.00187 |
| Sonnet 5 | $0.00075 | $0.00075 |
| Haiku 4.5 | $0.00037 | $0.00037 |
Grade A, and why
spec-injection-index 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 3d 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
Spec Injection Router
spec/ + tasks/ apply to developing this repo, not to end-user or shipped-app runtime configuration (see Repository scope in spec/README.md).
Before implementation, load spec/README.md and choose injected specs using its mapping and deterministic rules.
Required Workflow
- Always inject
spec/verification-integrity.mdfirst — this is the highest-priority spec and applies unconditionally. - Always inject
spec/task-artifact-language.mdfor task execution records (tasks/TASK-.../*). - Determine task type:
architecture,security,sandbox,agent,commands,mcp,python-sdk,docs-only, ormixed.
- Build
[Injected Specs]fromspec/README.md. - Follow all MUST / MUST NOT / CHECKLIST items in each injected spec.
- If multiple mappings match, inject all matched specs.
- For any Rust code change, always include
spec/rust-conventions.mdandspec/testing-policy.md. - If user-visible behavior, env vars, commands, architecture docs, or release matrix change, include
spec/docs-sync.md.
Output Convention
At task start, explicitly declare:
[Injected Specs]
- spec/<file-a>.md
- spec/<file-b>.md
...
At task end, confirm:
spec/verification-integrity.mdchecklist was completed (anti-hallucination, anti-false-positive),- which specs were injected,
- which checklists were completed,
- which verification commands were run (with actual output, not model assertions).
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.
- 3d ago First seen · 41 lines · 374 tokens per session scan A be53e69c8929
spec-injection-index is a cursor rule published in the GitHub repository EXboys/skilllite (167 stars, last pushed 7d ago), licensed MIT. It adds 374 tokens to every session, about $0.0019 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 cursor rules, from other repositories
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Cursor rule "global" from KroMiose/nekro-agent, covering 项目背景(简要), 开发规范, 文档, 开发指南 and 功能/页面开发完整流程.
ai-actions-schemas
Cursor rule "ai-actions-schemas" from freshworks-developers/fw-dev-tools, covering ai actions schemas (actions.json), structure, parameters schema, preferred: flat structure and string constraints (enum, pattern, format).
frontend-rules
前端 React 服务开发指南.
cognitive-principle-check
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cursorrules
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cognitive-insight-guard
洞见守卫。每条用户消息后,语义判断是否包含视角纠正、本质澄清、设计原则等认知价值内容。检测到时在回复末尾轻量提示是否沉淀。.