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/mikeprasad/aria-knowledge/autonpx skills add mikeprasad/aria-knowledge --skill autogit clone --depth 1 https://github.com/mikeprasad/aria-knowledgeWhat 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 | $0.00314 | $0.15441 |
| Opus 5 | $0.00157 | $0.07720 |
| Sonnet 5 | $0.00063 | $0.03088 |
| Haiku 4.5 | $0.00031 | $0.01544 |
Grade A, and why
auto 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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 · 491 lines · 314 tokens per session scan A 104ea7812a3f
auto is a skill published in the GitHub repository mikeprasad/aria-knowledge (17 stars, last pushed 6d ago), with no licence file. It adds 314 tokens to every session and 15,441 once invoked, about $0.0016 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
test-driven-development
TDD: enforce RED-GREEN-REFACTOR, tests before code.
autonomous-pipeline
Orchestrate the full AIDLC Autonomous Pipeline from a one-sentence requirement to a PR-ready delivery with TDD methodology. DDD drives judgment, SDD produces specs, TDD verifies delivery. Stages: Evaluate, Think, Plan, Build, Review, Test, Deliver, Reflect. Checkpoints on BLOCK or context limits. TRIGGER: "run…
remember
Routes user requests containing "remember", "recall", "checkpoint", "session", "todo", or "where were we" to the correct OpenEmpiric (OEM) MCP tool. Use when the user wants to persist, retrieve, or contextualize knowledge from project memory.
writing
将共享历史中的已验证事实和计算结果整理成符合受众、格式与长度约束的成稿。.
kayba-stage-5-action-plan
Triage each insight into discard/code-fix/prompt-fix and produce a prioritized action plan with specific recommendations. Trigger when the user says "run stage 5", "make action plan", "triage skills", or when invoked by the kayba-pipeline orchestrator. Requires eval outputs from stages 1-4.
kayba-pipeline
End-to-end agent evaluation and improvement pipeline. Takes a traces folder and optional HITL flag, then orchestrates sub-agents through 7 stages — each stage is its own skill invoked by a dedicated sub-agent. Trigger when the user says "run the pipeline", "kayba pipeline", "evaluate and fix", "full eval", "analyze…