AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.
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 sickn33/agentic-awesome-skills --skill agents-mdgit clone --depth 1 https://github.com/sickn33/agentic-awesome-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/sickn33/agentic-awesome-skills/agents-md)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/agents-md"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/agents-md/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/sickn33/agentic-awesome-skills/agents-md"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00031 | $0.01881 |
| Opus 5.5 | $0.00012 | $0.00752 |
| Sonnet 5.5 | $0.00006 | $0.00376 |
| Haiku 4.5 | $0.00003 | $0.00188 |
Grade A, and why
agents-md 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 11d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- agents-md — 100% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Maintain AGENTS.md from repository evidence
Overview
Create or improve agent instructions that help a coding agent change the repository correctly without rediscovering its workflow. Base every repository-specific command, path, and rule on evidence in the current checkout.
Prefer a focused diff over a wholesale rewrite. There is no universal line limit, required section list, symlink layout, or commit-attribution policy; follow the repository's own needs and maintainer intent.
When to Use
- The user asks to create, update, shorten, or audit
AGENTS.md. - A monorepo needs root instructions plus narrower package-level overrides.
- Existing agent instructions contain stale commands, duplicated policy, or unsupported claims.
- The user wants to reconcile
AGENTS.mdwithCLAUDE.md,.github/copilot-instructions.md, or other repository instruction files.
Use @agents-generator instead when the task specifically calls for its
packaged generation modes, assets, or backup workflow. Use this skill when a
maintainer-readable, evidence-first edit is the primary goal.
How It Works
1. Preserve existing intent
Before writing, read every instruction file that applies to the target path,
including existing AGENTS.md files and relevant tool-specific files such as
CLAUDE.md, GEMINI.md, .github/copilot-instructions.md, and
.github/instructions/*.instructions.md.
- Improve an existing
AGENTS.mdin place when possible. - Preserve accurate maintainer-authored rules and repository-specific policy.
- Do not replace another tool's instruction file with a symlink unless the user requests it and repository evidence shows identical content is desired.
- Do not silently choose between conflicting instructions. Follow the higher-priority applicable rule, or ask when the intended policy cannot be established from the repository.
2. Build a bounded evidence map
Inspect only enough of the repository to establish how work is actually done:
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.
- 11d ago Changed · -2 lines 08e8b7c38e14
- 1mo ago First seen · 218 lines · 31 tokens per session scan A bec0ca6a8a3d
agents-md is a skill published in the GitHub repository sickn33/agentic-awesome-skills (48,503 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 1,881 once invoked, about $0.0001 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 2026-09-05.
Other skills, from other repositories
ai-citation-verification
Verify AI citations across structural schema, resolvability (registry/URL/DOI), and semantic support of each claim by the cited span—not the whole context. Use for RAG answers, research agents, and regulated audit trails.
ai-evaluation-dataset
Build versioned JSONL evaluation datasets for AI workflows with golden/regression/holdout layers, leakage checks, rubrics, and provenance hashes. Use when creating or revising eval sets before comparing models, prompts, or RAG configs.
llm-json-contract-check
Validate AI-generated JSON against schema and business rules, separating refusals, truncation, parse failures, schema violations, and domain invariant breaches. Use before any tool call, DB write, or external side effect consumes model output.
prompt-regression-gate
Gate prompt, model, tool-schema, or packer changes with paired runs on a frozen eval set, slice-level regression checks, relative baselines, and explicit go/no-go thresholds. Use before merging prompt PRs or swapping models.
rag-retrieval-audit
Diagnose missing evidence in a RAG pipeline with labeled queries, chunk/trace inspection, Recall@K/MRR/nDCG, and stage-isolated failure taxonomy. Use when retrieval looks fine but answers fail, or when index/chunk/filter/rerank changes need before/after proof.
ai-pii-redaction-review
Privacy-review AI payloads with field-aware redaction, typed stable placeholders, leakage scans across prompts/logs/tools/RAG, and residual-risk reporting before external submission. Use before sending data to cloud LLMs or sharing traces.