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 agenttrace-session-auditgit 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/agenttrace-session-audit)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/agenttrace-session-audit"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/agenttrace-session-audit/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/agenttrace-session-audit"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/agenttrace-session-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 112 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00034 | $0.01280 |
| Opus 5 | $0.00017 | $0.00640 |
| Sonnet 5 | $0.00007 | $0.00256 |
| Haiku 4.5 | $0.00003 | $0.00128 |
Grade A, and why
agenttrace-session-audit 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 5d 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
8 near-identical copies found in the catalogue:
- agenttrace-session-audit — 100% identical, 0 lines differ
- agenttrace-session-audit — 100% identical, 0 lines differ
- agenttrace-session-audit — 100% identical, 0 lines differ
- agenttrace-session-audit — 100% identical, 0 lines differ
- agenttrace-session-audit — 100% identical, 0 lines differ
- agenttrace-session-audit — 100% identical, 0 lines differ
- agenttrace-session-audit — 100% identical, 0 lines differ
- agenttrace-session-audit — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agenttrace Session Audit
Overview
Use this skill to inspect local AI coding-agent sessions with agenttrace. It focuses on the process behind a run: token and cost spikes, tool failures, retry loops, latency gaps, anomalies, health scores, and session-to-session diffs.
agenttrace is local-first and reads session logs from tools such as Claude Code, Codex CLI, Gemini CLI, Aider, Cursor exports, OpenCode, Qwen Code, Kimi, and generic JSON or JSONL traces.
When to Use This Skill
- Use when a user asks why an AI coding run was slow, expensive, shallow, or unreliable.
- Use when reviewing local agent logs before retrying a failed or suspicious task.
- Use when building a lightweight CI health gate for AI-assisted coding sessions.
- Use when comparing two attempts and looking for changed tool paths, retries, or cost patterns.
How It Works
Step 1: Discover Available Sessions
Prefer an installed agenttrace binary when it is available on PATH. If the
current repository is luoyuctl/agenttrace, use go run ./cmd/agenttrace
instead.
agenttrace --doctor
agenttrace --overview
If no sessions are detected, report the directories checked by --doctor and
ask for the exported session file or log directory.
Step 2: Produce a Human-Readable Audit
Use Markdown when the user wants a concise report they can inspect or share.
agenttrace --overview -f markdown -o agenttrace-overview.md
In the report, lead with the highest-risk sessions and explain why they matter: critical anomalies, repeated tool failures, token or cost waste, long latency gaps, low health scores, and suspiciously shallow sessions.
Step 3: Inspect One Session or Directory
Use the latest session for a quick check, or pass an explicit export path when the user provides one.
agenttrace --latest
agenttrace --latest -f json
agenttrace path/to/session-or-export.json
agenttrace --overview -d path/to/session-dir
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.
- 5d ago First seen · 159 lines · 34 tokens per session scan A 495a107a6b8f
agenttrace-session-audit is a skill published in the GitHub repository sickn33/agentic-awesome-skills (46,230 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 1,280 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 2026-09-05.
Other skills, from other repositories
agenttrace-session-audit
Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.
agenttrace-session-audit
Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.
agenttrace-session-audit
Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.
agenttrace-session-audit
Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.
agenttrace-session-audit
Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.
monitoring-setup
Set up comprehensive monitoring with Prometheus, Grafana, and alerting. Covers metrics, dashboards, SLOs, and on-call runbooks.