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 audit-agent-run-evidencegit 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/audit-agent-run-evidence)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/audit-agent-run-evidence"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/audit-agent-run-evidence/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/audit-agent-run-evidence"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/audit-agent-run-evidence.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 | $0.00049 | $0.01748 |
| Opus 5 | $0.00024 | $0.00874 |
| Sonnet 5 | $0.00010 | $0.00350 |
| Haiku 4.5 | $0.00005 | $0.00175 |
Grade A, and why
audit-agent-run-evidence 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 6d 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:
- audit-agent-run-evidence — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Agent Run Evidence
Overview
Turn an end-to-end success statement into independently decidable claims. Reconstruct what happened from available records, grade each claim against the strongest witness, and keep missing evidence distinct from failure.
This is a read-only audit. Do not rerun tools, approve actions, resume workers, deploy artifacts, or modify evidence unless the user separately authorizes those actions.
When to Use
- Auditing a completed or interrupted agent run from traces and artifacts.
- Checking whether an agent's end-to-end success claim is actually supported.
- Reviewing MCP, gateway, sandbox, checkpoint, retry, memory, approval, or deployment evidence.
- Separating autonomous success from human-assisted or merely requested outcomes.
Do not use this skill to design instrumentation for a future run or to perform the missing actions. It evaluates evidence that already exists.
Establish the Contract
Record these inputs before judging the run:
- declared goal and terminal success criteria;
- run, workflow, task, and parent identifiers;
- immutable code, configuration, model, prompt, tool-schema, and artifact revisions when available;
- actors and trust boundaries: orchestrator, worker, sandbox, MCP server, gateway, human approver, CI, and deployment platform;
- retry, deadline, token, cost, concurrency, and human-escalation budgets;
- supplied evidence inventory and known collection gaps.
Do not silently strengthen the original success criteria. Do not weaken them to match the evidence that happens to exist.
Build a Claim Ledger
Split the overall claim into atomic predicates. Give every row a stable claim ID.
| Field | Required content |
|---|---|
claim_id |
Stable identifier |
predicate |
One falsifiable statement |
required_witness |
Source that can independently prove it |
evidence_refs |
Exact event, log, artifact, or record IDs |
counterevidence_refs |
Conflicting records |
coverage |
Required instances versus observed instances |
verdict |
proven, partially_proven, contradicted, or not_proven |
gap |
Missing field, actor, interval, or verification |
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.
- 6d ago First seen · 166 lines · 49 tokens per session scan A e704719075d0
audit-agent-run-evidence is a skill published in the GitHub repository sickn33/agentic-awesome-skills (46,230 stars, last pushed 2d ago), licensed MIT. It adds 49 tokens to every session and 1,748 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.
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