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 agent-harness-fault-injectiongit 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/agent-harness-fault-injection)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/agent-harness-fault-injection"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/agent-harness-fault-injection/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/agent-harness-fault-injection"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/agent-harness-fault-injection.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 | $0.00034 | $0.02494 |
| Opus 5 | $0.00017 | $0.01247 |
| Sonnet 5 | $0.00007 | $0.00499 |
| Haiku 4.5 | $0.00003 | $0.00249 |
Grade A, and why
agent-harness-fault-injection 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 13d 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:
- agent-harness-fault-injection — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Harness Fault Injection
Overview
Use a deterministic, non-production fault schedule to test whether an agent workflow preserves state, budgets, safety boundaries, and evidence when a dependency fails. The output is a small fault matrix, an event timeline, and a verdict that distinguishes recovered, contained, unrecoverable, and inconclusive runs.
When to Use This Skill
- Use when a multi-step agent, state machine, loop, or multi-agent workflow has a new recovery path.
- Use when sandbox execution, an MCP/tool call, a worker, a checkpoint store, or memory can time out or disappear.
- Use before claiming retry, resume, deadline, isolation, or partial-failure behavior is production-ready.
- Use when a regression needs reproducible failure evidence instead of a random chaos run.
Do not use this skill against a production target, real user data, live credentials, or an unbounded external service. Convert those cases to a local simulator or an authorized staging harness first.
Safety and Boundary Preconditions
- Freeze the workflow revision, model/prompt configuration, tool schemas, seed, input fixture, timeout, retry budget, deadline, and expected terminal states.
- Run in a disposable sandbox with synthetic inputs and stubbed tools. Keep network disabled unless the test explicitly needs a local test server.
- Make every injected failure an in-memory or fixture-controlled event. Never delete real data, revoke real credentials, kill an unrelated process, or mutate a live service to create a failure.
- Record the test scope and a run identifier before starting. A missing scope,
fixture, or recovery contract makes the verdict
inconclusive.
Recovery Contract
Write the invariant before injecting a fault. A useful contract names the state that must survive and the side effects that must not repeat:
After recovery, resume from the latest durable checkpoint, preserve the task
identity and safety policy, spend no more than the remaining retry/deadline
budget, and commit each externally visible effect at most once.
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.
- 13d ago First seen · 251 lines · 34 tokens per session scan A b64b7afa7bbc
agent-harness-fault-injection is a skill published in the GitHub repository sickn33/agentic-awesome-skills (46,288 stars, last pushed 3d ago), licensed MIT. It adds 34 tokens to every session and 2,494 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 2026-08-30.
Other skills, from other repositories
agent-harness-fault-injection
Use when an agent workflow needs deterministic recovery evidence for sandbox, MCP/tool, worker, checkpoint, memory, or orchestration failures.
agent-tool-replay-test
Replay recorded AI tool calls against deterministic fixtures to test argument validation, error handling, and side-effect boundaries.
ai-evaluation-dataset
Build a versioned JSONL evaluation dataset for an AI workflow, with acceptance criteria, held-out cases, and leakage checks.
llm-json-contract-check
Validate AI-generated JSON against an application's schema and business rules, distinguishing refusals and truncation from malformed output.
prompt-regression-gate
Compare prompt revisions on a frozen AI evaluation set with paired runs, slice-level regressions, and explicit release thresholds.
agent-injection-boundary-test
Test an authorized AI agent's handling of instructions embedded in retrieved documents or tool outputs using harmless canaries.