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 5.5 | $0.00014 | $0.00998 |
| Sonnet 5.5 | $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 1mo 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
2 near-identical copies found in the catalogue:
- agent-harness-fault-injection — 100% identical, 0 lines differ
- 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.
- 1mo 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 (48,548 stars, last pushed 3d ago), licensed MIT. It adds 34 tokens to every session and 2,494 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 2026-08-30.
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-harness-fault-injection
Use when an agent workflow needs deterministic recovery evidence for sandbox, MCP/tool, worker, checkpoint, memory, or orchestration failures.
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.
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.
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.
agent-tool-replay-test
Replay recorded agent tool/LLM trajectories against deterministic fixtures to test argument validation, errors, idempotency, and side-effect boundaries—separate from live tool-selection quality.