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 agentmods add skills/eigenwise/atomic-agents/troubleshootnpx skills add Eigenwise/atomic-agents --skill troubleshootgit clone --depth 1 https://github.com/Eigenwise/atomic-agentsWhat 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 | $0.00103 | $0.01429 |
| Opus 5 | $0.00051 | $0.00714 |
| Sonnet 5 | $0.00021 | $0.00286 |
| Haiku 4.5 | $0.00010 | $0.00143 |
Grade A, and why
troubleshoot 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Troubleshoot an Atomic Agents App
Diagnostic workflow for the "it's broken" moment. Most atomic-agents failures are one of a dozen known causes with mechanical fixes. Match the symptom, apply the fix, verify by re-running.
Workflow
- Capture the failure. Get the exact traceback or wrong-output sample plus the code that produces it (agent construction, schemas, client wiring). If the user pasted only a fragment of the error, ask for the full traceback before guessing.
- Match against the symptom table below. The quoted strings are the framework's real messages — match on them.
- Apply the fix and re-run the failing snippet. Do not declare the problem solved without a passing re-run.
- No match? Load the reference for the failing area (table at the bottom) and reason from there. For a whole-codebase audit rather than one failure, delegate to the
atomic-reviewersubagent instead.
Symptom table
Import and definition errors
| Symptom | Cause | Fix |
|---|---|---|
ImportError/ModuleNotFoundError on atomic_agents.lib.*, atomic_agents.agents.base_agent, or BaseAgent |
v1 import paths, removed in v2 | Import from the top level: from atomic_agents import AtomicAgent, AgentConfig, BaseIOSchema, BaseTool; context pieces from atomic_agents.context |
ValueError: <Name> must have a non-empty docstring to serve as its description at import time |
BaseIOSchema subclass without a docstring |
Add a docstring describing the schema — it flows into the LLM prompt, so write it for the model |
ValidationError when constructing AgentConfig, complaining about client |
Raw provider SDK client passed; AgentConfig.client requires an Instructor-wrapped client |
Wrap it: instructor.from_openai(...), instructor.from_anthropic(...), instructor.from_genai(...) |
TypeError about missing type parameters, or output typed as BasicChatOutputSchema when a custom schema was expected |
AtomicAgent instantiated without generics |
Write AtomicAgent[InputSchema, OutputSchema](config=...) — the type parameters carry runtime information (they drive Instructor's response_model) |
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.
- yesterday First seen · 67 lines · 103 tokens per session scan A 51951ffc4a31
troubleshoot is a skill published in the GitHub repository Eigenwise/atomic-agents (6,213 stars, last pushed 7d ago), licensed MIT. It adds 103 tokens to every session and 1,429 once invoked, about $0.0005 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.
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