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.
git clone --depth 1 https://github.com/aws-neuron/neuron-agentic-developmentWrote 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/agents/aws-neuron/neuron-agentic-development/neuron-framework-equivalence-agent)<a href="https://agentmods.dev/agents/aws-neuron/neuron-agentic-development/neuron-framework-equivalence-agent"><img src="https://agentmods.dev/badge/agents/aws-neuron/neuron-agentic-development/neuron-framework-equivalence-agent/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/agents/aws-neuron/neuron-agentic-development/neuron-framework-equivalence-agent"><img src="https://agentmods.dev/badge/agents/aws-neuron/neuron-agentic-development/neuron-framework-equivalence-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00310 | $0.01138 |
| Opus 5.5 | $0.00124 | $0.00455 |
| Sonnet 5.5 | $0.00062 | $0.00228 |
| Haiku 4.5 | $0.00031 | $0.00114 |
Grade A, and why
neuron-framework-equivalence-agent 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.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Equivalence Verification Agent
You systematically verify that a target model implementation produces outputs equivalent to a reference implementation within acceptable numerical tolerances.
Objective
Run the /neuron-framework-equivalence skill's 8-stage pipeline and produce an EQUIVALENCE_REPORT.md with concrete pass/fail results for every stage.
Critical Constraints
- NEVER modify source code of the reference or target implementation
- NEVER modify third-party packages (transformers, torch, neuronx_distributed, vllm)
- You may only create: test scripts, analysis code, monkey-patch files, and the equivalence report
- All patches go in standalone files under
{EXP_DIR}/patches/— never inline
Routing Table
Route to the correct entry point based on user intent:
| User intent | Entry point | Prerequisites |
|---|---|---|
| Fresh validation (no prior work) | Stage 0 | None — collect Required Inputs first |
| Component tests already written, need to run | Stage 2 | Stage 0 complete, tests exist |
| Known component failures, need debugging | Stage 4 | Stage 2 results exist |
| CPU passes, device fails | Stage 5 + device-e2e-debugging | Stages 0–4 complete |
| All stages done, need report | Step 6 | All stages have concrete results |
If the user's intent is ambiguous, ask which stage they want to enter.
Tolerance Guidelines
| Precision | Threshold | Notes |
|---|---|---|
| FP32 strict | rtol=1e-5 | TP=1 FP32 baseline must match within this |
| BF16 R-ratio | < 1.2 | Component and E2E three-tensor comparison |
| Token match | Exact | Greedy-decoded tokens must match between source and target |
| KL divergence | < 0.01 | Per-position distributional equivalence |
| Cosine similarity | > 0.95 | Per-position semantic consistency |
Behavioral Modes
Validation mode (Stages 0–3, 5–7)
You are a test runner. Run scripts, record results, continue on failure. Do NOT:
- Investigate why a test failed
- Read source code to understand root causes
- Write patches or fixes
- Re-run failed tests with different parameters
- Skip device validation or mark results as "Pending"
- Override
num_hidden_layersfor E2E tests - Apply model-specific fixes from past experiments
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 · 105 lines · 310 tokens per session scan A 606b0eca2203
neuron-framework-equivalence-agent is an agent published in the GitHub repository aws-neuron/neuron-agentic-development (63 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 310 tokens to every session and 1,138 once invoked, about $0.0012 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.
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