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 AnthonyAlcaraz/agentic-graph-rag-skills --skill semantic-backprop-attributorgit clone --depth 1 https://github.com/AnthonyAlcaraz/agentic-graph-rag-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/anthonyalcaraz/agentic-graph-rag-skills/semantic-backprop-attributor)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/semantic-backprop-attributor"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/semantic-backprop-attributor/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/anthonyalcaraz/agentic-graph-rag-skills/semantic-backprop-attributor"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/semantic-backprop-attributor.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 | $0.00183 | $0.02602 |
| Opus 5 | $0.00092 | $0.01301 |
| Sonnet 5 | $0.00037 | $0.00520 |
| Haiku 4.5 | $0.00018 | $0.00260 |
Grade A, and why
semantic-backprop-attributor 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 11d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Semantic Backpropagation Attributor
Overview
Agents are graphs, not pipelines. The dangerous failure mode in a self-evolving system is not a bad update. It is a good update to one node that silently breaks another. In a deeply interconnected graph, improving a component in isolation causes "action at a distance" failures that are hard to trace. Semantic backpropagation is the mechanism that prevents this.
The idea adapts the chain rule. In numerical backpropagation, gradients flow backward through a computational graph, updating each parameter by how it contributed to the loss. Semantic backpropagation does the same in natural language: the gradient is a structured description of the required change, and it flows backward through the execution graph from the point of failure.
Neighbor-awareness is the decisive part. When generating feedback for node v based on what successor w needed, the feedback includes not just v's output and w's error but the outputs of ALL OTHER predecessors of w. That context is what makes the feedback precise.
The chapter's concrete example: an Extractor pulls "Revenue: $10M", a CurrencyConverter converts it to "EUR 9.5M", and a Validator (which also received a DateChecker's "Date: 2022") flags that the exchange rate was 0.9, not 0.95. Without neighbor context, feedback to the Extractor reads "your $10M led to a conversion error" and the Extractor might wrongly change its extraction. With neighbor context, the error is correctly assigned to the CurrencyConverter's rate lookup and the Extractor is left unchanged. the neighbor-aware feedback example shows the same shape for a DevOps CausalAttributionNode, with ChangelogRetrieval and KnowledgeGraphQuery as the neighbor predecessors.
Honesty note on the metaphor: "backpropagation" here is an analogy, not a mechanism. A numerical gradient is exact and deterministic; this skill's "gradient" is credit assignment produced by LLM judgment over the execution graph — structured, neighbor-aware, and far better than unstructured blame, but still a hypothesis about causality, not a derivative. Treat every attribution as a claim to verify (rerun the trace with the blamed node patched) before committing an intervention on it. What IS deterministic in this skill: the graph traversal, the neighbor-context assembly, and the routing of the verdict.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 182 lines · 183 tokens per session scan A 1c5fae8aaebe
semantic-backprop-attributor is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 183 tokens to every session and 2,602 once invoked, about $0.0009 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 2026-08-31.
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