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 information-flow-control-gategit 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/information-flow-control-gate)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/information-flow-control-gate"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/information-flow-control-gate/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/information-flow-control-gate"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/information-flow-control-gate.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 | $0.00184 | $0.02403 |
| Opus 5 | $0.00092 | $0.01202 |
| Sonnet 5 | $0.00037 | $0.00481 |
| Haiku 4.5 | $0.00018 | $0.00240 |
Grade A, and why
information-flow-control-gate 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Information Flow Control Gate
Overview
Authentication answers "is this agent allowed to call this tool?" It cannot answer "is this DATA allowed to flow into this action?" When tools chain together, two new problems appear, and this gate is the deterministic layer that handles both.
Type matching (the NESTFUL problem). The NESTFUL benchmark shows even the
most advanced LLMs achieve only ~41% success on nested API calls when tool
relationships are implicit. They fail to recognize that getting COVID statistics
for a country requires first obtaining the country code; they struggle with type
matching between one API's output and another's input. Modeling REQUIRES_INPUT
and PRODUCES_OUTPUT types explicitly reduces multi-step reasoning to a graph
traversal: get_country_details("India") -> "IN" (type ISO_3166_1_alpha_2),
then get_covid_stats(location="IN").
Taint tracking (FIDES IFC). Every value carries a trust label from its
provenance. An email from an internal domain is TRUSTED; an external one is
UNTRUSTED. The taint propagates: mix trusted with untrusted and the result
inherits UNTRUSTED. A deterministic policy then blocks sensitive actions on
tainted data. Opaque-variable management hardens this: the LLM sees only an
opaque UUID reference, and must call read_variable to materialize content, so
malicious instructions embedded in untrusted content never directly steer the
model's reasoning. This stops two threats authentication cannot: agent tool
misuse (a legitimate tool used for a malicious purpose on tainted data) and
agent goal manipulation (untrusted instructions overriding the objective).
When to Use
- Tools chain: one tool's output feeds another tool's input
- The agent may ingest external/untrusted content (emails, scraped docs, third-party API responses) that could reach a sensitive action
- You want deterministic type-matched execution planning instead of hoping the LLM tracks variables between calls
- You need an auditable "why was this action blocked" answer
What ships with it
3 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 · 173 lines · 184 tokens per session scan A 4805736f7862
information-flow-control-gate is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 184 tokens to every session and 2,403 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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