fanout-analysis

fanout-analysis is a skill for Claude Code, Codex from trpc-group/trpc-agent-go. It costs 24 tokens per session (604 once invoked), scanned A, original, Apache-2.0.

A workflow for asking several independent analyses of the same question and combining their findings into one evidence-based answer. It chooses two to four useful viewpoints and keeps disagreements and missing evidence visible.

In plain words
What is it for?
Use it for research, planning, comparisons, reviews, and decisions where different kinds of evidence or reasoning should be considered separately before reaching a conclusion.
Why use it?
It reduces the risk of relying on one incomplete interpretation of a complex question. A separate synthesis step turns the findings into a recommendation when a decision is needed.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it for research, planning, comparisons, reviews, and decisions where different kinds of evidence or reasoning should be considered separately before reaching a conclusion.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/trpc-group/trpc-agent-go/fanout-analysis
About the project

tRPC-Agent-Go is a Go framework for building AI agent applications with language models, graph-based workflows, tools, memory, retrieval, evaluation, and observability. Go developers use it to create deployable agent systems that can integrate with A2A, AG-UI, and MCP. The catalogue add-ons provide reusable workflows and agent integrations for the framework.

trpc-group/trpc-agent-go · 1,770 stars · on GitHub · trpc-group.github.io

Install

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.

Any agent
npx skills add trpc-group/trpc-agent-go --skill fanout-analysis
Clone the repo
git clone --depth 1 https://github.com/trpc-group/trpc-agent-go

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for fanout-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/trpc-group/trpc-agent-go/fanout-analysis/github.svg)](https://agentmods.dev/skills/trpc-group/trpc-agent-go/fanout-analysis)
Your own site
<a href="https://agentmods.dev/skills/trpc-group/trpc-agent-go/fanout-analysis"><img src="https://agentmods.dev/badge/skills/trpc-group/trpc-agent-go/fanout-analysis/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.

agentmods 80×15 button for fanout-analysis

Your own site · 80×15
<a href="https://agentmods.dev/skills/trpc-group/trpc-agent-go/fanout-analysis"><img src="https://agentmods.dev/badge/skills/trpc-group/trpc-agent-go/fanout-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 604 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 33
    Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.
    Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00024 $0.00604
Opus 5 $0.00012 $0.00302
Sonnet 5 $0.00005 $0.00121
Haiku 4.5 $0.00002 $0.00060

Measured 9d ago against content hash 657929183e42, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

fanout-analysis 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 9d 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.

examples/dynamicworkflow/skills/skills/fanout-analysis/SKILL.md · 56 lines

How it starts

The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Parallel Analysis and Synthesis

Turn the user's request into a temporary fan-out/fan-in workflow. Keep the number and focus of branches appropriate to the request; do not create roles just to make the workflow look larger.

Process

  1. Extract the decision or question, the relevant constraints, and the output format from the user's request.
  2. Choose two to four independent analysis angles that cover different evidence or reasoning needs. Give every branch the same core question and only the context it needs. Do not let one branch depend on another branch's unfinished answer.
  3. Run those branches in parallel. Each branch should return concise findings, assumptions, and unresolved uncertainty rather than a polished final answer.
  4. Pass the ordered branch results, the original request, and the explicit decision criteria to a separate synthesis role. The synthesizer must distinguish agreement, disagreement, and missing evidence; it must not silently turn an unsupported claim into a fact.
  5. If the request requires a decision, have the synthesizer return a small structured object with recommendation, reasons, and uncertainties. Keep branch content as text unless a later control-flow decision genuinely needs typed fields.
  6. Return the synthesis and the key evidence trail. If a branch fails, keep that missing evidence explicit and follow the application's bounded failure policy; do not silently treat it as support or retry indefinitely.

Compilation Rules

  • Express independent branches with parallel([...]); preserve the input order when passing results to the synthesizer.
  • Use separate workflow-local Agent instances for each branch and for the synthesizer. Do not ask the synthesizer to redo every branch from memory.
  • Pass the original question, constraints, and branch outputs explicitly as inputs. A later stage must not depend on context that was only present in a previous Agent's prompt.
  • parallel returns None for a failed independent branch. Handle that value explicitly, and let the workflow or its caller decide whether a single, bounded rerun is appropriate; do not rely on exception-catching syntax or unbounded retries.
  • Keep the glue code small: create roles, pass JSON-compatible values, fan out, fan in, and return the result. Delegate substantive analysis to Agents.
  • Use tools=[] for roles that only reason over supplied inputs. Select a declared tool only for a branch whose task genuinely needs it, and keep mutating tools out of parallel branches unless their independence is clear.
  • If a structured synthesis is requested, read the Agent result's explicit structured object. Do not ask for JSON-looking text and parse it in the workflow.

Read the full file on GitHub · 56 lines

Changes

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.

  1. 9d ago First seen · 56 lines · 24 tokens per session scan A 657929183e42

Subscribe to this mod's changes

fanout-analysis is a skill published in the GitHub repository trpc-group/trpc-agent-go (1,770 stars, last pushed today), licensed Apache-2.0. It adds 24 tokens to every session and 604 once invoked, about $0.0001 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.