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.
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 trpc-group/trpc-agent-go --skill fanout-analysisgit clone --depth 1 https://github.com/trpc-group/trpc-agent-goWrote 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/trpc-group/trpc-agent-go/fanout-analysis)<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.
<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>- NVIDIA SkillSpector warn
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.
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 | $0.00024 | $0.00604 |
| Opus 5 | $0.00012 | $0.00302 |
| Sonnet 5 | $0.00005 | $0.00121 |
| Haiku 4.5 | $0.00002 | $0.00060 |
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.
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
- Extract the decision or question, the relevant constraints, and the output format from the user's request.
- 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.
- Run those branches in parallel. Each branch should return concise findings, assumptions, and unresolved uncertainty rather than a polished final answer.
- 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.
- If the request requires a decision, have the synthesizer return a small
structured object with
recommendation,reasons, anduncertainties. Keep branch content as text unless a later control-flow decision genuinely needs typed fields. - 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.
parallelreturnsNonefor 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
structuredobject. Do not ask for JSON-looking text and parse it in the workflow.
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.
- 9d ago First seen · 56 lines · 24 tokens per session scan A 657929183e42
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.
Other skills, from other repositories
hephaestus-network
Use when the user types $hephaestus-network, /hep-network, or /agentlas-network, mentions @Hephaestus, or asks Agentlas to staff a durable goal from registered Local, owner Cloud, and public Hub agents or teams. The active host LLM staffs each turn; the exact roster remains goal-bound until explicit completion.
routing-card-authoring
Use whenever a build emits or repairs .agentlas/routing-card.json — the shared card contract for the single-agent builder, the team builder, and the packager. States what belongs in every field, which fields the hub can actually match on, and which fields silently break matching when a sentence leaks into them.
hep-storm
Run a force-robust Stormbreaker loop — route to real agents, execute a verified pipeline to completion.
hephaestus-upload
Use when the user types $hephaestus-upload, /hep-upload, or /agentlas-upload, or asks to upload, publish, or list an Agentlas agent or team. Ask Cloud (private) vs Agentlas Hub (public) FIRST, then publish through the bundled Hephaestus gate.
hep-graph
Build an Agentlas automation by describing it, list saved ones, or request a run.
hep-network
Staff a task from registered Local, owner Cloud, and public Hub agents.