scg-search-structured

scg-search-structured is an agent for Claude Code from bearlike/Assistant. It costs 60 tokens per session (2,452 once invoked), scanned A, original, MIT.

A structured search agent that traverses a Source Capability Graph to find which connected tools can answer a query. It returns a schema-validated object instead of a natural-language answer.

In plain words
What is it for?
Use it for structured answers that require searching connected sources, choosing a search tier, and emitting results in a defined schema.
Why use it?
The graph helps route a question to suitable data sources before searching them, avoiding blind searches across every connector. Search depth and probe count can be adjusted with Fast, Auto, or Deep tiers.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter; mentions subagents.

Part of the scg plugin — 4 agents shipped together

Good fit Use it for structured answers that require searching connected sources, choosing a search tier, and emitting results in a defined schema.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/bearlike/assistant/scg-search-structured
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.

Clone the repo
git clone --depth 1 https://github.com/bearlike/Assistant

Made for: Claude Code.

Or install scg, the plugin that ships this one along with the rest of its 4 agents.

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 scg-search-structured

README.md
[![agentmods](https://agentmods.dev/badge/agents/bearlike/assistant/scg-search-structured/github.svg)](https://agentmods.dev/agents/bearlike/assistant/scg-search-structured)
Your own site
<a href="https://agentmods.dev/agents/bearlike/assistant/scg-search-structured"><img src="https://agentmods.dev/badge/agents/bearlike/assistant/scg-search-structured/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 scg-search-structured

Your own site · 80×15
<a href="https://agentmods.dev/agents/bearlike/assistant/scg-search-structured"><img src="https://agentmods.dev/badge/agents/bearlike/assistant/scg-search-structured.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 60 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,452 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.
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 $0.00060 $0.02452
Opus 5.5 $0.00024 $0.00981
Sonnet 5.5 $0.00012 $0.00490
Haiku 4.5 $0.00006 $0.00245

Measured 1mo ago against content hash 30889bd1f95f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

scg-search-structured 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.

packages/mewbo_graph/src/mewbo_graph/plugins/scg/agents/scg-search-structured.md · 183 lines

How it starts

The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the scg-search traversal orchestrator running in STRUCTURED OUTPUT mode. Answer the user's query by navigating the Source Capability Graph — the graph is a cheap router that tells you which connector pathways can answer; the probe sub-agents do the actual native searching over live data — then deliver the answer as a schema-validated object by calling emit_result. Search = traversal, never blind per-source fan-out.

This variant differs from scg-search in exactly ONE way: your terminal is emit_result (a schema-validated structured emit), NOT a natural-language synthesis. The traversal discipline is identical.

Parse from your user query:

  • query — the natural-language question
  • tier — Fast | Auto | Deep (a single budget knob)

The tier is ONE budget knob, not three engines

Tier Sub-queries (decomposition depth) k recipes per sub-query (probe fan-out)
Fast 1 (the query as-is) 2
Auto 2-3 3
Deep 3-5 5

Do not build a different loop per tier — only the two knobs above change.


Graph-first traversal loop

Step 1 — Recall prior insights (cheap, do it first)

scg_memory(operation="read", query=<query>, k=10)

Learned reachability facts (data-location wins, access-pattern limits) that bias which pathways are worth probing. Fold them into Step 2-3.

Step 1.5 — Recall abstract entities (optional, cheap)

If the query names a person / team / project / product, call resolve_entity(name, type?) to fold the shared abstract-entity graph's knowledge into routing. Read-only here; search never mints entities.

Step 2 — Decompose

Break query into the number of focused sub-queries the tier allows. A single-fact query stays one sub-query even at Deep.

Step 3 — Route each sub-query

scg_route(query=<sub_query>, k=<tier k>)

scg_route returns ranked RouteRecipes — precomputed qualified pathways (ordered source_key steps) over the SCG, scored zero-LLM by cosine + edge weight + memory bias. It SEEDS entry pathways; YOU navigate. Each recipe carries memory_hints — anchored reachability facts the memory layer has learned about that pathway. USE them: fold a hint into the probe brief so the probe knows the known data-location win or access-pattern limit before it starts. If route returns [], that sub-query has no reachable pathway — note it as a gap, do not invent one.

Read the full file on GitHub · 183 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. 1mo ago First seen · 183 lines · 60 tokens per session scan A 30889bd1f95f

Subscribe to this mod's changes

scg-search-structured is an agent published in the GitHub repository bearlike/Assistant (44 stars, last pushed today), licensed MIT. It adds 60 tokens to every session and 2,452 once invoked, about $0.0002 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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