scg-search

A search coordinator that uses a map of available data sources to find which connectors can answer a question, then searches those paths.

In plain words
What is it for?
Use it for natural-language searches across connected sources, with Fast, Auto, or Deep search levels and remembered routing insights.
Why use it?
It avoids searching every source blindly and adjusts the amount of query breakdown and parallel searching to the requested budget level.

Agent

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.

agentmods
npx agentmods add agents/bearlike/assistant/scg-search
Clone the repo
git clone --depth 1 https://github.com/bearlike/Assistant
Per session 59 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,992 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00059 $0.02992
Opus 5 $0.00030 $0.01496
Sonnet 5 $0.00012 $0.00598
Haiku 4.5 $0.00006 $0.00299

Measured 2d ago against content hash 9469ce9cc107, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

scg-search 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 2d 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.md · 195 lines

How it starts

The opening of the file, as written. The whole thing — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the scg-search traversal orchestrator. 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. Search = traversal, never blind per-source fan-out.

Parse from your user query:

  • query — the natural-language question
  • tierFast | Auto | Deep (a single budget knob; see below)

The tier is ONE budget knob, not three engines

Tier sets decomposition depth + probe count over a single traversal loop. It is NOT a number of verification rounds — there are no verification rounds.

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.


Traversal loop

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

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

These are 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. This is the SAME multiplex the wiki enrich phase populates — 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

For 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; the graph has already done the cheap pre-rank, so trust the ordering. Each recipe carries memory_hints — anchored reachability facts the memory layer learned about that pathway (data-location wins, access-pattern limits). USE them: fold a hint into the probe brief so the probe starts knowing the known win/limit. 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 · 195 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. 2d ago First seen · 195 lines · 59 tokens per session scan A 9469ce9cc107

Subscribe to this mod's changes

scg-search is an agent published in the GitHub repository bearlike/Assistant (41 stars, last pushed 8d ago), licensed MIT. It adds 59 tokens to every session and 2,992 once invoked, about $0.0003 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.

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