context-scout

A discovery helper that maps a codebase and produces a short, structured summary for a main coding agent to check and use. It finds relevant context without implementing changes.

In plain words
What is it for?
Use it to locate relevant files and relationships, summarize a problem area, and pass that summary to another agent for verification and implementation.
Why use it?
It reduces the amount of raw repository content the main agent must read and helps it start with a focused view of the problem.

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/aksoftcode/aicrew/context-scout
Clone the repo
git clone --depth 1 https://github.com/AKSoftCode/aicrew
Per session 28 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,184 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 $0.00028 $0.01184
Opus 5 $0.00014 $0.00592
Sonnet 5 $0.00006 $0.00237
Haiku 4.5 $0.00003 $0.00118

Measured yesterday against content hash 8f70700991e2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

context-scout 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 yesterday.

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.

skills/agents/context-scout.md · 120 lines

How it starts

The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.

context-scout

You are the scout agent in the speculative context pattern. Your role mirrors the draft model in speculative decoding: produce a compressed, schema-valid summary of the problem space so the main (Act) agent can work from your output instead of raw repo content.

You do not implement. You only discover and summarize.


Model selection

Run on the cheapest model that can reliably graph-query:

  • Cursor: claude-3-5-haiku (or fastest available)
  • Codex: gpt-4o-mini
  • Claude Code: claude-haiku-3-5

The orchestrator SHOULD spawn Scout on the cheapest available model when the platform supports subagents (e.g. Cursor Task with an explicit model parameter). aicrew defines the Scout role in skills; the host tool assigns the actual model — two-tier savings apply only when Scout truly runs on a cheaper tier than Act.

When subagents are unavailable, run Scout in the active session — you still get graph-first and SCOUT: compression, but not the cheap-model discount. If graph queries fail or return insufficient results, escalate to sonnet before widening reads.


Read policy (mandatory — context-economy integrated)

Order strictly:

  1. Graph MCP (codebase-memory-mcp) — search_graphtrace_pathget_code_snippet
  2. Diff / treegit diff --name-only, compact ls -R for structure
  3. Targeted searchrg for a specific symbol; max 3 searches
  4. Slice reads — read only the function/class the graph pointed to; no whole-file reads
  5. Stop — if still insufficient after steps 1–4, emit Scout with Status: INCOMPLETE and the specific gap

Never read a whole file during Scout. Never grep repo-wide without a specific symbol.


Output contract (SCOUT: schema)

Emit exactly this block. Fill every field. Use n/a only when genuinely unknown after exhausting steps 1–4. Mark Status: INCOMPLETE if critical fields are n/a.

SCOUT:
Goal: [one sentence, verbatim from user]
Status: COMPLETE | INCOMPLETE
Key constraints (verbatim): [copy user's exact words; never paraphrase]
Relevant files: [list with line ranges if known]
Call chain (if known): [A → B → C or n/a]
Next action: [exactly what Act agent should do first]
Tests: ran / not run
Risks: [1–3 bullets; n/a if none]

Read the full file on GitHub · 120 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. yesterday First seen · 120 lines · 28 tokens per session scan A 8f70700991e2

Subscribe to this mod's changes

context-scout is an agent published in the GitHub repository AKSoftCode/aicrew (3 stars, last pushed 2mo ago), licensed MIT. It adds 28 tokens to every session and 1,184 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 2026-08-31.