wiki-arsenal

The fast, wiki-first lookup engine for "what do I use against this surface". Runs the four knowledge areas in parallel so a deep lookup is one wall-clock, not four serial reads. arsenal delegates here; the hunt-* skills each carry their own wiki-first qmd_query (MCP-independent) and can hand off here for a fast parallel lookup. Never hand-roll from memory when the wiki has the answer.

Input: a surface, service, or vuln-class (e.g. Jenkins on 8080, SSRF, Kerberoasting).

0. Cache check first (0 tokens on a repeat)

Slug the surface (lowercase, non-alnum -> -). If targets/<active-eng>/arsenal/<slug>.md exists, read and return it. Do not re-spend. (<active-eng> = the dir named in targets/active.md.)

Mode: quick (DEFAULT - fire it constantly)

One mcp__wiki-search__qmd_query over the whole index (add a qmd_search keyword pass when the surface is an exact product/CVE string). Group the hits under the four areas and return each as path -> one-line snippet:

• Techniques (wiki/techniques/)
• Payloads (wiki/payloads/)
• Tools (wiki/tools/)
• Cheatsheets (wiki/cheatsheets/)

Cost ~1-2k tokens, no subagents. This is what arsenal calls by default and what you fire on every new surface to raise wiki coverage cheaply. Stop here unless the surface is worth deep prep.

Mode: deep (opt-in - "deep"/"full arsenal", or a whole service/target worth prepping)

Dispatch FOUR parallel subagents in a SINGLE message (Agent tool), one per area, with model: haiku - each only reads its area and distils a card, which a lightweight model does well at a fraction of the cost (a full-model fan-out measured ~170k tokens; haiku cuts that hard). Each is told to search only its area, read the top 2-3 matching pages, and return a compact ready-to-use card for its area ONLY (nothing else), citing the page paths it used:

Agent	Searches	Returns
tools	wiki/tools/	the automated tool(s) to run + the exact command line
payloads	wiki/payloads/	ready-to-send payloads for the vuln-class
techniques	wiki/techniques/	the attack steps / chain
cheatsheets	wiki/cheatsheets/	quick copy-paste commands

Each agent scopes its search to its area: pass a path filter to qmd, or query the whole index and keep only wiki/<area>/ hits, then read those pages. The pages an agent reads stay in that agent's context and are discarded; you ingest only its card.

Merge the four returned cards into one arsenal card, four labelled sections plus a final Sources: line listing every page used.

Persist the deep card

Write the merged card to targets/<active-eng>/arsenal/<slug>.md (create the dir on demand) with frontmatter surface: and generated:. That is the cache step 0 reads next time.

Guardrails (token control)

• Default is quick (~1-2k tokens). Only go deep on request or for a real service/target.
• Deep is bounded: exactly 4 agents on model: haiku, each capped to the top 2-3 pages. The cost is isolated to the subagents; your main context only gains the merged card.
• The cache prevents re-spend on the same surface.

Hand off

The full class-specific methodology lives in the matching hunt-* skill. After the arsenal card, hand off to it (e.g. Skill(hunt-ssrf)) for the actual exploitation loop.