| name | wiki-arsenal |
| description | Fast PARALLEL wiki lookup engine over wiki/techniques + wiki/payloads + wiki/tools + wiki/cheatsheets for a surface/service/vuln-class. Two modes - quick (one qmd search, cheap, fire constantly) and deep (4 parallel subagents, one per area, merged ready-to-use arsenal card, cached). This is the fast path arsenal that `arsenal` delegates to; the hunt-* skills each inline their own qmd_query and can hand off here for a parallel lookup. Use for "what do I use against <surface>", "arsenal for <X>", "deep/full arsenal", "tool + payload + technique + cheatsheet for <X>", "fast wiki lookup", "parallel wiki search", any "how do I attack/exploit <service|vuln-class>" where you want the documented tooling + payloads before hand-rolling. |
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.
