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.
npx agentmods add skills/0dayinc/pwn/dispatchnpx skills add 0dayInc/pwn --skill dispatchgit clone --depth 1 https://github.com/0dayInc/pwnWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00021 | $0.00480 |
| Opus 5 | $0.00010 | $0.00240 |
| Sonnet 5 | $0.00004 | $0.00096 |
| Haiku 4.5 | $0.00002 | $0.00048 |
Grade A, and why
pwn-ai-agent-dispatch 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.
What it actually says
PWN::AI::Agent::Dispatch
Tool-call dispatch: takes a single tool_call object (OpenAI shape), looks up the registered handler, parses args, runs it, and returns a JSON string suitable for a role:'tool' message. TOLERANT DISPATCH (local-model scaffolding) ------------------------------------------- Local models running on Ollama frequently emit almost- right tool calls: run_shell instead of shell, trailing commas, single-quoted JSON, arguments as a bare string. Strict parsing burns an iteration and often spirals. Dispatch now: * repair_name — Levenshtein-matches unknown names to the closest registered tool and records a Mistakes fingerprint (source: :repair) so the KNOWN MISTAKES block eventually teaches the model the right name. * parse_args — falls back to a JSON5-ish clean-up pass (strip trailing commas, swap single→double quotes, wrap a bare scalar as the tool's sole required arg). Frontier engines never hit these paths — repair is a no-op when the name/JSON are already valid.
When to use
Call PWN::AI::Agent::Dispatch from pwn_eval when the task needs this module.
Do not reimplement it in shell.
Methodologies
Generated from pwn/ai/agent/dispatch.rb. Prefer the public class methods below.
Class methods take (opts = {}) and read opts.
How to call
PWN::AI::Agent::Dispatch.help
PWN::AI::Agent::Dispatch.call(opts)
Public methods
callrepair_nametool_calls_from_texteffectauthorshelp
Source
pwn/ai/agent/dispatch.rb
Verification
PWN::AI::Agent::Dispatch.respond_to?(:call) after the
module is loaded. Read the source for parameter names.
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.
- 2d ago First seen · 51 lines · 21 tokens per session scan A 2fb549a6eacc
pwn-ai-agent-dispatch is a skill published in the GitHub repository 0dayInc/pwn (76 stars, last pushed 4d ago), licensed MIT. It adds 21 tokens to every session and 480 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 2026-08-30.
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