pwn-ai-agent-reflect

pwn-ai-agent-reflect is a skill for Claude Code from 0dayInc/pwn. It costs 21 tokens per session (642 once invoked), scanned A, original, MIT.

A controlled way for a PWN::AI agent to ask an AI model to examine local artifacts such as code, transcripts, findings, or decisions.

In plain words
What is it for?
Use it to request AI opinions on locally produced work or to turn session transcripts into lasting lessons, optionally using a different AI provider for review.
Why use it?
It provides one path for reviewing sensitive local data and requires reflection to be enabled before that data can be sent to a remote model.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Use it to request AI opinions on locally produced work or to turn session transcripts into lasting lessons, optionally using a different AI provider for review.

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Install with agentmods
npx agentmods add skills/0dayinc/pwn/reflect
View source ↗ 0dayInc/pwn
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.

Any agent
npx skills add 0dayInc/pwn --skill reflect
Clone the repo
git clone --depth 1 https://github.com/0dayInc/pwn

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for pwn-ai-agent-reflect

README.md
[![agentmods](https://agentmods.dev/badge/skills/0dayinc/pwn/reflect/github.svg)](https://agentmods.dev/skills/0dayinc/pwn/reflect)
Your own site
<a href="https://agentmods.dev/skills/0dayinc/pwn/reflect"><img src="https://agentmods.dev/badge/skills/0dayinc/pwn/reflect/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for pwn-ai-agent-reflect

Your own site · 80×15
<a href="https://agentmods.dev/skills/0dayinc/pwn/reflect"><img src="https://agentmods.dev/badge/skills/0dayinc/pwn/reflect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 21 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 642 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high YARA Match · line 15
    YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).
    Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
How audits are shown
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 5.1 $0.00021 $0.00642
Opus 5.5 $0.00008 $0.00257
Sonnet 5.5 $0.00004 $0.00128
Haiku 4.5 $0.00002 $0.00064

Measured 2mo ago against content hash a0742e4b8ce0, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

pwn-ai-agent-reflect 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 2mo 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.

etc/default_skills/pwn/ai/agent/reflect/SKILL.md · 48 lines

How it starts

The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PWN::AI::Agent::Reflect

PWN::AI::Agent::Reflect is the inward-facing counterpart to PWN::AI::Agent::Extrospection. Where Extrospection looks OUTWARD at the world the agent operates in (host state, toolchain, network, threat-intel), Reflect looks INWARD - it lets pwn hand a request to the active AI engine and reflect on its own artifacts, transcripts, findings, code, or decisions. This module is gated by PWN::Env[:ai][:module_reflection] so that potentially-sensitive local data is never shipped to a remote LLM unless the operator has explicitly opted in via pwn-vault / config. It is the single choke-point every PWN::AI::Agent::* domain agent (Assembly, BurpSuite, GQRX, HackerOne, SAST, VulnGen, ...) routes through when it wants an LLM opinion on locally-produced data, and it is also what PWN::AI::Agent::Learning.reflect uses to distill session transcripts into durable PWN::Memory lessons. TEACHER-STUDENT REFLECTION -------------------------- When PWN::Env[:ai][:reflect_engine] (or opts[:engine]) names a different provider than :active, Reflect.on temporarily flips :active for the duration of the introspection call. This lets a local Ollama model EXECUTE the task while a frontier model WRITES the durable lessons about it — the local model then reads back distilled reasoning it could never have produced itself. IMPLEMENTATION NOTE ------------------- Reflect.on MUST call the engine's text .chat API directly — never Loop.run. Nesting Loop.run re-enters TaskSummarizer/PromptBuilder/ auto_introspect and produces SystemStackError at the Pry after_read boundary whenever module_reflection is enabled. A thread-local depth counter still gates re-entrant Reflect.on (e.g. chat_for_plan inside an outer agent turn that also judges/reflects).

When to use

Call PWN::AI::Agent::Reflect from pwn_eval when the task needs this module. Do not reimplement it in shell.

Methodologies

Generated from pwn/ai/agent/reflect.rb. Prefer the public class methods below. Class methods take (opts = {}) and read opts.

Read the full file on GitHub · 48 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. 2mo ago First seen · 48 lines · 21 tokens per session scan A a0742e4b8ce0

Subscribe to this mod's changes

pwn-ai-agent-reflect is a skill published in the GitHub repository 0dayInc/pwn (86 stars, last pushed 2d ago), licensed MIT. It adds 21 tokens to every session and 642 once invoked, about $0.0001 per session on Opus 5.5. 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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