RedAmon is an AI framework that connects reconnaissance, exploitation, and post-exploitation activities into an automated security-testing pipeline, while organizing findings and proposing code fixes. It is for authorized penetration testing, security research, and education. The catalogue entries provide skills and instructions for working with the framework.
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 skills add samugit83/redamon --skill builtin-agent-skillgit clone --depth 1 https://github.com/samugit83/redamonWrote 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.
[](https://agentmods.dev/skills/samugit83/redamon/builtin-agent-skill)<a href="https://agentmods.dev/skills/samugit83/redamon/builtin-agent-skill"><img src="https://agentmods.dev/badge/skills/samugit83/redamon/builtin-agent-skill/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.
<a href="https://agentmods.dev/skills/samugit83/redamon/builtin-agent-skill"><img src="https://agentmods.dev/badge/skills/samugit83/redamon/builtin-agent-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium MCP Rug Pull · line 77 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00158 | $0.01770 |
| Opus 5.5 | $0.00063 | $0.00708 |
| Sonnet 5.5 | $0.00032 | $0.00354 |
| Haiku 4.5 | $0.00016 | $0.00177 |
Grade A, and why
builtin-agent-skill 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use
- Adding a first-class attack technique the agent classifies and follows a workflow for.
For a user-uploadable workflow, use add-community-skill
instead. For a tool the agent calls, use agentic-tool-integration.
For per-skill tunable defaults, use project-settings-cascade.
Critical Rules
- ALWAYS add the skill id to
KNOWN_ATTACK_PATHSin agentic/state.py. It is the Pydantic validator's allowlist; miss it and the classifier's output is rejected at runtime even though every other layer is wired. - NEVER skip layers 8 and 9 (the chat-drawer layers). They live in webapp and
produce no error when forgotten: the skill classifies and badges, but the
drawer tooltip and one-click example prompts are silently missing. Layer 8 =
BUILT_IN_SKILLSin attack-skills/available/route.ts; Layer 9 = aSESubGroupin suggestionData.ts. - NEVER let classification keywords overlap an existing skill. Every enabled built-in competes for the same user message; overlapping keywords (e.g. saying "SQL" in an SSRF section) make both skills mis-route. Disambiguate in the "Key distinction" line of the section in classification.py.
- ALWAYS gate the workflow on the required tool in the skill's branch of
_build_builtin_skill_workflow_parts()(agentic/prompts/init.py:198), which the think node (_inject_builtin_skill_workflow()), fireteam members and LATS all reach throughbuild_builtin_skill_workflow():and "<tool>" in allowed_tools. Without the guard the agent gets instructions for a toolTOOL_PHASE_MAPblocks in that phase. - ALWAYS register the skill's static workflow parts in
agentic/prompts/skill_parts.py and append
each one through
_seam.resolve_skill_part(skill_id, part_id, CONSTANT, kwargs)(kwargsonly where the site calls.format; a block built from operator data stays a plain append and is not registered). That seam is where an approved plugin override of the text applies; an unregistered skill silently never gets one. The unit gate enforces it:test_skill_parts_registry.pyfails for a skill inKNOWN_ATTACK_PATHSwithout parts, andtest_skill_parts_seam.py:: test_registry_matches_call_sitesfails for a block appended past the seam once the skill is inSKILLS(+REQUIRED_TOOL) ofagentic/tests/tools/record_skill_golden.py. Then re-record the golden fixture with the command in that file's docstring. - NEVER assume existing projects inherit the new skill.
builtInis a strict has-key check; existing projects need a jsonb update toattackSkillConfig. - ALWAYS add a registry entry for a per-skill tunable that became a
Projectcolumn in recon_settings/registry.yaml. A test walkingPrisma.ProjectScalarFieldEnumfails until every column has one. Draft it withpython3 tooling/scripts/extract_recon_registry.py, edit it, thenpython3 recon_settings/build.py. An attack-skill tunable is ordinarymcp: settabletuning now, bounded by its registry entry and gated at scan start byroeForbiddenCategories,roeForbiddenToolsandroeAllowDosrather than by being unreachable. Give ittraffic: activeso it joins the queued-job fingerprint, androe_capped: trueif its unit isrps. - Pick the snake_case id once and use that exact literal in all 10 layers.
grep -rn "<skill_id>" webapp/src agenticmust show it everywhere before you rebuild (an existing id likecve_exploitspans ~38 files).
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.
- 6d ago Changed · +16 lines · +8 tokens per session bdc98973af12
- 1mo ago Changed c46ac9438515
- 1mo ago Changed · +11 lines 6d28ed24fa88
- 1mo ago First seen · 86 lines · 150 tokens per session scan A 5ff098f3da93
builtin-agent-skill is a skill published in the GitHub repository samugit83/redamon (3,170 stars, last pushed yesterday), licensed MIT. It adds 158 tokens to every session and 1,770 once invoked, about $0.0006 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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