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.
git clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote 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/commands/adriannoes/awesome-agentic-ai/surface)<a href="https://agentmods.dev/commands/adriannoes/awesome-agentic-ai/surface"><img src="https://agentmods.dev/badge/commands/adriannoes/awesome-agentic-ai/surface.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00028 | $0.00342 |
| Opus 5 | $0.00014 | $0.00171 |
| Sonnet 5 | $0.00006 | $0.00068 |
| Haiku 4.5 | $0.00003 | $0.00034 |
Grade A, and why
surface 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 3d 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.
This is a copy
100% identical to surface — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
/surface
View the prioritized attack surface for a target.
What This Does
- Reads cached recon output from
recon/<target>/ - Reads hunt memory for patterns and previously tested endpoints
- Invokes the
recon-rankeragent to produce a prioritized ranking - Outputs P1 (start here), P2 (after P1), and Kill List (skip)
Usage
/surface target.com
Prerequisites
Run /recon target.com first. If no recon data exists, you'll be prompted to run recon.
Output
ATTACK SURFACE: target.com
═══════════════════════════════════════
Priority 1 (start here):
1. api.target.com/v2/users/{id} — IDOR candidate
Tech: Express + PostgreSQL | First seen 12 days ago
Suggested: numeric ID swap on GET/PUT/DELETE
2. api.target.com/graphql — introspection enabled, 47 mutations
Suggested: field-level auth check on sensitive mutations
Priority 2 (after P1):
1. cdn.target.com:8443/upload — file upload endpoint
Suggested: extension bypass, magic bytes
Kill List (skip):
- static.target.com — CDN only
- docs.target.com — third-party hosted
Memory:
- Pattern from alpha.com (same tech): auth bypass via method override ($800)
- 3 endpoints tested in previous session, 5 remain
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.
- 3d ago First seen · 52 lines · 0 tokens per session scan A c8218a007bc7
surface is a command published in the GitHub repository adriannoes/awesome-agentic-ai (55 stars, last pushed 8d ago), licensed MIT. It adds 28 tokens to every session and 342 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to surface, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
design-onboarding
Design a first-run experience end to end — activation path, progressive disclosure, and time to first value.
assistant-auto
Orchestrator in automatic mode. Choose the workflow that semantically fits based on the request + the injected repo context, then execute immediately via Skill.
amby.clarify
Resolve the open [NEEDS CLARIFICATION] markers in a feature spec.
aw-upgrade
Upgrade gh-aw extension, recompile and validate all workflows, and open a PR with changes.
code-review
Code review — local uncommitted changes or GitHub PR (pass PR number/URL for PR mode).
review
Conduct a five-axis code review — correctness, readability, architecture, security, performance.