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 Eliyce/paqad-ai --skill surface-extractiongit clone --depth 1 https://github.com/Eliyce/paqad-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/skills/eliyce/paqad-ai/surface-extraction)<a href="https://agentmods.dev/skills/eliyce/paqad-ai/surface-extraction"><img src="https://agentmods.dev/badge/skills/eliyce/paqad-ai/surface-extraction/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/eliyce/paqad-ai/surface-extraction"><img src="https://agentmods.dev/badge/skills/eliyce/paqad-ai/surface-extraction.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00036 | $0.00590 |
| Opus 5 | $0.00018 | $0.00295 |
| Sonnet 5 | $0.00007 | $0.00118 |
| Haiku 4.5 | $0.00004 | $0.00059 |
Grade A, and why
surface-extraction 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 8d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What It Does
Produces the raw surface inventory the rest of the workflow builds on: every page, screen,
endpoint, or command the extractor can prove from the code, each with resolving file:line
evidence, deduped, and folded into a stable fingerprint. Mostly deterministic — the engine
does the scanning; this skill orchestrates it and confirms the output is clean.
Use This When
Use this after readiness passes and before modeling. It is the bridge from "the app is mappable" to "here is what the app exposes, with proof".
Inputs
- The readiness verdict and detected extractor from
site-map-readiness. - The project source the extractor scans (route files, command programs, endpoint handlers).
- Read
references/extraction-evidence.mdbefore accepting or excluding a surface.
Procedure
Extraction is deterministic — the engine owns it. Do not re-scan the code by hand.
- Run
paqad-ai sitemap run; it runs the extractors, dedupes, unions evidence, and computes the extraction fingerprint. - Read the
extraction.jsonin the run bundle: each extracted surface carries its kind, afile:line, and a derivation (static | convention). - Confirm every surface's evidence resolves and no surface is a duplicate under a different label; flag any the engine could not ground rather than passing it through.
Output Contract
- A JSON object
{ surfaces: [{ id, kind, evidence, derivation }], fingerprint, blocked_checks: [...] }. - Every
surfaces[]entry carries at least one resolvingfile:lineevidence pointer. fingerprintis present and stable across identical inputs.
Escalate / Stop Conditions
- Stop and record a
blocked_checksentry when the extractor cannot read the app shape; never invent a surface to fill the gap. - Warn when the extraction is empty on a non-trivial app — that is a coverage gap, not a clean map.
- Do not promote a
convention-derived surface tostaticconfidence without resolving evidence.
Resources
references/extraction-evidence.mdagents/openai.yaml
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 66 lines · 36 tokens per session scan A 7f7c191e64cd
surface-extraction is a skill published in the GitHub repository Eliyce/paqad-ai (8 stars, last pushed today), licensed MIT. It adds 36 tokens to every session and 590 once invoked, about $0.0002 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 2026-09-03.
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