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 meltedinhex/analyst-ai-pack --skill building-config-extractorsgit clone --depth 1 https://github.com/meltedinhex/analyst-ai-packWrote 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/meltedinhex/analyst-ai-pack/building-config-extractors)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/building-config-extractors"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/building-config-extractors/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/meltedinhex/analyst-ai-pack/building-config-extractors"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/building-config-extractors.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 | $0.00066 | $0.00714 |
| Opus 5 | $0.00033 | $0.00357 |
| Sonnet 5 | $0.00013 | $0.00143 |
| Haiku 4.5 | $0.00007 | $0.00071 |
Grade A, and why
building-config-extractors 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 10d 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
Building Config Extractors
When to Use
- You have reversed a family's config format and want a reusable, declarative extractor instead of one-off scripts.
- You need to apply a spec (offset, XOR/base64 layers, field map) to many samples and emit structured, defanged config.
Do not use a spec built for one variant blindly on another — re-verify offsets/keys per variant. This skill reads samples statically and executes nothing.
Prerequisites
- A reversed understanding of the config (location, decode layers, fields) expressed as a JSON spec.
Safety & Handling
- Read bytes statically; defang recovered endpoints in output.
Workflow
Step 1: Write the extraction spec
Define a JSON spec: blob offset/length (or a marker to search), ordered decode layers
(xor with key, base64), and fields (name, offset, length, type: str/u16/u32/ipv4).
Step 2: Run the extractor
python scripts/analyst.py extract sample.bin --spec family.json
Locates the blob, applies decode layers, parses fields, and defangs URL/IP fields.
Step 3: Validate output
Confirm fields are plausible (valid hosts, ports, IDs); adjust the spec as needed.
Step 4: Reuse
Store the spec per family and apply across the corpus.
Validation
- The spec round-trips on a known sample to the expected config.
- Decode layers are applied in order and produce readable output.
- URL/IP fields are defanged in the emitted config.
Pitfalls
- Hardcoding a variant-specific offset that shifts in other builds — prefer markers.
- Wrong field endianness (
u16/u32little vs big) producing garbage ports. - Forgetting a decode layer (base64 over XOR) and parsing ciphertext as fields.
References
- See
references/api-reference.mdfor the extractor engine. - ATT&CK T1140 and config-extractor framework concepts (linked in frontmatter).
What ships with it
3 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.
- 10d ago First seen · 90 lines · 66 tokens per session scan A 7d0be17eaef6
building-config-extractors is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 66 tokens to every session and 714 once invoked, about $0.0003 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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