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 vlinx-io/VelaTerm --skill attack-path-analysisgit clone --depth 1 https://github.com/vlinx-io/VelaTermWrote 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/vlinx-io/velaterm/attack-path-analysis)<a href="https://agentmods.dev/skills/vlinx-io/velaterm/attack-path-analysis"><img src="https://agentmods.dev/badge/skills/vlinx-io/velaterm/attack-path-analysis/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/vlinx-io/velaterm/attack-path-analysis"><img src="https://agentmods.dev/badge/skills/vlinx-io/velaterm/attack-path-analysis.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 | $0.00060 | $0.01608 |
| Opus 5.5 | $0.00024 | $0.00643 |
| Sonnet 5.5 | $0.00012 | $0.00322 |
| Haiku 4.5 | $0.00006 | $0.00161 |
Grade A, and why
attack-path-analysis 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- attack-path-analysis — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Attack Path Analysis
Objective
Turn validated or still-plausible findings into explicit attacker stories, structured attack-path analysis facts, severity calibration, and a final reportability decision grounded in the threat model.
Artifact Resolution
The path references in this skill are the default locations for this phase.
If the user explicitly provides a different path for a required input or output, use the user-provided path instead of the corresponding default path referenced in this skill.
If a required input is still missing, stop and ask the user for it before continuing.
Use the shared scan artifact path conventions in ../../references/scan-artifacts.md.
Standard scans and Deep Scan workers assess attack paths within their ordinary Standard scan workflow; neither invokes this separate phase skill.
Compact Workbench-Backed Diff Mode
When a workbench-backed $security-diff-scan has a scanId, load the per-scan threat model and read the validated candidates with list_codex_security_candidates({ scanId, cursor?, limit? }). Analyze every reportable or deferred candidate, preserve every discovery and validation field and the original candidate order, and submit all decisions together with one record_candidate_attack_paths({ scanId, attackPaths: [{ candidateId, attackPath }] }) call. Submit attackPaths: [] when no candidate enters this phase. The existing tool atomically updates the stored candidates; do not create per-finding reports, receipts, or manual candidate ledgers in this compact diff mode. Keep attack-path facts, counterevidence, severity calibration, and policy adjustment as separate reasoning steps. Other scan and standalone workflows retain their existing artifact behavior.
Workflow
- Load the per-scan threat model path from
../../references/scan-artifacts.mdas the repo-specific threat-model source of truth. Start from this along with the potential findings. Both inputs are required for this workflow.- For repository-wide and scoped-path scans, include validation closure rows marked
reportableorsurvives: yeseven if they were not assigned polished candidate numbers during discovery.
- For repository-wide and scoped-path scans, include validation closure rows marked
- Determine whether the affected code is in scope for the repository threat model and whether it belongs to a product surface or production workflow.
- Build a factual attack path using repository evidence only:
- service mapping
- exposure and entry points
- identity, privilege, and trust boundaries
- secrets handling and sensitive-data flow
- reachability
- existing controls and mitigations
- Before finalizing scope or reportability-driving facts, identify the strongest repository counterevidence against the key scoping fields and explain why it is or is not dispositive.
- Calibrate impact and likelihood from the repository evidence.
- Apply a separate final policy-adjustment pass mechanically using those facts and the calibrated severity.
- Record final policy decision
ignoreexplicitly; in compact diff mode, retain its candidate record for coverage, and otherwise drop it from the surviving finding set. - For a durable diff scan, submit the nested decision for every eligible candidate in the single compact tool call. Otherwise, save that finding's visible attack-path report and append one attack-path receipt per candidate id at the default paths from
../../references/scan-artifacts.md. The receipt must record the candidate id, attack-path reportability decision, attack-path facts or exact proof gap, and attack-path artifact/report reference for that candidate finding.
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.
- 1mo ago First seen · 115 lines · 60 tokens per session scan A 63e820ce598d
attack-path-analysis is a skill published in the GitHub repository vlinx-io/VelaTerm (301 stars, last pushed 2d ago), licensed MIT. It adds 60 tokens to every session and 1,608 once invoked, about $0.0002 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 2026-09-14.
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