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 agentmods add skills/transilienceai/communitytools/attack-path-stitchernpx skills add transilienceai/communitytools --skill attack-path-stitchergit clone --depth 1 https://github.com/transilienceai/communitytoolsWrote 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/transilienceai/communitytools/attack-path-stitcher)<a href="https://agentmods.dev/skills/transilienceai/communitytools/attack-path-stitcher"><img src="https://agentmods.dev/badge/skills/transilienceai/communitytools/attack-path-stitcher.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 | $0.00042 | $0.01658 |
| Opus 5 | $0.00021 | $0.00829 |
| Sonnet 5 | $0.00008 | $0.00332 |
| Haiku 4.5 | $0.00004 | $0.00166 |
Grade A, and why
attack-path-stitcher 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 4d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Attack Path Stitcher
The Validation Run task (#3) produces confirmed findings per asset. Real attacker risk lives in chains: a finding on asset A leaks credentials that enable a finding on asset B that pivots into asset C. This skill builds that graph.
Mounted onto cloud-agent task #6.
Trigger
Cron daily (default 03:00 UTC). May also re-run after a Validation Run task batch completes.
Workflow
- Load inputs.
validated/*.json— every confirmed finding across all engagements.artifacts/org-surface.json— the org-wide surface graph (assets, services, network zones, trust relationships).findings/finding-NNN/evidence/raw-source.txt— for credential / token extraction during stitching.
- Build asset nodes. One node per asset in
org-surface.json, attributed with:tier,services,network_zone,trust_relationships. - Build edges — one edge per detected pivot. See
reference/edge-detectors.mdfor the seven detectors:- Credential reuse (creds leaked on A reused as auth on B)
- Shared secret / API key (same secret appears in two assets' evidence)
- Trust-zone transitive access (A in zone X has implicit reach to B in zone X)
- AD path hops (kerberoast / DC sync / RBCD chains)
- Cloud IAM role chains (assume-role from compromised asset)
- SSRF → internal asset reach (A's SSRF reaches B's internal endpoint)
- Supply-chain (A is a dependency of B per
source-code-scanningSBOM)
- Compute reachability closure. For each tier-
crown_jewelnode, BFS backwards through edges to find every external-facing node that can reach it. Mark these as "entry points". - Write graph to
artifacts/attack-paths.jsonplus a human DOT fileartifacts/attack-paths.dot(renderable with Graphviz).
Implementation runs through tools/chain-merger.py which handles the graph construction. The skill provides the rules the tool consults; the tool does the iteration.
Output
{OUTPUT_DIR}/
artifacts/
attack-paths.json # nodes, edges, entry_points, crown_jewel_paths
attack-paths.dot # Graphviz source
attack-paths.md # ranked list of distinct paths (human read)
What ships with it
1 file 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.
- 4d ago First seen · 101 lines · 42 tokens per session scan A a92cc17a97a4
attack-path-stitcher is a skill published in the GitHub repository transilienceai/communitytools (505 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 1,658 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 2026-08-30.
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