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/encod3d-sec/torch/coveragenpx skills add Encod3d-Sec/TORCH --skill coveragegit clone --depth 1 https://github.com/Encod3d-Sec/TORCHWhat 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 | $0.00049 | $0.00498 |
| Opus 5 | $0.00024 | $0.00249 |
| Sonnet 5 | $0.00010 | $0.00100 |
| Haiku 4.5 | $0.00005 | $0.00050 |
Grade A, and why
coverage 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 2d 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
Coverage
Systematic thoroughness: which phase items and applicable vuln classes have NOT been done.
Coverage now lives in the plan board (targets/<eng>/Approach.md), not a separate file.
Read the board
cat targets/<active>/Approach.md
python3 scripts/next_move.py # ranks [gap] test moves from the 4a table + findings + Deadends
- Phase items still
[ ](todo) or[~](doing) are the open work, in kill-chain order. - The
### 4atable is the per-asset coverage matrix: one row per (asset, vuln class); a row counts as tested when itsstatuscell is[x]/done. Any applicable class with no done row on an in-scope asset is a gap.next_move.pysurfaces these as[gap]moves.
Then (model)
- For each asset, the untested applicable classes ARE the to-do. Prioritise by impact + the
[gap]/[now]moves fromnext_move.py(fingerprint-targeted). - Pull payloads from
wiki/payloads/<class>for each untested class (orSkill(arsenal)). - After testing a class on an asset, add a
### 4arow toApproach.mdwith the class, the tool/payload,status[x], and thepoc/image (GATE 2). Otherwise the gap recurs. - A phase is done only when every applicable item is
[x]or[-](n/a) or[!](deadend).
Discipline
- Respect scope: out-of-scope assets are excluded by
next_move.py. - "Done" means tested, not necessarily clean - record findings separately as FINDs.
- Don't mark a row
[x]without actually testing it and capturing apoc/image; this checklist only helps if honest.
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.
- 2d ago First seen · 34 lines · 49 tokens per session scan A ca3ecd413764
coverage is a skill published in the GitHub repository Encod3d-Sec/TORCH (282 stars, last pushed 4d ago), licensed MIT. It adds 49 tokens to every session and 498 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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