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/mathieu0905/skilllens/skillscope-e2enpx skills add mathieu0905/skilllens --skill skillscope-e2egit clone --depth 1 https://github.com/mathieu0905/skilllensWhat 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 | $0.00072 | $0.02976 |
| Opus 5 | $0.00036 | $0.01488 |
| Sonnet 5 | $0.00014 | $0.00595 |
| Haiku 4.5 | $0.00007 | $0.00298 |
Grade A, and why
skillscope-e2e 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 yesterday.
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 — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SkillScope E2E
Use this skill for one selected (taskId, skillRelPath) instance.
The loop is:
select one task + one skill
-> run original skill on that task
-> collect trajectory and native verifier result
-> run SkillScope graph-guided agent judge on the selected skill
-> gate: pass-through, optimize, or mark not skill-caused
-> generate exactly one optimized SKILL.md when gated for optimization
-> rerun the same task with only that selected skill replaced
-> judge the optimized trajectory and optimized skill
-> compare native correctness and SkillScope non-compliance
Experiment Unit
Define the instance before running anything:
taskId: one SkillsBench task.skillRelPath: oneenvironment/skills/<skill-name>/SKILL.md.trial: default1original trial and1optimized trial unless the user asks otherwise.slug: one semantic experiment name for this instance.
Prefer one-skill SkillsBench tasks for the first clean loop. A one-skill task is one whose environment/skills directory contains exactly one SKILL.md.
If a task contains multiple skills:
- Analyze and optimize only the selected
skillRelPath. - Treat sibling skills as unchanged task context.
- Do not merge sibling skills into the optimization target.
- Do not optimize sibling skills in the same run.
- If the current CLI cannot restrict rerun or propose steps to the selected skill, stop and either choose a one-skill task or add target-skill filtering before continuing.
Versioning Rule
Use git commits for code, workflow, and skill versions. Do not create v1, v2, v8, or similar artifact directories as a substitute for version control.
Use semantic experiment names for artifacts, for example:
.skilllens/experiments/skillsbench-codex-gpt55/azure-bgp-single-original.skilllens/experiments/skillsbench-codex-gpt55/azure-bgp-single-optimized.skilllens/experiments/skillsbench-codex-gpt55/right-shift-contract-smoke
Each final comparison must name the git commit, run plan path, original run root, selected skill path, optimized skill root, optimized run root, and both analysis reports.
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.
- yesterday First seen · 334 lines · 72 tokens per session scan A fbf0174b57db
skillscope-e2e is a skill published in the GitHub repository mathieu0905/skilllens (2 stars, last pushed 2mo ago), licensed MIT. It adds 72 tokens to every session and 2,976 once invoked, about $0.0004 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 2026-08-31.
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