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-optimizernpx skills add mathieu0905/skilllens --skill skillscope-optimizergit 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 | $0.00068 | $0.02206 |
| Opus 5 | $0.00034 | $0.01103 |
| Sonnet 5 | $0.00014 | $0.00441 |
| Haiku 4.5 | $0.00007 | $0.00221 |
Grade A, and why
skillscope-optimizer 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.
How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SkillScope Optimizer
Purpose
Optimize one selected SKILL.md from SkillScope's program-analysis artifacts. Use the artifact chain below as the source of truth:
skill source -> constraints + skill graph
trace events -> trace facts
graph x facts -> findings
findings -> minimal skill patch
Required Inputs
Read every path named in the launch prompt. The optimizer must have:
- Original selected skill markdown.
constraints.json.skill-graph.json.trace-facts.json.findings.json.- Task/context and result/verifier artifacts when provided.
- Native verifier artifacts when provided.
If any IR artifact is missing, stop and report which artifact is missing. Missing IR routes the workflow back to skillscope-analyzer.
Optimization Direction
Optimize only when there is a real optimization target. When the native verifier already passed, non-compliance is low, and remaining failures are only process/reporting observability gaps, use the current skill as the rerun candidate, copy it to optimized-skill.md, record the pass-through reason in the report, and set optimization-packet.json edits to an empty array.
Use this priority order:
- Native verifier failed assertions that map to final-output or artifact contracts.
- SkillScope
violatedfindings onfinal_outputorartifacttargets. - Repeated
missedfindings on reachable final-output or artifact constraints. - High non-compliance or repeated failures across traces.
- Process/tool/reporting failures only when they explain a native or artifact failure, repeat across traces, or the user explicitly wants observability hardening.
A single successful trace with a low-severity process-only missed finding routes to analyzer confidence, branch reachability, or UI review unless the user explicitly requests observability hardening.
Treat satisfied constraints as preservation anchors. A covered constraint, a native-verifier-backed output invariant, or a nearby branch that worked should stay textually stable except when the replacement is a clearer equivalent required by a failed neighboring constraint. Every edit that touches a covered span should state the preserved invariant in optimization-packet.json.
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.
- 2d ago First seen · 158 lines · 68 tokens per session scan A 4649d8dd43c2
skillscope-optimizer is a skill published in the GitHub repository mathieu0905/skilllens (2 stars, last pushed 2mo ago), licensed MIT. It adds 68 tokens to every session and 2,206 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 2026-08-31.
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