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 ChrisLamDev/hermes-core-skills --skill skill-slimming-strategygit clone --depth 1 https://github.com/ChrisLamDev/hermes-core-skillsWrote 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/chrislamdev/hermes-core-skills/skill-slimming-strategy)<a href="https://agentmods.dev/skills/chrislamdev/hermes-core-skills/skill-slimming-strategy"><img src="https://agentmods.dev/badge/skills/chrislamdev/hermes-core-skills/skill-slimming-strategy/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/chrislamdev/hermes-core-skills/skill-slimming-strategy"><img src="https://agentmods.dev/badge/skills/chrislamdev/hermes-core-skills/skill-slimming-strategy.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 | $0.00034 | $0.00895 |
| Opus 5 | $0.00017 | $0.00447 |
| Sonnet 5 | $0.00007 | $0.00179 |
| Haiku 4.5 | $0.00003 | $0.00089 |
Grade A, and why
skill-slimming-strategy 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 10d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Slimming Strategy
When to Use
When a SKILL.md file exceeds 2000 words and needs to be made more token-efficient per Superpowers standards.
Classification First
Before slimming, classify the skill into one of these types. Each gets a different treatment:
Type A: Auto-generated API Reference Doc
Signs: Language like "Created On: Jun 06, 2025 | Last Updated On:..." or "This skill auto-generated from official documentation" Action: Replace entirely. Keep only the frontmatter + a 200-word summary with links to official docs. The original content is stale anyway — official docs are more current.
Example:
# Before: 22,733 words of copied PyTorch API docs
# After: 219 words — frontmatter + 3 bullet core concepts + 3 common tasks + links
Type B: Hand-Authored Pipeline / Flow Doc
Signs: Has step-by-step phases, checklists, decision trees. Written by a human for a specific project.
Action: Extract details to reference files. Keep the decision tree / flow diagram + one-sentence-per-phase in SKILL.md. Move detailed checklists, commands, and troubleshooting to references/ files.
Example:
# Before: 14,058 words — full paper writing pipeline with all 7 phases detailed
# After: 429 words — flow diagram + phase summary table + links to reference files
Type C: External Package / Vendored Skill
Signs: Has homepage: or repository: pointing to an external GitHub project. Contains vendored Python scripts.
Action: Don't touch. These are maintained by external authors. The scripts are the real content, not the SKILL.md prose.
Example: last30days — 19,231 words of scripts + instructions from mvanhorn.
Type D: Project-Specific Debug Flow
Signs: Contains real-world debugging steps learned through painful experience. Lots of "quick fix" ordering.
Action: Abstract to decision tree. Keep the symptom diagnosis (Case A vs Case B) + quick-fix order in SKILL.md. Move deep-dive explanations and repair procedures to references/.
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.
- 10d ago First seen · 86 lines · 34 tokens per session scan A ab054f707509
skill-slimming-strategy is a skill published in the GitHub repository ChrisLamDev/hermes-core-skills (9 stars, last pushed 2mo ago), licensed MIT. It adds 34 tokens to every session and 895 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 2026-08-31.
Other skills, from other repositories
unicli-repair
Evidence-driven repair workflow for a broken Uni-CLI adapter. Trigger on a failed unicli envelope, a quarantined adapter, or an explicit adapter-repair request. Classifies non-source failures, edits only the reported adapter path, and uses the original command as a bounded oracle.
autonomous-run
Prepare, start, inspect, resume, or stop a finite local overnight coding run after a human has accepted a Wayfinder terminal spec; coordinates a declared Claude/Codex maker and independent checker without pushing, merging, or writing to external systems.
authentication-patterns
OAuth 2.0, JWT, SSO, MFA, NextAuth/Clerk/Supabase Auth implementation patterns.
case-interview-practice
Interactive consulting case interview practice with structured frameworks, feedback mechanisms, and progressive difficulty. Use when preparing for management consulting interviews, case competitions, or business problem-solving exercises.
finance
Financial analysis expertise for financial modeling (DCF, LBO, M&A), valuation, financial statement analysis, capital allocation, treasury management, and corporate finance decisions. Use when building financial models, analyzing statements, or making investment decisions.
i18n-localization
Internationalization and localization for global applications. Use when adding multi-language support, handling regional formats, or preparing apps for global markets.