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 jtsang4/efficient-coding --skill general-skill-creatorgit clone --depth 1 https://github.com/jtsang4/efficient-codingWrote 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/jtsang4/efficient-coding/general-skill-creator)<a href="https://agentmods.dev/skills/jtsang4/efficient-coding/general-skill-creator"><img src="https://agentmods.dev/badge/skills/jtsang4/efficient-coding/general-skill-creator/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/jtsang4/efficient-coding/general-skill-creator"><img src="https://agentmods.dev/badge/skills/jtsang4/efficient-coding/general-skill-creator.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 | $0.00068 | $0.03730 |
| Opus 5.5 | $0.00027 | $0.01492 |
| Sonnet 5.5 | $0.00014 | $0.00746 |
| Haiku 4.5 | $0.00007 | $0.00373 |
Grade A, and why
general-skill-creator 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 1mo 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 — 440 lines — stays where its author put it; the contents beside it link to each section on GitHub.
General Skill Creator
A skill for creating portable Agent Skills and improving them through realistic test runs.
Agent Skills are reusable instruction packages for coding agents. A portable skill should rely on the shared skill pattern: a SKILL.md file with frontmatter, concise instructions, and optional bundled resources such as scripts/, references/, assets/, and evals/.
Core Loop
Work through this loop:
- Understand the user intent and the target agent environments.
- Confirm where the skill should be installed or authored.
- Draft or edit the skill.
- Create realistic test prompts.
- Run the prompts with the skill and against a baseline.
- Help the user review qualitative outputs and quantitative checks.
- Improve the skill from the feedback.
- Repeat until the skill is useful, portable, and lean.
Use the current coding agent's native capabilities for execution. If the environment has subagents, use them for independent runs. If the environment has headless execution, use it for repeatable test runs. Headless execution means a non-interactive agent run launched by CLI, SDK, API, or task runner, where the prompt, inputs, workspace, and output path are provided up front.
Communicating With The User
Match the user's level of technical detail. Explain terms such as "eval", "assertion", "headless", and "baseline" when the user seems new to agent workflows.
Keep the user involved at the decisions that shape portability:
- Which agents or agent families should the skill support?
- Where should the skill live: repository, user-level skills folder, organization-managed folder, or a custom path?
- Should the skill be authored once in a canonical directory and copied or linked into agent-specific locations?
- Should test runs prioritize quality, trigger accuracy, speed, or reproducibility?
Capture Intent
Start by extracting answers from the current conversation when available. Ask for missing details only when they change the skill design.
What ships with it
17 files 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.
- agents/analyzer.md 10 KB
- agents/comparator.md 7.1 KB
- agents/grader.md 8.8 KB
- assets/eval_review.html 6.9 KB
- eval-viewer/generate_review.py 16 KB runs code
- eval-viewer/viewer.html 44 KB
- LICENSE.txt 11 KB
- references/schemas.md 4.7 KB
- scripts/__init__.py 0 B runs code
- scripts/aggregate_benchmark.py 14 KB runs code
- scripts/generate_report.py 13 KB runs code
- scripts/improve_description.py 3.2 KB runs code
- scripts/package_skill.py 4.1 KB runs code
- scripts/quick_validate.py 3.9 KB runs code
- scripts/run_eval.py 403 B runs code
- scripts/score_trigger_results.py 5.2 KB runs code
- scripts/utils.py 1.6 KB runs code
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.
- 1mo ago First seen · 440 lines · 68 tokens per session scan A f03f4972bec5
general-skill-creator is a skill published in the GitHub repository jtsang4/efficient-coding (2 stars, last pushed 8d ago), licensed MIT. It adds 68 tokens to every session and 3,730 once invoked, about $0.0003 per session on Opus 5.5. 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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