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/sammcj/agentic-coding/skill-creator-primernpx skills add sammcj/agentic-coding --skill skill-creator-primergit clone --depth 1 https://github.com/sammcj/agentic-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/sammcj/agentic-coding/skill-creator-primer)<a href="https://agentmods.dev/skills/sammcj/agentic-coding/skill-creator-primer"><img src="https://agentmods.dev/badge/skills/sammcj/agentic-coding/skill-creator-primer.svg" alt="Measured on agentmods" 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 | $0.00074 | $0.08246 |
| Opus 5 | $0.00037 | $0.04123 |
| Sonnet 5 | $0.00015 | $0.01649 |
| Haiku 4.5 | $0.00007 | $0.00825 |
Grade A, and why
skill-creator-primer 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 today.
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 — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Creator Primer
Note: If the skill-creator skill is missing: stop and ask the user to install skill-creator@claude-plugins-official (registering the marketplace first via /plugin marketplace add anthropics/claude-plugins-official if needed), or to clone https://github.com/anthropics/skills and link its skills directory into their local skills directory.
Route to an entry point by the task in front of you (which-one routing per "Routing when branches multiply", not a content index):
- Creating a new skill -> start at "Capture Intent from Conversation" and work forward.
- Editing an existing skill -> the sections covering what you're changing, then the "Self-Review Protocol".
- Reviewing a skill, or a diff to one -> "Reviewing a Skill".
- Tuning a description or its triggering -> "Writing Effective Descriptions".
- Producing a report, page or visual of a skill's findings ->
references/html-report.md.
Predictable Process, Not Identical Output
A skill wrangles determinism out of a stochastic system where applicable. What it makes predictable is the process - the agent taking the same steps each run - not the output. A brainstorming skill should predictably diverge: its tokens vary, its behaviour doesn't. This is the lens for the rest of this primer: triggering, structure, steering, and pruning are all levers on process consistency; cost and maintainability follow. Judge any change by whether it makes the agent behave more consistently, given what that particular skill is for.
Track Each Step as a Task
Before you create, update, or review a skill, create a task (todo) for each step of the work - the primer sections you'll apply, plus a self-review pass - each phrased with its completion criterion, then work them to completion.
- This is the primer's own defence against premature completion: with the finish line in view, the agent tends to make the visible edit and skip the review. Tracked tasks keep the whole process in front of you.
- Scale the ceremony to the change: substantial skill work warrants a task per step; a trivial edit still earns its description update, a trigger-conflict check, and a self-review pass, tracked or not.
What ships with it
15 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.
- assets/skill-template.md 963 B
- CHANGELOG.md 25 KB
- CLAUDE.md 2.5 KB
- evals/trigger.json 3.0 KB
- references/html-report.md 2.0 KB
- references/steering.md 2.7 KB
- references/trigger-evals.md 2.0 KB
- ruff.toml 982 B
- scripts/eval_triggering.py 8.2 KB runs code
- scripts/hook_report_skill_tokens.py 2.3 KB runs code
- scripts/list_descriptions.py 1.9 KB runs code
- scripts/render_report.py 51 KB runs code
- scripts/validate_skill.py 54 KB runs code
- tests/test_render_report.py 35 KB runs code
- tests/test_validate_skill.py 46 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.
- today Changed · +6 lines a6deeb6b83ec
- 4d ago First seen · 387 lines · 74 tokens per session scan A b176baae0c35
skill-creator-primer is a skill published in the GitHub repository sammcj/agentic-coding (159 stars, last pushed today), licensed Apache-2.0. It adds 74 tokens to every session and 8,246 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 2026-08-30.
Other skills, from other repositories
Canvas Skill
Present interactive HTML, visualizations, dashboards, and generated interfaces on connected Cybara nodes.
api-debug
Debug REST, GraphQL, webhook, and OAuth/API-key integrations with layered request, auth, schema, and response checks.
code-wiki
Generate architecture wiki docs, module maps, and Mermaid diagrams for a codebase using Cybara file/search/write tools.
comfyui
Drive ComfyUI (local Stable Diffusion / image-gen pipeline) via its HTTP API to generate and edit images. Use when the user has ComfyUI running and wants image generation/upscale/img2img workflows.
mactop
Retrieve real-time hardware metrics from Apple Silicon Macs using mactop. Provides CPU, RAM, GPU, power, thermal, network, disk I/O, and Thunderbolt bus information. Use when the user wants system stats, hardware monitoring, or performance metrics on Apple Silicon Macs.
huggingface-jobs
Plan, launch, inspect, monitor, and cancel authenticated Hugging Face Jobs for CPU, GPU, TPU, batch, inference, and data workloads.