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 ssmurfgg04-gif/context-m --skill coding-agentgit clone --depth 1 https://github.com/ssmurfgg04-gif/context-mWrote 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/ssmurfgg04-gif/context-m/coding-agent)<a href="https://agentmods.dev/skills/ssmurfgg04-gif/context-m/coding-agent"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/coding-agent/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/ssmurfgg04-gif/context-m/coding-agent"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/coding-agent.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 | $0.00019 | $0.00734 |
| Opus 5.5 | $0.00008 | $0.00294 |
| Sonnet 5.5 | $0.00004 | $0.00147 |
| Haiku 4.5 | $0.00002 | $0.00073 |
Grade A, and why
coding-agent 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use
User explicitly requests code implementation. Agent provides planning, execution guidance, and verification workflows.
Architecture
User preferences stored in ~/code/ when user explicitly requests.
~/code/
- memory.md # User-provided preferences only
Create on first use: mkdir -p ~/code
Quick Reference
| Topic | File |
|---|---|
| Memory setup | memory-template.md |
| Task breakdown | planning.md |
| Execution flow | execution.md |
| Verification | verification.md |
| Multi-task state | state.md |
| User criteria | criteria.md |
Scope
This skill ONLY:
- Provides coding workflow guidance
- Stores preferences user explicitly provides in
~/code/ - Reads included reference files
This skill NEVER:
- Executes code automatically
- Makes network requests
- Accesses files outside
~/code/and the user's project - Modifies its own SKILL.md or auxiliary files
- Takes autonomous action without user awareness
Core Rules
1. Check Memory First
Read ~/code/memory.md for user's stated preferences if it exists.
2. User Controls Execution
- This skill provides GUIDANCE, not autonomous execution
- User decides when to proceed to next step
- Sub-agent delegation requires user's explicit request
3. Plan Before Code
- Break requests into testable steps
- Each step independently verifiable
- See
planning.mdfor patterns
4. Verify Everything
| After | Do |
|---|---|
| Each function | Suggest running tests |
| UI changes | Suggest taking screenshot |
| Before delivery | Suggest full test suite |
5. Store Preferences on Request
| User says | Action |
|---|---|
| "Remember I prefer X" | Add to memory.md |
| "Never do Y again" | Add to memory.md Never section |
Only store what user explicitly asks to save.
Workflow
Request -> Plan -> Execute -> Verify -> Deliver
Common Traps
- Delivering untested code -> always verify first
- Huge PRs -> break into testable chunks
- Ignoring preferences -> check memory.md first
What ships with it
7 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.
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 · 121 lines · 19 tokens per session scan A 1021c4d7db47
coding-agent is a skill published in the GitHub repository ssmurfgg04-gif/context-m (2 stars, last pushed today), licensed Apache-2.0. It adds 19 tokens to every session and 734 once invoked, about $0.0001 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.
Other skills, from other repositories
agent-memory-discipline
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kv-cache-design
A guide to arranging an AI agent's conversation so previously processed text can be reused. A KV cache stores intermediate results for earlier tokens, while a prompt cache reuses a stable beginning across requests.
agent-evaluation
A guide to measuring whether an AI agent works reliably on the tasks that matter. It covers repeatable test environments, program checks, human-like scoring rules, failure analysis, and experiments that separate model problems from system problems.
agent-evolution
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post-training-strategy
A guide to choosing and planning ways to improve an AI model after its initial training. It explains when to add knowledge, teach a response format, use trial-and-error training, or use a smaller model to learn from a larger one.
ai-style
A Chinese-language writing guide for producing or revising reader-facing text such as product announcements, public-account articles, emails, and README files. It focuses on reducing patterns that make writing sound machine-generated.