Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/EladAriel/pseudo-code-prompting-pluginnpx agentmods add skills/eladariel/pseudo-code-prompting-plugin/project-explanationWrote 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/eladariel/pseudo-code-prompting-plugin/project-explanation)<a href="https://agentmods.dev/skills/eladariel/pseudo-code-prompting-plugin/project-explanation"><img src="https://agentmods.dev/badge/skills/eladariel/pseudo-code-prompting-plugin/project-explanation.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 5.1 | $0.00000 | $0.02539 |
| Opus 5 | $0.00000 | $0.01269 |
| Sonnet 5 | $0.00000 | $0.00508 |
| Haiku 4.5 | $0.00000 | $0.00254 |
Grade A, and why
project-explanation 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 6d 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 — 413 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project Explanation Skill
Generates engaging technical explanations of projects that preserve architectural wisdom and lessons learned.
What This Skill Does
Provides patterns, templates, and best practices for creating comprehensive technical documentation that:
- Explains complex systems clearly with analogies
- Justifies technology decisions
- Captures lessons learned and pitfalls
- Reads like an engaging essay, not boring documentation
- Preserves architectural knowledge for future engineers
When to Use
- Creating technical documentation for team onboarding
- Explaining project architecture to stakeholders
- Documenting technology decisions and trade-offs
- Preserving lessons learned from building a system
- Analyzing unfamiliar codebases
Core Principles
1. Clarity Through Analogy
Complex systems become understandable through comparison to familiar concepts.
Pattern:
"Think of [system] like [familiar thing]. [Familiar thing] does X, Y, Z.
Similarly, [system] does A, B, C to solve [problem]."
Examples:
- "Think of our cache like a store's back room. Items in the back room are grabbed quickly, but eventually expire and need restocking."
- "Our rate limiter is like a traffic light. It lets requests through quickly most of the time, but when traffic gets heavy, it queues them up."
- "Message queues are like mailboxes. You drop a letter in, the mailman delivers it eventually. You don't wait around for the delivery."
2. Lessons Through Anecdotes
People remember stories better than facts. Share how lessons were learned.
Pattern:
Lesson: [What was learned]
Story: "We discovered this when [situation]. [What happened].
[How we fixed it]."
Takeaway: [How to apply in future]
Example:
Lesson: Database Migrations Need Careful Planning
Story: "We had a production database with 10 million user records. We wanted
to add a new column with a NOT NULL constraint. We didn't think to make it
nullable first, then backfill. We locked the entire table for 2 hours.
Customers couldn't log in. 😱"
Takeaway: For large tables, always backfill with nullable columns first,
then add constraints. Test on production volume in staging first.
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.
- 6d ago First seen · 413 lines · 0 tokens per session scan A 4cfcb33a6683
project-explanation is a skill published in the GitHub repository EladAriel/pseudo-code-prompting-plugin (2 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,539 tokens. 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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