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/anthonyposchen/agent-skills/encode-lessonsnpx skills add AnthonyPoschen/agent-skills --skill encode-lessonsgit clone --depth 1 https://github.com/AnthonyPoschen/agent-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/anthonyposchen/agent-skills/encode-lessons)<a href="https://agentmods.dev/skills/anthonyposchen/agent-skills/encode-lessons"><img src="https://agentmods.dev/badge/skills/anthonyposchen/agent-skills/encode-lessons.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 | $0.00046 | $0.00597 |
| Opus 5 | $0.00023 | $0.00298 |
| Sonnet 5 | $0.00009 | $0.00119 |
| Haiku 4.5 | $0.00005 | $0.00060 |
Grade A, and why
encode-lessons 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 3d 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Encode Lessons in Structure
A repeated correction is evidence that attention is the wrong control. When a small durable mechanism can prevent the same mistake, prefer it to another reminder.
Decide Whether The Lesson Is Durable
Treat a human correction, preventable failure, repeated workaround, or missed instruction as a learning signal. Classify it before changing the system:
- One-off: fix the immediate problem. Do not create a permanent rule for a minor isolated event.
- Recurring or costly: find the smallest guard that prevents the pattern.
- Requires judgment: keep a clear instruction. Make the failure mode and the decision it requires obvious.
Do not confuse a vague preference with a proven pattern. A single high-risk or high-cost mistake can still justify a durable guard.
Choose The Strongest Suitable Guard
Prefer a mechanism that prevents the wrong path over text that asks someone not to take it.
- Make an invalid state impossible through data shape, types, or ownership.
- Add a static check, lint rule, CI validation, or banned API.
- Provide a canonical helper, template, generated structure, or safe default.
- Validate at runtime when input is external or the condition is dynamic.
- Use a concise rule and a concrete failure example when the decision cannot be enforced without judgment.
Choose the smallest mechanism that is reliable for the actual risk. Do not build a framework, tool, or policy layer for a minor one-off preference.
Close The Loop
When a durable guard is justified:
- Put it in the layer that owns the failure.
- Prove the guard catches the bad path or makes it impossible.
- Remove or shorten any redundant reminder, while keeping useful rationale, exceptions, and user-facing documentation.
- Leave the surrounding code, tooling, or skill easier to follow than before.
When the guard reaches beyond the current task's natural scope, report the specific pattern and proposed guard. Do not start an unrelated framework or repository-wide migration without approval.
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.
- 3d ago First seen · 71 lines · 46 tokens per session scan A 8341b9eb928e
encode-lessons is a skill published in the GitHub repository AnthonyPoschen/agent-skills (2 stars, last pushed 4d ago), licensed MIT. It adds 46 tokens to every session and 597 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
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brainstorming
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chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
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