Borrowing it
Nothing to install: this file belongs to tellahq/opensession. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tellahq/opensession/main/.agents/skills/pstack-suite/skills/principle-encode-lessons-in-structure/SKILL.mdgit clone --depth 1 https://github.com/tellahq/opensessionWrote 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/tellahq/opensession/principle-encode-lessons-in-structure)<a href="https://agentmods.dev/skills/tellahq/opensession/principle-encode-lessons-in-structure"><img src="https://agentmods.dev/badge/skills/tellahq/opensession/principle-encode-lessons-in-structure/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/tellahq/opensession/principle-encode-lessons-in-structure"><img src="https://agentmods.dev/badge/skills/tellahq/opensession/principle-encode-lessons-in-structure.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00049 | $0.00475 |
| Opus 5.5 | $0.00020 | $0.00190 |
| Sonnet 5.5 | $0.00010 | $0.00095 |
| Haiku 4.5 | $0.00005 | $0.00047 |
Grade A, and why
principle-encode-lessons-in-structure 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.
This is a copy
95% identical to principle-encode-lessons-in-structure — 26 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Encode Lessons in Structure
Encode recurring fixes in mechanisms (tools, code, metadata, automation) instead of textual instructions. Every error, human correction, and unexpected outcome is a learning signal. Capture it, route it, and close the loop.
Why: Textual instructions are easy to miss. They require the reader to notice, remember, and comply. Structural mechanisms (lint rules, metadata flags, runtime checks, automation scripts) enforce the rule without cooperation.
Pattern: When you catch yourself writing the same instruction a second time:
- Ask: can this be a lint rule, a metadata flag, a runtime check, or a script?
- If yes, encode it. Delete the instruction
- If no (genuinely requires judgment), make the instruction more prominent and add an example of the failure mode
Pick the strongest rung. When more than one mechanism would work, choose the strongest the situation allows (an unrepresentable state that cannot compile, then a lint or banned API that fails CI, then a canonical helper, then a runtime check), because agents copy whatever the surrounding code already does and a weaker guard becomes the next template.
Corollary: Don't paper over symptoms. If the fix is structural, ONLY use the structural fix. The instruction IS the symptom.
Feedback loop:
- Capture every correction. When the human intervenes or tests fail, decide if it's a one-off or a pattern.
- Route to the right layer. One-off -> brain note. Recurring fix -> skill or lint rule. Systemic issue -> principle.
- Close the loop. Don't just record. Apply now or create a concrete todo.
Anti-patterns:
- Acknowledging without recording ("I'll keep that in mind" does not persist)
- Recording without routing (a brain note about a lint rule that should exist is wasted unless the lint rule gets implemented)
- Fixing without generalizing (fixing one instance while leaving the recurring pattern intact)
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 · 32 lines · 49 tokens per session scan A 4acdb3f466ef
principle-encode-lessons-in-structure is a skill published in the GitHub repository tellahq/opensession (399 stars, last pushed today), licensed MIT. It adds 49 tokens to every session and 475 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to principle-encode-lessons-in-structure, differing in 26 lines, and is treated as a copy.
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