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/painhardcore/pstack/principle-foundational-thinkingnpx skills add painhardcore/pstack --skill principle-foundational-thinkinggit clone --depth 1 https://github.com/painhardcore/pstackWhat 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 | $0.00042 | $0.00331 |
| Opus 5 | $0.00021 | $0.00166 |
| Sonnet 5 | $0.00008 | $0.00066 |
| Haiku 4.5 | $0.00004 | $0.00033 |
Grade A, and why
principle-foundational-thinking 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 2d 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
88% identical to principle-foundational-thinking — 1 line 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
Foundational Thinking
Structural decisions protect option value. Code-level decisions protect simplicity. Over-engineering is often a premature decision that closes doors. The right foundational data structure keeps doors open.
Data structures first. Get the data shape right before writing logic. The right shape makes downstream code obvious. Define core types early, trace every access pattern, and choose structures that match the dominant paths. A data-structure change late is a rewrite. Early, it is often a one-line diff.
At code level, DRY the structure, not every line. Types and data models should converge. Three similar statements still beat a premature abstraction. Prefer explicit over clever. Test behavior and edge cases, not line counts.
Concurrency corollary. Before sharing state between actors, ask "what happens if another actor modifies this concurrently?" If not "nothing", isolate.
Scaffold first. If something helps every later phase, do it first. Ask "does every subsequent phase benefit from this existing?" CI, linting, test infrastructure, and shared types are scaffold. Sequence for option value: setup before features, tests before fixes. Keep commits small and single-purpose.
Each increment should land a coherent abstraction or deepen one that exists. Do not spread a new capability across callers as special-case coordination.
Subtraction comes before scaffolding: remove dead weight first, then lay foundations.
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.
- 2d ago First seen · 21 lines · 42 tokens per session scan A 64aec61f4d1f
principle-foundational-thinking is a skill published in the GitHub repository painhardcore/pstack (1 stars, last pushed 6d ago), licensed MIT. It adds 42 tokens to every session and 331 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to principle-foundational-thinking, differing in 1 line, and is treated as a copy.
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