pstack is a collection of plain Agent Skills and engineering principles that guide AI-assisted software development across coding agents such as Claude Code, Codex, Pi, Cursor, and OpenCode. It is for developers who want reusable practices for tasks including testing, technical writing, debugging, review, and coordinating agent work. The catalogue entries are the project's skills, agents, and plugin.
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 backnotprop/pstack --skill principle-boundary-disciplinegit clone --depth 1 https://github.com/backnotprop/pstackWrote 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/backnotprop/pstack/principle-boundary-discipline)<a href="https://agentmods.dev/skills/backnotprop/pstack/principle-boundary-discipline"><img src="https://agentmods.dev/badge/skills/backnotprop/pstack/principle-boundary-discipline/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/backnotprop/pstack/principle-boundary-discipline"><img src="https://agentmods.dev/badge/skills/backnotprop/pstack/principle-boundary-discipline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk 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 | $0.00046 | $0.00379 |
| Opus 5.5 | $0.00018 | $0.00152 |
| Sonnet 5.5 | $0.00009 | $0.00076 |
| Haiku 4.5 | $0.00005 | $0.00038 |
Grade A, and why
principle-boundary-discipline 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
98% identical to principle-boundary-discipline — 2 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
Boundary Discipline
Place validation, type narrowing, and error handling at system boundaries. Trust internal code unconditionally. Business logic lives in pure functions. The shell is thin and mechanical.
Why: Scattered validation is noisy, redundant, and gives a false sense of safety. Keep logic out of framework wiring so it can be tested without the framework.
The pattern:
- At boundaries (CLI args, config files, external APIs, network protocols): validate, return errors, handle defensively.
- Inside the system: typed data, error propagation, no re-validation. Trust the types.
- Across the boundary. Expose domain concepts, not the boundary's private representation. Keep general-purpose mechanism inside and special-purpose policy at the edge.
Applications:
Validation and error handling:
- Validate config at parse time (the boundary), not inside business logic
- Parse raw data into domain types at the boundary
- Do not re-export transport, storage, framework, or wire types through the public surface
- No redundant nil checks deep in call chains if the boundary already validated
Code organization:
- Business logic in pure functions with no framework dependencies
- Parse functions: pure transforms from raw bytes to typed state
- Prompt construction: structured state in, string out
- Scoring and assessment: pure transforms from state to results
The tests:
- "Is this data crossing a system boundary right now?" If not, validation is redundant.
- "Can this be a pure function that the shell just calls?" If yes, extract it.
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 Changed 62ac2862c2ca
- 2mo ago First seen · 35 lines · 46 tokens per session scan A ab3c690ce361
principle-boundary-discipline is a skill published in the GitHub repository backnotprop/pstack (3,158 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 379 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to principle-boundary-discipline, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
analyze-kernel-bottleneck
Systematically identify whether a GPU kernel is compute-bound, memory-bound, or latency-bound using roofline analysis, occupancy calculations, compute/load ratio per tile, and SASS instruction inspection. Produces a decision matrix for optimization strategy selection (cp.async, warp interleaving, tiling…
conduct-empirical-wire-capture
Capture outbound HTTP and telemetry from a CLI harness at runtime. Covers capture-channel selection (transcript file vs verbose-fetch stderr vs outbound proxy vs on-disk state), hook-driven per-event capture vs long-running session capture, JSONL output format for diff-friendly artifacts, and the observability table…
correlate-observability-signals
Unify metrics, logs, and traces for cohesive debugging. Implement exemplars for log-to-trace linking, build unified dashboards using RED/USE methods, and enable rapid root cause analysis across observability signals. Use when investigating complex incidents spanning multiple systems, reducing mean time to resolution…
decode-minified-js-gates
Classify gate call variants in a minified JavaScript bundle. Covers context-window extraction around a flag occurrence, identification of 4–6 reader variants (sync boolean, sync config-object, bootstrap-aware TTL, truthy-only, async bootstrap, async bridge), default-value extraction (boolean / null / numeric /…
chrysopoeia
Extract maximum value from existing code — performance optimization, API surface refinement, and dead weight elimination. The art of turning base code into gold through systematic identification and amplification of value-bearing patterns. Use when optimizing a working but sluggish codebase, refining an API surface…
clean-codebase
Remove dead code, unused imports, fix lint warnings, and normalize formatting across a codebase without changing business logic or architecture. Use when lint warnings have piled up during rapid development, unused imports and variables clutter files, dead code paths were never removed, formatting is inconsistent, or…