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/backnotprop/pstack/whynpx skills add backnotprop/pstack --skill whygit clone --depth 1 https://github.com/backnotprop/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 | $0.00089 | $0.04599 |
| Opus 5 | $0.00044 | $0.02299 |
| Sonnet 5 | $0.00018 | $0.00920 |
| Haiku 4.5 | $0.00009 | $0.00460 |
Grade A, and why
why 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.
How it starts
The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Why
Investigate the motivation and intent behind code. Why was it built this way? What edge cases were considered? What product, business, or operational constraints shaped the design? What alternatives were rejected, and why?
Companion to the how skill. how answers what the code does and how it works. why answers what forces led to its shape.
How this skill works
Historical context spreads across seven evidence categories: source control history, issue or ticket tracking, long-form documents, real-time team chat, infrastructure observability, error or exception tracking, and product analytics warehouses. You cannot predict from the question alone which one holds the answer, so the skill enumerates available MCPs at run time, maps each to a category, queries all seven in parallel, then synthesizes with explicit confidence calibration. Null results from searched categories are first-class evidence about how the decision was made; report them alongside positive findings. The default is coverage, not minimalism.
Operating Posture
Operate as a careful, cautious, precise investigator. Think like a detective piecing together a historical case from fragmentary records. When the record is thin, say so.
Concretely:
- Evidence before narrative. Collect the pieces first, then see what story they support. Never pick a story and recruit the evidence that fits it.
- Precision over polish. Prefer the exact quote and citation over a smooth paraphrase. A reader should be able to follow any claim back to its source and verify it in under a minute.
- Consider what you haven't seen. The evidence you find is a sample, not the whole truth. Before concluding, ask what you would expect to see if an alternative explanation were true, and whether you looked for it.
- Name the gaps. If a thread goes cold, a source isn't searchable, or a question has no answer, document the gap. Don't paper it over with an authoritative-sounding guess.
- Hedge on purpose. When evidence is indirect, your language should signal it ("appears to", "likely", "suggests"). Confidence-matching phrasing is a feature of the output, not a stylistic choice the synthesizer may override.
- No shortcut by code-reading. The code tells you what it does, rarely why it exists. Resist inferring intent from code shape.
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/epistemics.md 7.6 KB
- references/investigator-prompt.md 6.8 KB
- references/source-playbook.md 1.3 KB
- references/sources/code-archaeology.md 3.4 KB
- references/sources/databricks.md 6.8 KB
- references/sources/datadog.md 5.2 KB
- references/sources/incident-postmortem.md 1.9 KB
- references/sources/linear.md 2.9 KB
- references/sources/notion.md 2.8 KB
- references/sources/sentry.md 4.8 KB
- references/sources/slack.md 3.0 KB
- references/synthesizer-prompt.md 7.8 KB
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 · 230 lines · 89 tokens per session scan A dc8f2d8a7dbe
why is a skill published in the GitHub repository backnotprop/pstack (165 stars, last pushed 13d ago), licensed MIT. It adds 89 tokens to every session and 4,599 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
wayfinder
Plan a huge chunk of work (more than one agent session can hold) as a shared map of decision tickets on your issue tracker, and resolve them one at a time until the way to the destination is clear.
setup-matt-pocock-skills
Configure this repo for the engineering skills: set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.
accessibility
Consolidated accessibility skill entrypoint for WCAG 2.2, ARIA Authoring Practices, cognitive accessibility, Section 508, EN 301 549, design intent verification, and the Accessibility Planner workflow.
release-notes
Generate user-facing release notes from tickets, PRDs, or changelogs. Creates clear, engaging summaries organized by category (new features, improvements, fixes). Use when writing release notes, creating changelogs, announcing product updates, or summarizing what shipped.
retro
Facilitate a structured sprint retrospective — what went well, what didn't, and prioritized action items with owners and deadlines. Use when running a retrospective, reflecting on a sprint, creating action items from team feedback, or learning how to run effective retros.
bug-triage
Read all open bugs in production/qa/bugs/, re-evaluate priority vs. severity, assign to sprints, surface systemic trends, and produce a triage report. Run at sprint start or when the bug count grows enough to need re-prioritization.