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/michael-denyer/pstack-claude/whynpx skills add michael-denyer/pstack-claude --skill whygit clone --depth 1 https://github.com/michael-denyer/pstack-claudeWhat 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 | $0.04772 |
| Opus 5 | $0.00044 | $0.02386 |
| Sonnet 5 | $0.00018 | $0.00954 |
| Haiku 4.5 | $0.00009 | $0.00477 |
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 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.
This is a copy
89% identical to why — 20 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.
How it starts
The opening of the file, as written. The whole thing — 240 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.
Platform note. On Codex or another non-Claude runtime, the Claude tool names and claude-* slugs named below are Claude defaults. Resolve them via codex-tools.md.
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 6.9 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.
- 3d ago First seen · 240 lines · 89 tokens per session scan A ee7e1e2f9846
why is a skill published in the GitHub repository michael-denyer/pstack-claude (142 stars, last pushed 6d ago), licensed MIT. It adds 89 tokens to every session and 4,772 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to why, differing in 20 lines, and is treated as a copy.
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chat-perf
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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.
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