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 minghinmatthewlam/agent-guards --skill whygit clone --depth 1 https://github.com/minghinmatthewlam/agent-guardsWrote 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/minghinmatthewlam/agent-guards/why)<a href="https://agentmods.dev/skills/minghinmatthewlam/agent-guards/why"><img src="https://agentmods.dev/badge/skills/minghinmatthewlam/agent-guards/why/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/minghinmatthewlam/agent-guards/why"><img src="https://agentmods.dev/badge/skills/minghinmatthewlam/agent-guards/why.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00089 | $0.01945 |
| Opus 5.5 | $0.00036 | $0.00778 |
| Sonnet 5.5 | $0.00018 | $0.00389 |
| Haiku 4.5 | $0.00009 | $0.00194 |
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.
This is a copy
92% identical to why — 19 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 — 99 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. The companion how skill answers what the code does and how it works. why answers what forces led to its shape.
Each spawn below names a role line in the ~/.config/pstack/models.md rule and a default. Set model to that line's value, or to the default if the rule or the line is missing. Leave model unset when the value is auto or inherit-parent. If the Task tool rejects a slug, use the default and say so. If it rejects the default, use the closest valid slug of the same family from its error message.
Every spawn is generalPurpose with readonly: false (agent mode). Do not use readonly/Ask mode. It strips MCP access, which disables the MCP-backed investigators and the synthesizer's citation spot-checks. Investigators still shouldn't write anything.
Operating Posture
Operate as a careful, cautious, and precise investigator. Be honest about what you know vs what you're inferring. Read references/epistemics.md for the full confidence framework and phrasing guide. The synthesizer must follow it.
Step 1. Understand the Target and the Question
The target is usually a chunk of code, a pattern, a feature, or a named design decision. The question is usually a design rationale, a tradeoff, a motivating edge case, an external constraint, dead code, or a broad history sweep.
If the target is vague ("why do we do it this way?" with no clear referent), make your best guess from conversation context (open files, recent edits, cursor location, what was just discussed). State your interpretation briefly so the user can redirect if you're off, then proceed.
Step 2. Establish the Code Anchor
Before spawning investigators, build the seed context inline: file paths and line ranges, key symbols (functions, classes, constants), the last few commits touching the target, PR numbers from merge commits (pattern (#1234) in the subject line), and any linked ticket IDs. Run git blame -L <start>,<end> <file> for last-touch commits, git log --follow -p -- <file> for full history with patches through renames, git log --oneline -20 -- <file> for recent commits with PR numbers visible, and git log -1 --format=%B <commit> to extract PR numbers. Then pull the PR bodies and discussion for any substantive commit: gh pr view <number> --json title,body,author,createdAt,mergedAt,labels,closingIssuesReferences,comments,reviews. This anchor goes to every investigator and to the synthesizer.
What ships with it
13 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.
- agents/openai.yaml 43 B
- references/epistemics.md 6.6 KB
- references/investigator-prompt.md 5.4 KB
- references/source-playbook.md 1.3 KB
- references/sources/code-archaeology.md 3.3 KB
- references/sources/databricks.md 6.8 KB
- references/sources/datadog.md 4.9 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 4.6 KB
- references/sources/slack.md 3.0 KB
- references/synthesizer-prompt.md 7.0 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 · 99 lines · 89 tokens per session scan A a8d8eb7622b8
why is a skill published in the GitHub repository minghinmatthewlam/agent-guards (47 stars, last pushed 2d ago), licensed MIT. It adds 89 tokens to every session and 1,945 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to why, differing in 19 lines, and is treated as a copy.
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