Borrowing it
Nothing to install: this file belongs to tellahq/opensession. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tellahq/opensession/main/.agents/skills/pstack-suite/skills/how/SKILL.mdgit clone --depth 1 https://github.com/tellahq/opensessionWrote 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/tellahq/opensession/how)<a href="https://agentmods.dev/skills/tellahq/opensession/how"><img src="https://agentmods.dev/badge/skills/tellahq/opensession/how/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/tellahq/opensession/how"><img src="https://agentmods.dev/badge/skills/tellahq/opensession/how.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector 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 | $0.00064 | $0.01531 |
| Opus 5.5 | $0.00026 | $0.00612 |
| Sonnet 5.5 | $0.00013 | $0.00306 |
| Haiku 4.5 | $0.00006 | $0.00153 |
Grade A, and why
how 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
78% identical to how — 117 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
How
Explore the codebase to answer "how does X work?" questions. Produce clear architectural explanations at the level of a senior engineer onboarding onto a subsystem. Enough to build a working mental model, not annotated source code.
Two modes:
- Explain (default). Explore the codebase and produce a clear explanation
- Critique. Explain first, then spawn multiple models to independently identify architectural issues
Explain Mode
Step 1. Understand the Question and Assess Complexity
Parse what the user is asking about:
- "How does the rate limiter work?", a subsystem
- "How do we handle billing for on-demand usage?", a feature flow
- "How is the auth service structured?", an architectural overview
- "Walk me through what happens when a user submits a form", a runtime trace
Identify the scope. If ambiguous, state your best-guess interpretation before exploring. Don't ask. Let the user redirect if you're off.
Assess complexity to decide the approach:
- Simple (a single module, a small utility, a narrow question like "how does function X work"): skip explorer agents; the explainer explores and explains in a single pass. Go to Step 2b.
- Complex (a subsystem spanning multiple files/services, a cross-cutting feature, a full architectural overview): spawn parallel explorer agents first, then hand off to the explainer. Go to Step 2a.
When in doubt, lean simple. You can always spawn explorers if the explainer hits a wall.
Step 2a. Explore (complex questions only)
Decompose the question into 2-4 parallel exploration angles, each a distinct slice of the subsystem so explorers don't duplicate work. Example split for "how does the rate limiter work?":
- Explorer 1: data model and state management
- Explorer 2: request path and enforcement
- Explorer 3: configuration and metrics infrastructure
The right decomposition depends on the question. Use your judgment. Narrow questions: 2 explorers is fine. Broad subsystems: up to 4.
Discover the policy-gated Open Session session tools and spawn all explorers in parallel in ask mode. Begin each self-contained brief with /pstack. Omit model unless the current workspace preset provides a deliberate configured supporting model. Do not grant write or edit tools.
What ships with it
4 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.
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 First seen · 120 lines · 64 tokens per session scan A 0c3cf2627fe4
how is a skill published in the GitHub repository tellahq/opensession (399 stars, last pushed today), licensed MIT. It adds 64 tokens to every session and 1,531 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 78% identical to how, differing in 117 lines, and is treated as a copy.
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