gstack is a set of opinionated tools that gives a coding agent workflows associated with roles such as product leadership, design, engineering management, release management, documentation, and quality assurance. It is for developers who want one coding agent to support more parts of the software delivery process.
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 garrytan/gstack --skill gstack-openclaw-investigategit clone --depth 1 https://github.com/garrytan/gstackWrote 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/garrytan/gstack/gstack-openclaw-investigate)<a href="https://agentmods.dev/skills/garrytan/gstack/gstack-openclaw-investigate"><img src="https://agentmods.dev/badge/skills/garrytan/gstack/gstack-openclaw-investigate/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/garrytan/gstack/gstack-openclaw-investigate"><img src="https://agentmods.dev/badge/skills/garrytan/gstack/gstack-openclaw-investigate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 | $0.00046 | $0.01226 |
| Opus 5.5 | $0.00018 | $0.00490 |
| Sonnet 5.5 | $0.00009 | $0.00245 |
| Haiku 4.5 | $0.00005 | $0.00123 |
Grade A, and why
gstack-openclaw-investigate 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- gstack-openclaw-investigate — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Systematic Debugging
Iron Law
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST.
Fixing symptoms creates whack-a-mole debugging. Every fix that doesn't address root cause makes the next bug harder to find. Find the root cause, then fix it.
Phase 1: Root Cause Investigation
Gather context before forming any hypothesis.
-
Collect symptoms: Read the error messages, stack traces, and reproduction steps. If the user hasn't provided enough context, ask ONE question at a time. Don't ask five questions at once.
-
Read the code: Trace the code path from the symptom back to potential causes. Search for all references, read the logic around the failure point.
-
Check recent changes:
git log --oneline -20 -- <affected-files>Was this working before? What changed? A regression means the root cause is in the diff.
-
Reproduce: Can you trigger the bug deterministically? If not, gather more evidence before proceeding.
-
Check memory for prior debugging sessions on the same area. Recurring bugs in the same files are an architectural smell.
Output: "Root cause hypothesis: ..." ... a specific, testable claim about what is wrong and why.
Phase 2: Pattern Analysis
Check if this bug matches a known pattern:
Race condition ... Intermittent, timing-dependent. Look at concurrent access to shared state.
Nil/null propagation ... NoMethodError, TypeError. Missing guards on optional values.
State corruption ... Inconsistent data, partial updates. Check transactions, callbacks, hooks.
Integration failure ... Timeout, unexpected response. External API calls, service boundaries.
Configuration drift ... Works locally, fails in staging/prod. Env vars, feature flags, DB state.
Stale cache ... Shows old data, fixes on cache clear. Redis, CDN, browser cache.
Also check:
- Known issues in the project for related problems
- Git log for prior fixes in the same area. Recurring bugs in the same files are an architectural smell, not a coincidence.
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 · 135 lines · 46 tokens per session scan A ab4ef9f9482a
gstack-openclaw-investigate is a skill published in the GitHub repository garrytan/gstack (137,834 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 1,226 once invoked, about $0.0002 per session on Opus 5.5. 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.
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