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/thanh-abaii/gstack-windows-port/gstack-learnnpx skills add thanh-abaii/gstack-windows-port --skill gstack-learngit clone --depth 1 https://github.com/thanh-abaii/gstack-windows-portWrote 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/thanh-abaii/gstack-windows-port/gstack-learn)<a href="https://agentmods.dev/skills/thanh-abaii/gstack-windows-port/gstack-learn"><img src="https://agentmods.dev/badge/skills/thanh-abaii/gstack-windows-port/gstack-learn.svg" alt="Measured on agentmods" 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 | $0.00075 | $0.09613 |
| Opus 5 | $0.00037 | $0.04806 |
| Sonnet 5 | $0.00015 | $0.01923 |
| Haiku 4.5 | $0.00007 | $0.00961 |
Grade A, and why
gs:learn 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 4d 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
86% identical to gs:checkpoint — 350 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 — 848 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Preamble (run first)
python "bin/gstack-boot.py" --skill learn | iex
If PROACTIVE is "false", do not proactively suggest gstack skills AND do not
auto-invoke skills based on conversation context. Only run skills the user explicitly
types (e.g., /gs:qa, /gs:ship). If you would have auto-invoked a skill, instead briefly say:
"I think /skillname might help here — want me to run it?" and wait for confirmation.
The user opted out of proactive behavior.
If SKILL_PREFIX is "true", the user has namespaced skill names. When suggesting
or invoking other gstack skills, use the /gstack- prefix (e.g., /gstack-qa instead
of /qa, /gstack-ship instead of /ship). Disk paths are unaffected — always use
$GSTACK_ROOT/[skill-name]/SKILL.md for reading skill files.
If output shows UPGRADE_AVAILABLE <old> <new>: read $GSTACK_ROOT/gstack-upgrade/SKILL.md and follow the "Inline upgrade flow" (auto-upgrade if configured, otherwise AskUserQuestion with 4 options, write snooze state if declined). If JUST_UPGRADED <from> <to>: tell user "Running gstack v{to} (just updated!)" and continue.
If WRITING_STYLE_PENDING is yes: You're on the first skill run after upgrading
to gstack v1. Ask the user once about the new default writing style. Use AskUserQuestion:
v1 prompts = simpler. Technical terms get a one-sentence gloss on first use, questions are framed in outcome terms, sentences are shorter.
Keep the new default, or prefer the older tighter prose?
Options:
- A) Keep the new default (recommended — good writing helps everyone)
- B) Restore V0 prose — set
explain_level: terse
If A: leave explain_level unset (defaults to default).
If B: run $GSTACK_BIN/gstack-config set explain_level terse.
Always run (regardless of choice):
Remove-Item -Force ~/.gstack/.writing-style-prompt-pending
touch ~/.gstack/.writing-style-prompted
This only happens once. If WRITING_STYLE_PENDING is no, skip this entirely.
If LAKE_INTRO is no: Before continuing, introduce the Completeness Principle.
Tell the user: "gstack follows the Boil the Lake principle — always do the complete
thing when AI makes the marginal cost near-zero. Read more: https://garryslist.org/posts/boil-the-ocean"
Then offer to open the essay in their default browser:
open https://garryslist.org/posts/boil-the-ocean
touch ~/.gstack/.completeness-intro-seen
Only run open if the user says yes. Always run touch to mark as seen. This only happens once.
If TEL_PROMPTED is no AND LAKE_INTRO is yes: After the lake intro is handled,
ask the user about telemetry. Use AskUserQuestion:
Help gstack get better! Community mode shares usage data (which skills you use, how long they take, crash info) with a stable device ID so we can track trends and fix bugs faster. No code, file paths, or repo names are ever sent. Change anytime with
gstack-config set telemetry off.
Options:
- A) Help gstack get better! (recommended)
- B) No thanks
If A: run $GSTACK_BIN/gstack-config set telemetry community
If B: ask a follow-up AskUserQuestion:
How about anonymous mode? We just learn that someone used gstack — no unique ID, no way to connect sessions. Just a counter that helps us know if anyone's out there.
Options:
- A) Sure, anonymous is fine
- B) No thanks, fully off
If B→A: run $GSTACK_BIN/gstack-config set telemetry anonymous
If B→B: run $GSTACK_BIN/gstack-config set telemetry off
Always run:
touch ~/.gstack/.telemetry-prompted
This only happens once. If TEL_PROMPTED is yes, skip this entirely.
If PROACTIVE_PROMPTED is no AND TEL_PROMPTED is yes: After telemetry is handled,
ask the user about proactive behavior. Use AskUserQuestion:
gstack can proactively figure out when you might need a skill while you work — like suggesting /gs:qa when you say "does this work?" or /gs:investigate when you hit a bug. We recommend keeping this on — it speeds up every part of your workflow.
What ships with it
1 file 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.
- 4d ago First seen · 848 lines · 75 tokens per session scan A 5db7eca226a2
gs:learn is a skill published in the GitHub repository thanh-abaii/gstack-windows-port (34 stars, last pushed 3mo ago), licensed MIT. It adds 75 tokens to every session and 9,613 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to gs:checkpoint, differing in 350 lines, and is treated as a copy.
Other skills, from other repositories
mem0-oss-to-platform
Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…
Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
auditing-subgroup-fairness
Audit an OpenMed NER or de-identification model for performance disparities across demographic subgroups (sex, age band, race/ethnicity when available) using openmed.eval.fairnessreport. Use when the user wants per-subgroup recall and leakage, wants to check whether de-identification under-protects a group, wants to…
agent-memory
../../../engineering/agent-memory/skills/agent-memory/SKILL.md.
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…