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 ttxttx1111/sts2-llm --skill sts2-review-rungit clone --depth 1 https://github.com/ttxttx1111/sts2-llmWrote 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/ttxttx1111/sts2-llm/sts2-review-run)<a href="https://agentmods.dev/skills/ttxttx1111/sts2-llm/sts2-review-run"><img src="https://agentmods.dev/badge/skills/ttxttx1111/sts2-llm/sts2-review-run/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/ttxttx1111/sts2-llm/sts2-review-run"><img src="https://agentmods.dev/badge/skills/ttxttx1111/sts2-llm/sts2-review-run.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 | $0.00033 | $0.00912 |
| Opus 5 | $0.00016 | $0.00456 |
| Sonnet 5 | $0.00007 | $0.00182 |
| Haiku 4.5 | $0.00003 | $0.00091 |
Grade A, and why
sts2-review-run 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
STS2 review run workflow
Use this skill when the task is to learn from what just happened.
What this skill owns
- compact checkpoint reviews after elites, bosses, near-lethal fights, and build-defining pivots
- full post-run or post-swing reviews
- deciding whether a heuristic is candidate, validated, situational, or retired
- updating persistent memory files with the new evidence
- maintaining a detailed review archive at ../../sts2-memory/reviews/README.md
- reconciling the active run timeline with the final run outcome
Review loop
-
Start from the actual run context
- summarize the state, fight, reward, or route pivot that triggered review
- identify the few decisions that materially changed the outcome
-
Distinguish tactical notes from durable lessons
- the full, detailed review belongs in the archive under ../../sts2-memory/reviews/
- tactical notes belong in the current playbook at ../../sts2-memory/current-playbook.md
- durable lessons belong in the ledger at ../../sts2-memory/lesson-ledger.md
-
Use the right review depth
- for a compact checkpoint, produce 2 to 4 sentences covering decision quality, risk trade, and which lesson was validated or challenged
- for a major swing, death, or win, use review-template.md
- every major review must include a deck autopsy that names the actual structural failure, not just the surface symptom
- because STS2 is highly random, compare the review against 1 to 3 relevant prior runs or explicitly say that the sample is still too small
- for any review that used retries or branch testing, explicitly record the baseline line, the best tested branch, the concrete gain from that branch, and why that branch was still enough or still not enough
-
Update lesson status explicitly
candidate: promising, lightly testedvalidated: repeatedly useful and safe to trustsituational: only right with clear qualifiersretired: no longer trusted
What ships with it
2 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.
- 10d ago First seen · 62 lines · 33 tokens per session scan A a8e34a3ee26f
sts2-review-run is a skill published in the GitHub repository ttxttx1111/sts2-llm (41 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 912 once invoked, about $0.0002 per session on Opus 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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