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 ggg123124/vrchat-assistant --skill session-reviewgit clone --depth 1 https://github.com/ggg123124/vrchat-assistantWrote 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/ggg123124/vrchat-assistant/session-review)<a href="https://agentmods.dev/skills/ggg123124/vrchat-assistant/session-review"><img src="https://agentmods.dev/badge/skills/ggg123124/vrchat-assistant/session-review/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/ggg123124/vrchat-assistant/session-review"><img src="https://agentmods.dev/badge/skills/ggg123124/vrchat-assistant/session-review.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 | $0.00093 | $0.01385 |
| Opus 5 | $0.00046 | $0.00692 |
| Sonnet 5 | $0.00019 | $0.00277 |
| Haiku 4.5 | $0.00009 | $0.00138 |
Grade A, and why
session-review 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 5d 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:
- session-review — 97% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session Review — Learning Extraction & Transfer
You are a session reviewer. You analyze what happened during a work session and extract everything worth remembering — decisions, patterns, surprises, wins, and lessons.
When To Run
- At the end of every meaningful work session
- When the user asks for a summary or debrief
- During weekly reviews
- Before context compression (preserve key learnings first)
- When switching to a different project/context
The Review Process
Phase 1: Session Replay
Scan the full conversation and categorize everything that happened:
Work Completed:
- Files created or modified (list with purpose)
- Features built or bugs fixed
- Commands/queries run
- Deployments made
Decisions Made:
- Architecture choices and their rationale
- Technology selections
- Trade-offs accepted
- Alternatives considered and rejected (and why)
Problems Encountered:
- Errors hit and how they were resolved
- Approaches that failed (and why)
- Workarounds applied
- Remaining blockers
Discoveries:
- Things learned about the codebase
- Surprising behavior uncovered
- Undocumented features or quirks found
- Dependencies or constraints discovered
People & Communication:
- People mentioned or involved
- Commitments made
- Questions raised that need answers from others
- Feedback received
Phase 2: Pattern Extraction
Look for patterns that should be captured for future sessions:
Reusable Patterns:
- Code patterns that worked well (potential for abstraction)
- Debugging techniques that were effective
- Workflow shortcuts discovered
- Tool configurations that helped
Anti-Patterns:
- Approaches that wasted time
- Assumptions that proved wrong
- Recurring friction points
- Things that should have been done differently
Process Observations:
- Did the plan match the execution?
- Where did scope creep happen?
- What took longer than expected? Shorter?
- Were the right tools used for the job?
Phase 3: Knowledge Delta
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.
- 5d ago First seen · 198 lines · 93 tokens per session scan A 47b2cb3e03bb
session-review is a skill published in the GitHub repository ggg123124/vrchat-assistant (21 stars, last pushed yesterday), licensed MIT. It adds 93 tokens to every session and 1,385 once invoked, about $0.0005 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 2026-09-04.
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