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/s977043/river-review/skill-optimizernpx skills add s977043/river-review --skill skill-optimizergit clone --depth 1 https://github.com/s977043/river-reviewWrote 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/s977043/river-review/skill-optimizer)<a href="https://agentmods.dev/skills/s977043/river-review/skill-optimizer"><img src="https://agentmods.dev/badge/skills/s977043/river-review/skill-optimizer.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 5.1 | $0.00085 | $0.00965 |
| Opus 5 | $0.00043 | $0.00483 |
| Sonnet 5 | $0.00017 | $0.00193 |
| Haiku 4.5 | $0.00009 | $0.00097 |
Grade A, and why
skill-optimizer 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 6d 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pattern declaration
Primary pattern: Reviewer Secondary patterns: Pipeline Why: optimization starts with diagnosis against criteria, then applies small changes in a controlled sequence with eval checkpoints.
Purpose
Improve an existing skill without breaking what already works.
Your job is to:
- inspect the current skill package
- define or refine success criteria
- identify failure modes
- propose one small change at a time
- attach each change to an eval hypothesis
- reject changes that cannot be evaluated
Core optimization rule
Never apply a large rewrite first.
Optimize in small units:
- description
- gate logic
- workflow order
- examples
- prohibited behaviors
- output contract
- review checklist
- supporting file structure
Change only one major unit per proposal.
Phase 0: Baseline audit
Inspect:
- current
SKILL.md - current supporting files
- invocation settings
- current examples
- current failure reports or user complaints
- existing eval cases if any
Then summarize:
- what the skill is supposed to do
- where it fails
- whether the issue is discovery, execution, or validation
Phase 1: Success criteria
Define 3 to 6 evaluation criteria.
Each criterion must be:
- specific
- observable
- pass/fail or narrowly scored
Separate:
- trigger quality
- task fidelity
- completeness
- safety / side effects
- output format compliance
Phase 2: Failure mapping
Classify failures into:
- over-triggering
- under-triggering
- missing context collection
- vague output
- hallucinated assumptions
- skipped verification
- unnecessary tool use
- high token or step cost
Phase 2.5: Pattern mismatch diagnosis
Before proposing wording or structure fixes, you must check whether the failure is caused by the wrong pattern or a missing secondary pattern. Do not skip this phase.
Check:
- acts too early on ambiguous input → missing Inversion
- output structure is inconsistent across runs → missing Generator
- returns unvalidated or unchecked output → missing Reviewer
- skips required steps or loses sequence control → missing Pipeline
- lacks domain-specific accuracy → missing Tool Wrapper
What ships with it
3 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.
- 6d ago First seen · 198 lines · 85 tokens per session scan A 99a4535dc6c4
skill-optimizer is a skill published in the GitHub repository s977043/river-review (3 stars, last pushed yesterday), licensed MIT. It adds 85 tokens to every session and 965 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
review-all
Multi-agent code review for diffs (project-agnostic). Covers standards, bugs, security, DRY, smells, perf, tests, API contracts, a11y/i18n. Verifies each finding to eliminate false positives. Use for /review-all, pre-PR/pre-commit review, or auditing uncommitted/staged changes.
logic-health
Sweep a directory, module, or full codebase for logic correctness and produce a scored health dashboard with systemic patterns. Trigger when the user requests a health view — "audit the whole codebase", "health check", "health overview", "logic health overview", "audit src/", "audit auth and payments modules", "where…
logic-diff
Compare two code versions for semantic equivalence via semi-formal tracing of both versions side-by-side. Trigger when the user shares a refactor, rewrite, migration, or A/B implementation and wants to confirm behavior is unchanged — "did I break anything", "is this equivalent", "are these equivalent", "semantically…
omnicheck-gitlab
Use when checking if MR review findings have been applied — verifies both OmniForge-generated and human reviewer comments against the current diff, posts nudge replies on unaddressed threads.
omnicheck-github
Use when checking if PR review findings have been applied — verifies both OmniForge-generated and human reviewer comments against the current diff, posts nudge replies on unaddressed threads.
omnicreate-gitlab
Use when creating a GitLab merge request (OmniForge). Auto-populates title and description from commits, supports draft MRs, labels, assignees, reviewers, and issue linking.