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/richfrem/agent-plugins-skills/exploration-optimizernpx skills add richfrem/agent-plugins-skills --skill exploration-optimizergit clone --depth 1 https://github.com/richfrem/agent-plugins-skillsWrote 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/richfrem/agent-plugins-skills/exploration-optimizer)<a href="https://agentmods.dev/skills/richfrem/agent-plugins-skills/exploration-optimizer"><img src="https://agentmods.dev/badge/skills/richfrem/agent-plugins-skills/exploration-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 | $0.00041 | $0.00709 |
| Opus 5 | $0.00020 | $0.00354 |
| Sonnet 5 | $0.00008 | $0.00142 |
| Haiku 4.5 | $0.00004 | $0.00071 |
Grade A, and why
exploration-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 yesterday.
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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Exploration Optimizer
Discovery Phase
Ask for:
- The target exploration skill or agent to optimize.
- The eval set to use, or whether to generate one from the current architecture.
- The iteration budget.
- Whether auto-apply of winning variants is allowed.
- Which metrics matter most for this loop: routing quality, artifact usefulness, handoff stability, re-entry quality, or human intervention burden.
- Whether post-run survey data exists and should be included in the decision.
Recap
Confirm:
- target component
- eval source
- loop budget
- chosen scoring dimensions
- whether survey data is available
- whether auto-apply is enabled
Execution
This skill implements autoresearch-style optimization for the exploration-cycle system. It uses a baseline-first iteration loop to improve skill prompts and logic.
Usage:
python ./scripts/execute.py \
--target ${plugins}/skills/user-story-capture/SKILL.md \
--eval-script ./scripts/eval_runner.py \
--goal "Improve Gherkin block accuracy" \
--iterations 3
Iteration Loop
The execute.py script follows a disciplined loop:
- Change one dominant variable per iteration.
- Re-run evaluations.
- Mark the attempt as
keepordiscard. - If the run crashes or times out, log the failure and continue from the last known good state.
- Never let a subjective preference override a clear regression in the tracked metrics.
- Use survey feedback as a quality signal, not an excuse to ignore the baseline-first method.
What ships with it
7 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.
- yesterday First seen · 89 lines · 41 tokens per session scan A 2a651de8dcb8
exploration-optimizer is a skill published in the GitHub repository richfrem/agent-plugins-skills (6 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 709 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 2026-09-03.
Other skills, from other repositories
composio
Use Composio from Agent Swarm through the agent-swarm x composio CLI route, the swarmx MCP tool, or a registered ctx.api.composio script connection. Trigger when a task needs connected third-party app tools such as Gmail, Google Calendar, Google Docs, Google Drive, GitHub, Slack, Notion, or HubSpot through Tool Router…
soul
Embody this digital identity. Read SOUL.md first, then STYLE.md, then examples/. Become the person—opinions, voice, worldview.
close-issue
Close a GitHub or GitLab issue with a summary comment.
implement-issue
Implement a GitHub issue or GitLab issue and create a PR/MR.
bbc-skill
Fetch Bilibili (哔哩哔哩) video comments for UP主 self-analysis. Use when the user asks to collect, download, export, or analyze comments on a Bilibili video (BV号 / URL / UID). Produces JSONL + summary.json suitable for further Claude Code analysis (sentiment, keywords, audience trends). Read-only; does not…
team-coordination
Coordinate software development work by analyzing requirements, delegating tasks to specialized sub-agents (developer, code-reviewer, tester), and synthesizing their work into cohesive deliverables. Use this for complex projects that require multiple specialized perspectives.