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/gleanwork/cursor-plugins/searchnpx skills add gleanwork/cursor-plugins --skill searchgit clone --depth 1 https://github.com/gleanwork/cursor-pluginsWhat 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 | $0.00061 | $0.00735 |
| Opus 5 | $0.00030 | $0.00367 |
| Sonnet 5 | $0.00012 | $0.00147 |
| Haiku 4.5 | $0.00006 | $0.00073 |
Grade A, and why
search 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.
This is a copy
100% identical to search — 0 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Structured Glean Search
Perform a structured search across Glean enterprise knowledge and return vetted, quality-assessed results.
Core Principles
- Relevance over completeness: Show the best results, not all results
- Be skeptical: Not every keyword match is relevant
- Context matters: Include enough info to assess relevance
Search Process
1. Identify the Query
Determine the search topic from the user's request or current conversation context. If no query is apparent, ask the user what they want to search for before proceeding.
2. Execute Search
Use the Glean search tool with the user's query. Return the most relevant results.
3. Assess Results
For each result, evaluate:
Relevance:
- ✅ RELEVANT: Actually about the query topic
- ❌ SKIP: Keyword coincidence, different context
Currency:
- ✅ CURRENT: Recent update
- ⚠️ OLD: May be outdated
Only show results that pass the relevance check. If old, note it.
4. Present Vetted Results
For each included result:
- Title (as a clickable link if URL available)
- Source (app/datasource)
- Last updated (with freshness indicator: ✅ <6mo, ⚠️ 6-12mo, ❌ >12mo)
- Snippet (relevant excerpt)
- Relevance note (why this matches)
5. Note Quality
After results, include:
- How many results were found vs. shown
- Any concerns about result quality
- Suggestions if results seem limited
6. Offer Follow-up Actions
After showing results, offer these follow-up actions:
- Read a document in full
- Refine the search with filters (by date, owner, app/source, or different keywords)
- Search a related topic
Example Output
## Search Results: [query]
Found [X] results, showing top [Y] most relevant:
### 1. [Title] ✅
**Source**: Confluence | **Updated**: 2 weeks ago ✅
> [Relevant snippet...]
**Why relevant**: [Brief note on why this matches]
### 2. [Title] ⚠️
**Source**: Slack | **Updated**: 8 months ago ⚠️
> [Relevant snippet...]
**Why relevant**: [Note] | **Caveat**: May be outdated
---
**Quality note**: [X] results filtered out (keyword matches in different context)
**If these don't help**: Try [alternative search suggestion]
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 · 106 lines · 61 tokens per session scan A 39330b524321
search is a skill published in the GitHub repository gleanwork/cursor-plugins (3 stars, last pushed 12d ago), licensed MIT. It adds 61 tokens to every session and 735 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to search, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…