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/claude-plugins/glean_runnpx skills add gleanwork/claude-plugins --skill glean_rungit clone --depth 1 https://github.com/gleanwork/claude-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 | $0.00014 | $0.01010 |
| Opus 5 | $0.00007 | $0.00505 |
| Sonnet 5 | $0.00003 | $0.00202 |
| Haiku 4.5 | $0.00001 | $0.00101 |
Grade A, and why
glean_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 2d 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:
- glean_run — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Glean Run
Discover and use Glean skills to help with enterprise app tasks (Jira, Slack, Google Workspace, Salesforce, etc.) or actions you don't already have a tool for. Where possible, aim to complete the user's request end-to-end rather than just listing available skills.
Authentication
Authentication is handled exclusively by the setup tool. If any other tool
returns a response containing [SETUP_REQUIRED], the user needs to
(re-)authenticate via setup.
When this happens:
- Call
setup(no arguments).- If no Server URL is configured,
setupreturns[SETUP_REQUIRED]with instructions. Relay them, ask the user for their work email, then callsetupagain withemailset to what they provided. - Once a Server URL is configured,
setupopens the Glean sign-in page in the browser and waits for sign-in.
- If no Server URL is configured,
- Once
setupreturns "Glean setup is complete", retry the original tool call.
Do not treat [SETUP_REQUIRED] as an error or try to work around it any
other way.
Step 0: Verify Setup
Call setup (with no arguments). If the connection isn't ready, setup
returns instructions — follow them and call setup again; it guides the whole
flow. Once it returns "Glean setup is complete", proceed to Step 1.
Step 1: Plan tool usage
A small set of popular tools is directly available, and no discovery is needed to use them. Discover is complementary and recommended if the direct tools cannot satisfy the user request end to end.
Calling find_skills_and_tools
If no arguments were provided and the task can't be inferred from conversation context, ask the user what they'd like to do before proceeding.
Call find_skills_and_tools with the task descriptions.
Break the request into small, task-atomic queries — keep only the core action,
dropping the surrounding context (recipients, timing, reasons, constraints) —
and pass each as a separate entry in queries.
find_skills_and_tools({
queries: [
"<atomic sub-task 1>",
"<atomic sub-task 2>"
]
})
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.
- 2d ago First seen · 125 lines · 14 tokens per session scan A fc6a3562f8e1
glean_run is a skill published in the GitHub repository gleanwork/claude-plugins (25 stars, last pushed 12d ago), licensed MIT. It adds 14 tokens to every session and 1,010 once invoked, about $0.0001 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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Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.