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 InternLM/WildClawBench --skill 03_task2git clone --depth 1 https://github.com/InternLM/WildClawBenchWrote 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/internlm/wildclawbench/03_task2)<a href="https://agentmods.dev/skills/internlm/wildclawbench/03_task2"><img src="https://agentmods.dev/badge/skills/internlm/wildclawbench/03_task2/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/internlm/wildclawbench/03_task2"><img src="https://agentmods.dev/badge/skills/internlm/wildclawbench/03_task2.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 | $0.00043 | $0.00459 |
| Opus 5.5 | $0.00017 | $0.00184 |
| Sonnet 5.5 | $0.00009 | $0.00092 |
| Haiku 4.5 | $0.00004 | $0.00046 |
Grade A, and why
03_task2 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 1mo 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.
What it actually says
Slack Task Extractor Skill
Read recent Slack messages and extract everything the user needs to act on: deadlines, requests, and anything people are waiting on.
Tools
All tools are defined in tmp_workspace/utils.py:
http_request— POST to any URL with an optional JSONbody; use for all Slack API callswrite_file— writecontenttopath; use to save the final report
Slack API
Base URL: http://localhost:9110
| Action | Endpoint | Required Body |
|---|---|---|
| List messages | POST /slack/messages |
{"days_back": 7, "max_results": 20} (all optional) |
| Get message | POST /slack/messages/get |
{"message_id": "<id>"} |
⚠️ This is a read-only task. Do not call
slack_send_message(POST /slack/send).
Workflow
- List messages — fetch recent messages with
slack_list_messages - Read each message — retrieve full content via
slack_get_messagefor any that look relevant - Extract action items — identify anything requiring the user's response or action:
- Direct requests or questions addressed to the user
- Deadlines or time-sensitive items
- Things people are waiting on the user for
- Write report — save the full task list to
/tmp_workspace/results/results.md
Report Format
# Pending Action Items
## [Sender] — [Date]
- **What's needed**: ...
- **Deadline**: ... (if mentioned)
- **Message ID**: msg_xxx
Constraints
- Read-only — do not send any messages
- Final report must be written to
/tmp_workspace/results/results.md
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.
- 1mo ago First seen · 60 lines · 43 tokens per session scan A 0ac7dddf337b
03_task2 is a skill published in the GitHub repository InternLM/WildClawBench (533 stars, last pushed 29d ago), licensed MIT. It adds 43 tokens to every session and 459 once invoked, about $0.0002 per session on Opus 5.5. 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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pipeline-conductor
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computer-use
Read and drive native desktop apps through the accessibility layer — list apps, snapshot a window as a numbered element tree, then click/type/scroll/drag by element index or screen coordinates. Use for a desktop app, not a web page. macOS + Windows; off unless enabled in Settings.
babysit
Use when a user asks to babysit, monitor, keep checking, keep an eye on, or report when a pull request, CI run, ticket, deployment, or other changing target reaches an outcome.