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/matheusht/redthread/researchnpx skills add matheusht/redthread --skill researchgit clone --depth 1 https://github.com/matheusht/redthreadWhat 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 | $0.00022 | $0.00192 |
| Opus 5 | $0.00011 | $0.00096 |
| Sonnet 5 | $0.00004 | $0.00038 |
| Haiku 4.5 | $0.00002 | $0.00019 |
Grade A, and why
Deep Research & Context Gathering 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 today.
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
Deep Research Skill
Trigger condition:
When the task involves understanding large swathes of the codebase, exploring patterns, or finding documentation context prior to planning.
Requirements:
- Model Selection: Use Opus 4.6 for executing this Research skill. Do not rely on smaller/tier-two models for deep cross-module correlation.
- Behavior constraint: You are restricted entirely from modifying files, creating structures, or proposing code diffs.
- Execution pattern:
- Use
grep_searchandview_file. - Extract paths and explicit line numbers
(e.g., path/file.py#L40-L50). - Synthesize an objective summary.
- Validate your token footprint. If >25% context, prune redundant returns.
- Use
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.
- today First seen · 19 lines · 22 tokens per session scan A e2565e75b793
Deep Research & Context Gathering is a skill published in the GitHub repository matheusht/redthread (47 stars, last pushed 10d ago), licensed MIT. It adds 22 tokens to every session and 192 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 2026-09-01.
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