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/nirecom/agents/deep-researchnpx skills add nirecom/agents --skill deep-researchgit clone --depth 1 https://github.com/nirecom/agentsWhat 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 | $0.00027 | $0.00397 |
| Opus 5 | $0.00014 | $0.00198 |
| Sonnet 5 | $0.00005 | $0.00079 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
deep-research 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.
What it actually says
Investigate external information related to the given task.
Procedure
DR-1. Delegate to web-researcher:
Agent({ subagent_type: "web-researcher", prompt: JSON.stringify({
topic: TOPIC, context: CONTEXT,
artifact_dir: PLANS_DIR
}) })
On failed status: surface summary to user and stop.
DR-2. Read the report from artifact_path (one read, at the end).
DR-3. Present findings — output format: ## Deep Research: PERFORMED|FAILED (1 line) + artifact_path pointer (1 line) + ≤200 char summary. Do not re-emit the full report text in assistant output. The caller must not re-summarize or paraphrase these findings — DR-3 output is the complete user-facing surface.
Rules
- Do not modify any project files
- Always include source URLs for traceability
- Prefer primary sources (official docs, RFCs) over blog posts
- When sources contradict each other, report both sides instead of choosing one
Completion
After completing this skill:
- Run:
echo "<<WORKFLOW_MARK_STEP_research_complete>>"(must be the ENTIRE Bash command — no pipes, no && chaining, no redirection)
Skip this skill when no external knowledge is needed (e.g., the task is purely internal to the codebase).
If research is genuinely not needed for this task:
- Run:
echo "<<WORKFLOW_RESEARCH_NOT_NEEDED: {reason}>>"(reason must be ≥3 non-space chars, not a placeholder like "none"/"skip", and contain no '>')
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 · 40 lines · 27 tokens per session scan A ee5b0ea21cbf
deep-research is a skill published in the GitHub repository nirecom/agents (3 stars, last pushed 3d ago), licensed MIT. It adds 27 tokens to every session and 397 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 2026-08-31.
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