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 Razaib-khan/ForgeWeave --skill deep-researchgit clone --depth 1 https://github.com/Razaib-khan/ForgeWeaveWrote 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/razaib-khan/forgeweave/deep-research)<a href="https://agentmods.dev/skills/razaib-khan/forgeweave/deep-research"><img src="https://agentmods.dev/badge/skills/razaib-khan/forgeweave/deep-research/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/razaib-khan/forgeweave/deep-research"><img src="https://agentmods.dev/badge/skills/razaib-khan/forgeweave/deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00040 | $0.01325 |
| Opus 5.5 | $0.00016 | $0.00530 |
| Sonnet 5.5 | $0.00008 | $0.00265 |
| Haiku 4.5 | $0.00004 | $0.00133 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research
Purpose
Execute a multi-stage research pipeline that decomposes a vague topic into structured subtopics, gathers usage-focused information from authoritative sources via parallel agents, validates claims for consistency, produces a synthesis-grade report, and converts raw findings into a reusable industry-grade skill. This is invoked internally by the deep-research skill — never call the pipeline stages directly.
When to Use
- A comprehensive, multi-faceted report is needed covering 3+ subtopics
- The topic requires crawling multiple authoritative sources (docs, API refs, guides)
- The output must be structured, validated, and free of hallucination
- The question cannot be answered by a single source or quick lookup
When Not to Use
- A quick factual answer is needed — use websearch instead
- Only one source needs to be checked — use the MCP data plane tools directly
- The topic is a simple how-to question — answer directly
- Real-time data is needed (stock prices, live scores) — use web-research
Inputs
| Input | Type | Required | Description |
|---|---|---|---|
topic |
string | Yes | The research topic or question |
depth |
enum | No (default: standard) | quick, standard, deep |
focus |
enum | No (default: usage) | usage, architecture, comparison, general |
constraints |
string | No | Additional rules from AGENTS.md or user |
output_mode |
enum | No (default: formatted) | formatted, unformatted |
skill_mode |
enum | No (default: skill) | skill, no-skill |
Expected Outputs
| Output | Condition | Description |
|---|---|---|
research/<slug>-plan.md |
Always | Structured plan with subtopics, questions, seed URLs |
research/<slug>-raw/ |
Always | One file per subtopic from parallel research agents |
research/<slug>-validated.md |
Always | Cross-checked, deduplicated, hallucination-filtered |
research/<slug>-report.md |
formatted mode |
Final synthesis with all findings, code examples, sources |
.opencode/skills/<topic>/SKILL.md |
skill mode |
Reusable skill generated from findings |
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evals/test_happy_path.json 398 B
- examples/README.md 700 B
- references/pipeline-architecture.md 2.5 KB
- references/url-sourcing-rules.md 1.2 KB
- scripts/plan_generator.py 2.8 KB runs code
- scripts/synthesize_report.py 2.6 KB runs code
- scripts/validate_research.py 2.2 KB runs code
- templates/plan_template.md 413 B
- templates/report_template.md 918 B
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 · 124 lines · 40 tokens per session scan A 5ebd237b3d91
Deep Research is a skill published in the GitHub repository Razaib-khan/ForgeWeave (1 stars, last pushed 4mo ago), licensed MIT. It adds 40 tokens to every session and 1,325 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 2026-08-31.
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