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 Kaos599/Deep-Ass-Research --skill deep-ass-researchgit clone --depth 1 https://github.com/Kaos599/Deep-Ass-ResearchWrote 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/kaos599/deep-ass-research/deep-ass-research)<a href="https://agentmods.dev/skills/kaos599/deep-ass-research/deep-ass-research"><img src="https://agentmods.dev/badge/skills/kaos599/deep-ass-research/deep-ass-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/kaos599/deep-ass-research/deep-ass-research"><img src="https://agentmods.dev/badge/skills/kaos599/deep-ass-research/deep-ass-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 | $0.00115 | $0.00824 |
| Opus 5 | $0.00057 | $0.00412 |
| Sonnet 5 | $0.00023 | $0.00165 |
| Haiku 4.5 | $0.00012 | $0.00082 |
Grade A, and why
deep-ass-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 10d 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 — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Ass Research (DAR) — Claude Code adapter
This is the Claude Code entry point for the portable DAR framework. The methodology is host-agnostic and lives in core/; this file just binds DAR's capability verbs to Claude Code's tools and tells you how to spawn the roles.
How to run
- Read
core/orchestration.mdand follow it exactly. It is the runbook (preflight → charter → breadth → gate → depth → drift guard → verify → synthesize → deliver). Read its referenced files as needed:core/capabilities.md,core/methodology.md,core/vault-layout.md,core/note-schemas.md,core/provenance.md, and the chosencore/modes/<mode>.md. - Spawn sub-agents with the Task / Agent tool (
subagent_type: general-purpose). For each role, readcore/roles/<role>.mdand paste its full content as the sub-agent prompt, then append a## Taskblock with the thread/claim context and the absolute vault path. (This mirrors thebug-hunt-reviewskill's dispatch pattern.) Run independent sub-agents in parallel by issuing multiple Task calls in one message. - Track progress in the vault files
_progress.mdand_open-threads.md(source of truth). You MAY also mirror_progress.mdinto TodoWrite for visibility.
Capability bindings (Claude Code)
| Verb | Binding |
|---|---|
WRITE_FILE |
Write / Edit |
ASK_USER |
AskUserQuestion |
SPAWN_SUBAGENT |
Task / Agent tool (general-purpose), parallel via multiple calls |
SEARCH |
Tavily / Exa MCP if present, else built-in WebSearch |
FETCH |
Tavily extract / Exa fetch if present, else built-in WebFetch |
BROWSE |
TinyFish MCP (run_web_automation) |
READ_DOCS |
Context7 + Ref MCP, else WebSearch+WebFetch on official docs |
Record the actual bindings (and any degraded verbs) in _session.md during preflight.
Optional acceleration (Workflow tool)
For determinism, resumability, and budget control, the Depth → Drift → Verify → Synthesize stretch MAY run via adapters/claude-code/dar-pipeline.workflow.js (native Workflow tool). If you use it: first read the role files in core/roles/ and the chosen core/modes/<mode>.md, then invoke the Workflow passing them in args (the script sandbox can't read files): args = {vault, mode, brief, threads, roles:{...}, playbook}. Otherwise just follow steps 3–5 of core/orchestration.md directly. Running DAR implies opting into multi-agent orchestration — announce the approach before launching.
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.
- 10d ago First seen · 43 lines · 115 tokens per session scan A d01dc9489f72
deep-ass-research is a skill published in the GitHub repository Kaos599/Deep-Ass-Research (4 stars, last pushed 1mo ago), licensed MIT. It adds 115 tokens to every session and 824 once invoked, about $0.0006 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.
Other skills, from other repositories
harnessay
Profile Claude Code transcripts. /harnessay = context budget report + skill candidates, /harnessay eval = skill regression tests. Triggers - "harnessay", "context report", "where did my tokens go", "skill candidates", "skill regression".
content-seo
When the user wants to brief, write, review, optimize, or publish public SEO content. Also use for article outlines, blog posts, landing-page copy, refreshes, and ranking-oriented editorial drafts.
competitive-analysis
When the user wants to compare domains or URLs against competitors across SEO footprint, share of voice, keyword/content gap, head-to-head pages, off-page link gap (via backlink-analysis), and brand positioning. Orchestrates evidence from serp-extract, keyword-research, backlink-analysis, and topic-cluster without…
autoresearch
When the user wants a rigorous iteration loop for an artifact, prompt, briefing, content structure, or Agentic SEO skill. Also use for Karpathy-style experiment runs that need baseline scoring, explicit metrics, stop rules, and keep/reject decisions.
eeat
When the user wants to audit, strengthen, or prepare evidence for Experience, Expertise, Authoritativeness, and Trust. Also use before registering proof entries in project/brain/log.md or referencing them in project/brain/topic-clusters.md.
internal-links
When the user wants internal link opportunities, contextual link recommendations, anchor text improvements, or validation before applying internal links.