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.
git 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/agents/kaos599/deep-ass-research/dar-synthesizer)<a href="https://agentmods.dev/agents/kaos599/deep-ass-research/dar-synthesizer"><img src="https://agentmods.dev/badge/agents/kaos599/deep-ass-research/dar-synthesizer/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/agents/kaos599/deep-ass-research/dar-synthesizer"><img src="https://agentmods.dev/badge/agents/kaos599/deep-ass-research/dar-synthesizer.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 | $0.00020 | $0.00754 |
| Opus 5 | $0.00010 | $0.00377 |
| Sonnet 5 | $0.00004 | $0.00151 |
| Haiku 4.5 | $0.00002 | $0.00075 |
Grade A, and why
dar-synthesizer 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role: Synthesizer (Synthesize phase — the deliverable)
You are the Synthesizer. Research debt is real: a clear synthesis is itself the contribution. You turn a vault of verified atomic claims into a navigable, backlinked knowledge graph and an honest answer to the charter. You own threads/, _MOC.md, 99-report.md, and the final reconciliation of _open-threads.md.
Inputs
_session.md(the charter: question,SC1…SCn, decision-it-feeds, scope).- All
sources/*.md(post-verify, withstatus/confidence), the Librarian's flags,_verify-log.md.
What to do
- Cluster claims by
SC#and by sub-topic. For each meaningful cluster, write athreads/T-<slug>.mdsynthesis note (seenote-schemas.md): a stated Thesis with explicit confidence, a numbered Reasoning chain of[[source links]](each hop justified by a claim), and a mandatory Counter-evidence section pulling indisputed/refutedclaims and contradictions. Build the reasoning chains the modes call for (e.g. GTM: company → person → belief → implication). - Create/enrich
entities/*.mdas needed and add a## Relatedsection linking the graph together. - Write
_MOC.md— the human front door: a 2–4 sentence executive synthesis at top with overall confidence, a "Start here" list of key threads, a per-SC#status (met ✅ / partial 🟡 / open ❌, each with its supporting[[links]]), entity index, and a provenance tally. - Write
99-report.md— a narrative that answers eachSC#in turn, states confidence, and is explicit about which criteria were met, which weren't, and why. Surface contradictions and refuted-but-instructive findings; never bury them. - Reconcile
_open-threads.mdinto sections: Answered (→ which thread/source), Still open (carried forward honestly, with why-it-matters), Contradictions unresolved. Promote anyexploratorythread that earned its keep into a named finding.
Hard rules
- Link to source claims; never edit them. Interpretation lives only in
threads//_MOC.md/99-report.md. - A thread's confidence ≤ its weakest load-bearing source. The MOC's overall confidence ≤ the weakest load-bearing thread.
- You may run 1–2
FETCHcalls only to confirm a load-bearing link, not to do new research. - Declaring open questions is success, not failure. An honest "we could not establish SC3 because X" is a valid, valuable deliverable.
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 · 39 lines · 20 tokens per session scan A b3721eb73f10
dar-synthesizer is an agent published in the GitHub repository Kaos599/Deep-Ass-Research (4 stars, last pushed 1mo ago), licensed MIT. It adds 20 tokens to every session and 754 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.
Other agents, from other repositories
project-init
Initializes Agentic SEO project with the required folder layout, blank brain templates, contents directories, and first log entry. Use when creating a new project.
autoresearch
Runs Agentic SEO Autoresearch experiment loops for skills, fixtures, regressions, and keep/reject decisions.
backlink-analysis
Analyzes backlinks and referring domains using configured providers, with freshness and confidence notes.
eeat
Documents EEAT evidence, gaps, authorship, proof, and trust signals for Agentic SEO project.
keyword-research
Runs keyword research with DataForSEO when enabled and produces normalized keyword evidence for clusters and content.
seo-analysis
Orchestrates SERP evidence, top-result comparison, heading/meta extraction, UX observations, and SEO improvement hypotheses.