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 rvk7895/llm-knowledge-bases --skill research-reportgit clone --depth 1 https://github.com/rvk7895/llm-knowledge-basesWrote 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/rvk7895/llm-knowledge-bases/research-report)<a href="https://agentmods.dev/skills/rvk7895/llm-knowledge-bases/research-report"><img src="https://agentmods.dev/badge/skills/rvk7895/llm-knowledge-bases/research-report/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/rvk7895/llm-knowledge-bases/research-report"><img src="https://agentmods.dev/badge/skills/rvk7895/llm-knowledge-bases/research-report.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 | $0.00019 | $0.01582 |
| Opus 5 | $0.00010 | $0.00791 |
| Sonnet 5 | $0.00004 | $0.00316 |
| Haiku 4.5 | $0.00002 | $0.00158 |
Grade A, and why
research-report 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Attribution: Originally authored by Weizhena. Included with attribution for use in the Deep query workflow.
Research Report - Summary Report
Trigger Method
/research-report
Execution Flow
Step 0: Load Report Preferences
Read the vault's kb.yaml and extract the report_preferences: block. These are free-text prose instructions written by the user at init (kb-init §5.5) or via /kb-preferences. They control audience, register, depth, code handling, diagrams, self-containment, citations, and argument iteration for this report.
If the block is present: apply every field as an instruction to the prose you write in subsequent steps. Treat the field text literally — it is the working rule, not a keyword list. The Python generator script written in Step 3 should also respect these preferences (e.g., if depth asks for multi-paragraph per-item walkthroughs, emit the item template with multi-paragraph slots; if diagrams says ASCII-only, don't emit mermaid).
If the block is missing: fall back to factory defaults in plugins/kb/references/report-style-guide.md silently and include this line in the final report output:
No
report_preferencesset inkb.yaml— using factory defaults. Run/kb-preferences initto customize.
If kb.yaml itself is missing: tell the user to run kb-init and stop.
Per-task overrides. If the user's current request explicitly contradicts a stored preference ("make this one short", "skip the diagrams for this report"), follow the request for this report only. Do NOT modify kb.yaml — that's what the reflection step is for.
Step 1: Locate Results Directory
Find */outline.yaml in current working directory, read topic and output_dir configuration.
Step 2: Scan Optional Summary Fields
Read all JSON results and extract fields suitable for display in the table of contents (numeric, short indicators), such as:
- github_stars
- google_scholar_cites
- swe_bench_score
- user_scale
- valuation
- release_date
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 · 125 lines · 19 tokens per session scan A fa10e1a172d0
research-report is a skill published in the GitHub repository rvk7895/llm-knowledge-bases (37 stars, last pushed 2mo ago), licensed MIT. It adds 19 tokens to every session and 1,582 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 2026-08-30.
Other skills, from other repositories
link-memory
Use after important user-approved decisions, when durable context should be proposed or reviewed, and for explicit Link memory lifecycle work: remember, recall, review, update, archive, restore, forget, or explain local memories through the CLI without requiring MCP.
link-retrieve
Use before answering work that may depend on user memory, project history, source-backed notes, or prior decisions; retrieve compact Link context through the CLI without loading the whole wiki or requiring MCP.
link-health
Use at the start of Link work when readiness is unclear, after installs or upgrades, and before repairs; verify health, inspect interrupted writes, back up, and repair generated indexes without MCP.
link-ingest
Use when raw files are present, source pages look stale, or a user asks to ingest notes into Link; refresh source-backed wiki pages, propose memories, and validate updates through the CLI without MCP.
superbrain-distill
Internal SuperBrain skill — run by the detached capture child to distill a session-event delta into routed Obsidian notes. Not for direct user invocation.
superbrain-recall
Search the user's SuperBrain second-brain vault. Use whenever the user references past work, prior decisions, "how did we", "did we already", earlier sessions, a project's history, or anything that may already be recorded — before answering from scratch.