llm-wiki-research

llm-wiki-research is a skill for Claude Code from anh-chu/llm-wiki-pm. It costs 42 tokens per session (2,116 once invoked), scanned A, original, MIT.

A research workflow for maintaining a product-management wiki, a collection of linked notes about products, customers, competitors and strategy. It gathers source material through a separate fetching worker and records findings in the wiki.

In plain words
What is it for?
Use it for research sprints, competitive comparisons, customer notes, strategy updates and filling in incomplete wiki pages.
Why use it?
It gives research a repeatable place and process, so competitive findings and customer knowledge do not remain scattered or become hard to update.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions AGENTS.md.

Part of the llm-wiki-pm plugin — 8 skills, 5 agents, 4 hooks shipped together

Good fit Use it for research sprints, competitive comparisons, customer notes, strategy updates and filling in incomplete wiki pages.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/anh-chu/llm-wiki-pm/llm-wiki-research
Install

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.

Any agent
npx skills add anh-chu/llm-wiki-pm --skill llm-wiki-research
Clone the repo
git clone --depth 1 https://github.com/anh-chu/llm-wiki-pm

Made for: Claude Code.

Or install llm-wiki-pm, the plugin that ships this one along with the rest of its 8 skills, 5 agents, 4 hooks.

Wrote 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.

agentmods badge for llm-wiki-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/anh-chu/llm-wiki-pm/llm-wiki-research/github.svg)](https://agentmods.dev/skills/anh-chu/llm-wiki-pm/llm-wiki-research)
Your own site
<a href="https://agentmods.dev/skills/anh-chu/llm-wiki-pm/llm-wiki-research"><img src="https://agentmods.dev/badge/skills/anh-chu/llm-wiki-pm/llm-wiki-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.

agentmods 80×15 button for llm-wiki-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/anh-chu/llm-wiki-pm/llm-wiki-research"><img src="https://agentmods.dev/badge/skills/anh-chu/llm-wiki-pm/llm-wiki-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,116 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00042 $0.02116
Opus 5 $0.00021 $0.01058
Sonnet 5 $0.00008 $0.00423
Haiku 4.5 $0.00004 $0.00212

Measured 11d ago against content hash cef2cc6d13f7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

llm-wiki-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 11d 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.

skills/llm-wiki-research/SKILL.md · 256 lines

How it starts

The opening of the file, as written. The whole thing — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LLM Wiki Research

Sub-skill of llm-wiki-pm. Handles research sprints, competitive deep dives, and auto-enrichment of stub entities. Delegates all URL fetching to worker-source-fetcher so privacy filtering and raw/ logging happen correctly.

WebFetch note: WebFetch is listed in allowed-tools for quick page previews (e.g., confirming a URL before delegation). For source capture (saving to raw/), always delegate to worker-source-fetcher. Never call WebFetch directly for source saving — privacy filtering and raw/ logging won't happen.

Orient First

Orient per AGENTS.md before any writes:

  1. Read $WIKI/SCHEMA.md
  2. Read $WIKI/index.md
  3. Read last 20-30 lines of $WIKI/log.md
  4. Read $WIKI/overview.md

Research sprints may create many pages. Get user confirmation before creating 5+ pages.

Wiki Path Resolution

WIKI=$(cat .wiki-path 2>/dev/null | tr -d '[:space:]')
WIKI=${WIKI:-${CLAUDE_PLUGIN_OPTION_wiki_path:-${WIKI_PATH:-$(pwd)}}}

Worker Delegation

This skill uses worker-source-fetcher for all URL fetching. Never call WebFetch directly for source capture — always invoke the worker:

"Use worker-source-fetcher to fetch [URL]"

This ensures privacy filtering and raw/ logging happen correctly. The worker returns: "OK: saved to raw/<subdir>/<slug>.md" — use that path for synthesis.


Operation 1: Research Sprint

Trigger: "research sprint on [topic]", "deep research on [topic]"

① Define scope

Confirm with user:

  • What is the topic / question to answer?
  • Target depth: surface / standard / deep
  • Time budget (number of sources)

② Wiki-first

grep -r "<topic>" $WIKI --include="*.md" -l

Read all relevant pages. Surface: "Wiki has N relevant pages. Here's what we already know: [...]. Gaps: [...]."

③ Research plan

Present 3-5 specific sources to fetch — name each, why it's relevant, what question it answers. Typical: analyst reports, company pages, recent press, whitepapers, industry forums. Get user confirmation before fetching.

Read the full file on GitHub · 256 lines

Changes

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.

  1. 11d ago First seen · 256 lines · 42 tokens per session scan A cef2cc6d13f7

Subscribe to this mod's changes

llm-wiki-research is a skill published in the GitHub repository anh-chu/llm-wiki-pm (6 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 2,116 once invoked, about $0.0002 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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