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Agent skill

Advise

by Abilityai in Abilityai/cornelius

Solve problems using knowledge base insights - extracts search terms, runs parallel KB queries, synthesizes advice grounded in your own frameworks

MITAuto-check: notesKnowledge Management

Install Advise

skills CLI
$ npx skills add Abilityai/cornelius --skill advise -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Abilityai/cornelius advise --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Abilityai/cornelius.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/advise .claude/skills/advise && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
advise
GitHub stars
109
Token cost
~1.6k tokens
SKILL.md length
580 words
Files
1
Skills in repo
54
Repo updated
First seen
Licence
MIT

At a glance

Solve problems using knowledge base insights - extracts search terms, runs parallel KB queries, synthesizes advice grounded in your own frameworks

  • Works in 6 steps: Extract Search Terms (no tool calls -… → Parallel Knowledge Retrieval → Read Top Insights → …
  • Tasks that involve Knowledge bases
  • SKILL.md covers Purpose, Problem, Process and Output Format, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Advise is an agent skill from Abilityai/cornelius. Solve problems using knowledge base insights - extracts search terms, runs parallel KB queries, synthesizes advice grounded in your own frameworks

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Knowledge Management, covering Knowledge bases. The repository describes itself as: AI-powered second brain template for Claude Code + Obsidian. The licence is MIT.

When your agent uses it

  • Tasks that involve Knowledge bases

Example prompts

  • “/advise”

Requirements

  • Pre-approved tools (allowed-tools): Bash, Read

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Extract Search Terms (no tool calls - just reasoning)
  2. Parallel Knowledge Retrieval
  3. Read Top Insights
  4. 5: Check BDG Context (optional, if top results are frameworks)
  5. Synthesize Advice
  6. Reasoning Checks (required)

What it can do on your machine

Read from SKILL.md and the folder at commit b9bea90. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Read

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Advise loads about 1.6k tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 580 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~38
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Read

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Abilityai/cornelius at commit b9bea90, republished under its MIT licence (© Abilityai). 580 words, ~1,641 tokens.

Download SKILL.mdSave it as .claude/skills/advise/SKILL.md (or your agent's skills folder).
name
advise
description
Solve problems using knowledge base insights - extracts search terms, runs parallel KB queries, synthesizes advice grounded in your own frameworks
allowed-tools
Bash, Read
automation
autonomous
argument-hint
<describe your problem or question in natural language>
user-invocable
true
metadata.version
1.2
metadata.updated
2026-07-31
metadata.author
Ability.ai
metadata.changelog
1.2: Route structured decisions to the new /decide sibling (rule-matched choice vs framework map), 1.1: Wire in the reasoning-checks contract (Step 5)…

Advise

Help solve problems by grounding advice in your accumulated knowledge and frameworks.

Purpose

Turn natural language problems into KB-grounded advice. Fast path: no subagents, no changelogs, no multi-layer expansion.

Routing: if the problem is a structured decision between courses of action ("X or Y?", go/no-go, "is it worth") → use /decide instead: it applies an explicit decision rule (ergodic filter, EV, robustness, value-of-information) and delivers tripwires, not just frameworks. /advise is for framing, understanding, and open problems.

Problem

$ARGUMENTS

Process

Step 1: Extract Search Terms (no tool calls - just reasoning)

From the problem description, identify 3-4 keyword clusters that would match relevant KB content:

  • Core concepts (what domain is this?)
  • Related frameworks (what mental models apply?)
  • Analogous patterns (what similar problems exist?)

Example:

  • Problem: "Should I focus on fundraising or product development?"
  • Search terms: decision making tradeoffs, explore exploit, focus prioritization, opportunity cost
Step 2: Parallel Knowledge Retrieval

Read role: reasoning (contract: scope-mount - never copy its tables, never run a bare search). Two passes per term in the same batch: core is the spine (the user's thinking), the reasoning mount is the evidence layer (what he has read). Before the batch, run the scope-mount trigger check on the problem text; a hit appends ,company / ,thinkers / ,Books/<slug> to the wide pass for this run only. Direction questions about the org are not yours - route to /canon-advise.

Run the searches in parallel (single message, multiple Bash calls):

bash
BRAIN_READ_SCOPE=core                          resources/local-brain-search/run_search.sh "search term 1" --limit 3 --json
BRAIN_READ_SCOPE=core,Books,document-insights  resources/local-brain-search/run_search.sh "search term 1" --limit 5 --json
BRAIN_READ_SCOPE=core                          resources/local-brain-search/run_search.sh "search term 2" --limit 3 --json
BRAIN_READ_SCOPE=core,Books,document-insights  resources/local-brain-search/run_search.sh "search term 2" --limit 5 --json
BRAIN_READ_SCOPE=core                          resources/local-brain-search/run_search.sh "search term 3" --limit 3 --json
BRAIN_READ_SCOPE=core,Books,document-insights  resources/local-brain-search/run_search.sh "search term 3" --limit 5 --json
resources/local-brain-search/run_connections.sh --hubs --json   # fingerprint - always core; for Step 5's attractor check, same batch
Step 3: Read Top Insights

From the search results, read 2-3 of the most relevant note files in parallel - when both passes returned, read at least one from each. A Books/ or Document Insights/ note is encountered material: cite it as what the user has read, never as his view (Check 3 in Step 5 enforces this):

bash
# Use Read tool on the top-scoring, most relevant files
Step 3.5: Check BDG Context (optional, if top results are frameworks)

For any top result that looks like a framework or key insight, check its BDG context:

bash
resources/brain-graph/run_brain_graph.sh inspect "Top Result Name" --json

This reveals: lifecycle phase (is it generative?), staleness (is it still fresh?), and typed edges (what does it drive?). Prioritize generative frameworks over reflective notes. Warn if citing a stale note.

Show full SKILL.md (230 more words)Show less
Step 4: Synthesize Advice

Combine the retrieved insights to address the original problem:

  • Apply frameworks from the notes to the specific situation
  • Cite specific notes: [[Note Title]]
  • Highlight tensions or tradeoffs the KB reveals
  • Give concrete recommendations grounded in your own thinking
  • Prioritize generative notes (lifecycle > 0.6) - these are the user's strongest frameworks
Step 5: Reasoning Checks (required)

Apply the shared contract in .claude/skills/reasoning-checks/SKILL.md before finalizing:

  • Epistemic Inversion (always) - specific falsifier required; a generic hedge means redo it
  • Attractor Check (always) - against the hubs fetched in Step 2; ≥2 top-10 hubs load-bearing → generate one non-attractor framing
  • Provenance Base (always) - tally from the frontmatter of the notes read in Step 3; unendorsed synthesis must be labelled as such
  • Reference Class (only if the advice hinges on a forecast, magnitude, or probability)

Append the resulting blocks after the Bottom line.

Output Format

markdown
## [Problem summary - one line]

**Relevant frameworks from your KB:**
- [[Note 1]] - [how it applies]
- [[Note 2]] - [how it applies]
- [[Note 3]] - [how it applies]

**My take (grounded in your insights):**

[2-4 paragraphs synthesizing advice, citing notes, applying frameworks to the specific problem]

**Key tradeoffs to consider:**
- [Tradeoff 1]
- [Tradeoff 2]

**Bottom line:** [One clear recommendation or framing]

[reasoning-checks blocks: Epistemic Inversion · Attractor Check · Provenance Base · Reference Class (when quantitative)]

Rules

  • NO subagent spawning - all work happens inline
  • NO changelog creation - this is conversational, not archival
  • NO spreading activation - use static search for speed
  • Parallel execution - run all searches in one message, all reads in the next
  • Maximum 2 rounds of tool calls - searches + hubs (parallel) + reads (parallel); the reasoning checks reuse those results, no extra round
  • Cite your sources - always reference the specific notes used
  • Be actionable - don't just dump knowledge, apply it to the problem
  • If KB lacks relevant content, say so honestly and offer general reasoning instead

© Abilityai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/advise of Abilityai/cornelius.

Open the folder on GitHubat commit b9bea90

Compare with similar skills

Advise next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Advise compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Advise this skillAbilityai/cornelius109—~1.6kAutomated safety check: NotesMIT
Capture Conversationoutline/outline41k—~474Automated safety check: PassCustom licence
Find And Citeoutline/outline41k—~537Automated safety check: PassCustom licence
Xhs Virtual Productchenjin-cmd/xhs-virtual-product726—~862Automated safety check: PassMIT
OpenkbVectifyAI/OpenKB4.8k1 repos~2kAutomated safety check: WarnApache-2.0
Learn From Materialsdmoshehun-prog/learn-from-materials937—~7.9kAutomated safety check: PassMIT

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Categories

Questions about Advise

What does Advise do?

Solve problems using knowledge base insights - extracts search terms, runs parallel KB queries, synthesizes advice grounded in your own frameworks. Advise is an agent skill from Abilityai/cornelius.

When should I use Advise?

Advise fits situations like: tasks that involve Knowledge bases.

How do I install Advise in Claude Code?

Run `npx skills add Abilityai/cornelius --skill advise -a claude-code`. Or copy the skill folder (.claude/skills/advise in Abilityai/cornelius) into .claude/skills/advise in your project. Claude Code loads it when a task matches its description.

How do I install Advise in Codex?

Run `npx skills add Abilityai/cornelius --skill advise -a codex`. Or copy the skill folder (.claude/skills/advise in Abilityai/cornelius) into .agents/skills/advise in your project. Codex loads it when a task matches its description.

Can I use Advise in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Abilityai/cornelius --skill advise -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/advise, .gemini/skills/advise, .github/skills/advise and .opencode/skills/advise in your project.

What does Advise need to run?

SKILL.md names no scripts, command-line tools or credentials: Advise is instructions for the agent only. Its frontmatter pre-approves these tools: Bash, Read.

Does Advise access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Advise safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Advise use?

Advise is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Advise use?

About 1.6k tokens (SKILL.md is roughly 6.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Advise?

Skills that share tags, products or a category with Advise: Capture Conversation (outline/outline, 41k stars), Find And Cite (outline/outline, 41k stars), Xhs Virtual Product (chenjin-cmd/xhs-virtual-product, 726 stars) and Openkb (VectifyAI/OpenKB, 4.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Advise?

Abilityai (a GitHub organization) maintains it in Abilityai/cornelius, which has 109 GitHub stars. The repository holds 54 skills in this directory. The repository was last updated on October 8, 2026.

Source: Abilityai/cornelius on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.