Agent skill

Detect Tensions

by Abilityai in Abilityai/cornelius

Detect productive contradictions between notes - high semantic similarity with opposing conclusions that represent synthesis opportunities

MITAuto-check: notes

Install Detect Tensions

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

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

GitHub CLI
$ gh skill install Abilityai/cornelius detect-tensions --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/detect-tensions .claude/skills/detect-tensions && 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
detect-tensions
GitHub stars
109
Token cost
~1.1k tokens
SKILL.md length
416 words
Files
1
Skills in repo
51
Repo updated
First seen
Licence
MIT

At a glance

Detect productive contradictions between notes - high semantic similarity with opposing conclusions that represent synthesis opportunities

  • Works in 4 steps: Run tension detection → Filter false positives, then present… → Track existing tensions → …
  • SKILL.md covers State Dependencies, Process and Key Principle
  • Calls python

What it does

Detect Tensions is an agent skill from Abilityai/cornelius. Detect productive contradictions between notes - high semantic similarity with opposing conclusions that represent synthesis opportunities

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: AI-powered second brain template for Claude Code + Obsidian. The licence is MIT.

Example prompts

  • “/detect-tensions”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Bash, Read

Workflow steps

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

  1. Run tension detection
  2. Filter false positives, then present synthesis opportunities
  3. Track existing tensions
  4. Probe for cross-vocabulary tensions the detector cannot see

What it can do on your machine

Read from SKILL.md and the folder at commit fd5e9a4. 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

    Shell commands in SKILL.md call:

    • python

    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

Detect Tensions loads about 1.1k tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 416 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~39
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

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 fd5e9a4, republished under its MIT licence (© Abilityai). 416 words, ~1,080 tokens.

Download SKILL.mdSave it as .claude/skills/detect-tensions/SKILL.md (or your agent's skills folder).
name
detect-tensions
description
Detect productive contradictions between notes - high semantic similarity with opposing conclusions that represent synthesis opportunities
allowed-tools
Bash, Read
user-invocable
true
automation
gated
metadata.version
1.1
metadata.updated
2026-06-29
metadata.changelog
1.1: Document detector blind spots - filter the false-positive flood (boilerplate/near-duplicate pairs) and probe manually for cross-vocabulary tensions the…

Detect Productive Tensions

ℹ️ First, set expectations: before anything else, print one short line with this skill's version and its most recent change - the top entry of metadata.changelog above - e.g. detect-tensions vX.Y - recent: <summary>. Then proceed.

Scans the knowledge base for productive contradictions: note pairs with high semantic similarity but opposing conclusions. These tension zones are where the most valuable articles and frameworks emerge.

State Dependencies

SourceLocationReadWriteDescription
Enrichmentsresources/brain-graph/data/graph_enrichments.json✓✓Tension records saved
FAISS Indexresources/local-brain-search/data/brain.faiss✓Similarity search
Metadataresources/local-brain-search/data/brain_metadata.pkl✓Note content

Process

Step 1: Run tension detection

Default thresholds (similarity > 0.75, divergence > 0.3):

bash
cd $PROJECT_ROOT/resources/brain-graph
../local-brain-search/venv/bin/python cli.py tensions

Broader search (more results, lower quality):

bash
../local-brain-search/venv/bin/python cli.py tensions --similarity 0.70 --divergence 0.2
Step 2: Filter false positives, then present synthesis opportunities

The raw count is dominated by false positives - discard them before presenting:

  • Boilerplate / near-duplicate pairs. The signature is high similarity with maximal divergence (e.g. sim ≈ 1.00, divergence ≈ 1.00), and pairs where both notes are changelogs, registries, or near-identical restatements of one principle. These are detector artifacts, not contradictions.

Keep only pairs that assert genuinely opposing conclusions about the same question. For each surviving tension, explain:

  • What the two notes assert
  • Why they contradict
  • What synthesis opportunity exists (article topic, framework potential)
Step 3: Track existing tensions
bash
../local-brain-search/venv/bin/python cli.py status --json

Check tension_count for total tracked tensions.

Show full SKILL.md (212 more words)Show less
Step 4: Probe for cross-vocabulary tensions the detector cannot see

The detector pairs notes by cosine similarity (floor ~0.70) and scores opposition with a keyword heuristic (negation vs. assertion words). It is therefore structurally blind to the most valuable tensions: genuine contradictions are usually cross-vocabulary - two frameworks reaching opposite conclusions in different language - which fall BELOW the similarity floor and read too assertively for the keyword check. A thin or empty result does NOT mean no tensions exist; this tool surfaces candidates, it does not certify absence.

Compensate by manually checking known opposing-framework pairs even when they score below threshold, e.g.:

  • loss aversion (prospect theory) ↔ ergodicity / Kelly (bias vs. correct policy)
  • Bayesian/Brier "assign a probability" ↔ "There Is No Bayesian Dial" / radical uncertainty
  • heuristics-and-biases ↔ ecological / evolutionary rationality
  • expert failure as psychological ↔ expert failure as structural

Treat these as candidate tension edges regardless of the detector's similarity score.

Root-cause fix (code, out of scope for this playbook): durable precision needs resources/brain-graph/tension.py to replace the keyword stance heuristic with an LLM stance-classifier on a shared proposition, lower the similarity floor with theme/MOC-anchored cross-cluster pairing, and exclude index/changelog-layer nodes from the scan.

Key Principle

Tensions are features, not bugs. The system NEVER auto-resolves tensions. It surfaces them as the most productive intellectual territory in the vault.

© 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/detect-tensions of Abilityai/cornelius.

Open the folder on GitHubat commit fd5e9a4

Compare with similar skills

Detect Tensions 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.

Detect Tensions compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Detect Tensions this skillAbilityai/cornelius109—~1.1kAutomated safety check: NotesMIT
Product Lensaffaan-m/ECC275k2 repos~841Automated safety check: PassMIT
Productthedaviddias/Front-End-Checklist74k—~592Automated safety check: PassMIT
Product Analyticsalirezarezvani/claude-skills28k1 repos~1.4kAutomated safety check: PassMIT
Product Strategyphuryn/pm-skills27k—~1.2kAutomated safety check: PassMIT
Product Design Framinglobehub/lobehub83k—~2.6kAutomated safety check: PassCustom licence

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Questions about Detect Tensions

What does Detect Tensions do?

Detect productive contradictions between notes - high semantic similarity with opposing conclusions that represent synthesis opportunities. Detect Tensions is an agent skill from Abilityai/cornelius.

How do I install Detect Tensions in Claude Code?

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

How do I install Detect Tensions in Codex?

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

Can I use Detect Tensions 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 detect-tensions -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/detect-tensions, .gemini/skills/detect-tensions, .github/skills/detect-tensions and .opencode/skills/detect-tensions in your project.

What does Detect Tensions need to run?

Going by SKILL.md and its folder, Detect Tensions needs the command-line tools its instructions call (python). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash, Read.

Does Detect Tensions 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 Detect Tensions 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 Detect Tensions use?

Detect Tensions 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 Detect Tensions use?

About 1.1k tokens (SKILL.md is roughly 4.3k 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 Detect Tensions?

Skills that share tags, products or a category with Detect Tensions: Product Lens (affaan-m/ECC, 275k stars), Product (thedaviddias/Front-End-Checklist, 74k stars), Product Analytics (alirezarezvani/claude-skills, 28k stars) and Product Strategy (phuryn/pm-skills, 27k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Detect Tensions?

Abilityai (a GitHub organization) maintains it in Abilityai/cornelius, which has 109 GitHub stars. The repository holds 51 skills in this directory. The repository was last updated on September 22, 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.