Agent skill

Manage Thinking Topics

by Abilityai in Abilityai/cornelius

Manage the incubation loop topic lifecycle — seed new questions, review status, crystallize converged conclusions, and retire stale topics

MITAuto-check: notes

Install Manage Thinking Topics

skills CLI
$ npx skills add Abilityai/cornelius --skill manage-thinking-topics -a claude-code

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

GitHub CLI
$ gh skill install Abilityai/cornelius manage-thinking-topics --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/manage-thinking-topics .claude/skills/manage-thinking-topics && 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
manage-thinking-topics
GitHub stars
109
Token cost
~3.9k tokens
SKILL.md length
1,591 words
Files
1
Skills in repo
54
Repo updated
First seen
Licence
MIT

At a glance

Manage the incubation loop topic lifecycle — seed new questions, review status, crystallize converged conclusions, and retire stale topics

  • Works in 12 steps: Read Registry → For Each Active Topic - Load Summary → For Each Converged Topic - Load Summary → …
  • SKILL.md covers State Dependencies, Modes, Mode: review and Mode: seed, plus 3 more sections
  • Calls git and python3

What it does

Manage Thinking Topics is an agent skill from Abilityai/cornelius. Manage the incubation loop topic lifecycle — seed new questions, review status, crystallize converged conclusions, and retire stale topics

Its SKILL.md is about 3.9k 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

  • “/manage-thinking-topics”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash

Workflow steps

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

  1. Read Registry
  2. For Each Active Topic - Load Summary
  3. For Each Converged Topic - Load Summary
  4. Present Status Report
  5. Gather Requirements
  6. Validate Slug
  7. Create Topic File
  8. Add to Registry
  9. Confirm
  10. Load Converged Topic
  11. Search for Existing Notes
  12. Choose Destination

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:

    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git
    • python3

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

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    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

Manage Thinking Topics loads about 3.9k tokens when it runs. Until then it costs about 40 tokens; SKILL.md has 1,591 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~40
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k

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: Read, Write, Edit, Grep, Glob, Bash

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). 1,591 words, ~3,883 tokens.

Download SKILL.mdSave it as .claude/skills/manage-thinking-topics/SKILL.md (or your agent's skills folder).
name
manage-thinking-topics
description
Manage the incubation loop topic lifecycle — seed new questions, review status, crystallize converged conclusions, and retire stale topics
allowed-tools
Read, Write, Edit, Grep, Glob, Bash
automation
manual
user-invocable
true
argument-hint
[seed|review|crystallize|retire] [topic-slug]

Manage Thinking Topics

Human-in-the-loop manager for the incubation loop topic registry. Handles the full lifecycle: seeding new questions into analysis, reviewing current status, graduating converged conclusions to permanent notes, and retiring topics no longer worth pursuing.

Companion skill: .claude/skills/incubation-loop/SKILL.md — the analytical engine that processes active topics.

State Dependencies

SourceLocationReadWriteDescription
Thinking RegistryBrain/05-Meta/Thinking/THINKING-REGISTRY.md✓✓Master list of all topics + status
Topic FilesBrain/05-Meta/Thinking/[topic-slug].md✓✓Per-topic reasoning journal
Permanent NotesBrain/02-Permanent/✓Atomic crystallized conclusions (graph-integrated, future-reasoning substrate)
Synthesis ReportsBrain/05-Meta/Reports/✓Terminal crystallized conclusions (decision-input snapshots, excluded from LBS index)
AI Extracted NotesBrain/AI Extracted Notes/✓Alternative destination for crystallized notes

Modes

Invoke with one of four modes. If no mode is provided, default to review.


Mode: review

Purpose: Show the full topic landscape at a glance - what's active, what needs crystallization, what's done.

Step 1: Read Registry
bash
date '+%Y-%m-%d'

Read Brain/05-Meta/Thinking/THINKING-REGISTRY.md.

Step 2: For Each Active Topic - Load Summary

For each topic with status: active, read its thinking file. Extract:

  • run_count - how many iterations completed
  • last_run - date of last iteration
  • Current leading hypothesis + confidence score
  • Next move in rotation (run_count mod 6)
  • Any open questions flagged
Step 3: For Each Converged Topic - Load Summary

For each topic with status: converged, read its thinking file. Extract:

  • The converged conclusion (Current Best Answer from last run)
  • Confidence score at convergence
  • Run count at convergence
Step 4: Present Status Report

Output a structured report:

## Thinking Topics: Status Review [DATE]

### Active Topics (N)
| Topic | Runs | Last Run | Leading Hypothesis | Confidence | Next Move |
|-------|------|----------|-------------------|------------|-----------|

### Converged — Ready for Crystallization (N)
| Topic | Runs | Conclusion Summary | Confidence |
|-------|------|-------------------|------------|

### Crystallized — Done (N)
| Topic | Permanent Note | Crystallized |
|-------|---------------|-------------|

### Summary
- [N] topics in active analysis
- [N] topics ready for crystallization (action needed)
- [N] topics fully crystallized
- Next incubation-loop run: processes all active topics

If there are converged topics, highlight: "[N] topics ready for crystallization — run /manage-thinking-topics crystallize [slug] to graduate each one."


Mode: seed

Purpose: Add a new question to the incubation loop.

Argument: topic slug (e.g., agent-memory-architecture)

Step 1: Gather Requirements

If the topic slug is not provided, ask for it. Then gather:

  1. Central question - The exact question being analyzed. Precise framing matters - vague questions stay vague.
  2. Why it matters - 1-2 sentences on stakes or relevance.
  3. Initial hypotheses - At least 2 competing hypotheses with rough initial confidence percentages (must sum to ~100%).
  4. Known evidence - Any notes or sources already known to be relevant (optional).
  5. Constraints/assumptions - What's taken as given? What's out of scope? (optional)
Step 2: Validate Slug

Check that Brain/05-Meta/Thinking/[topic-slug].md does NOT already exist. If it does, warn and stop.

Check that the slug is not already in the registry. If it is, warn and stop.

Step 3: Create Topic File
bash
date '+%Y-%m-%d'

Create Brain/05-Meta/Thinking/[topic-slug].md:

markdown
---
created: YYYY-MM-DD
updated: YYYY-MM-DD
created_by: claude-sonnet-4-6
updated_by: claude-sonnet-4-6
agent_version: 01.25
topic: "[exact question being analyzed]"
run_count: 0
last_run: null
status: active
---

# Thinking: [Topic Title]

## Central Question
[The exact question. Precise framing matters - ambiguous questions stay ambiguous.]

## Why This Question Matters
[1-2 sentences on stakes or relevance]

## Initial Hypotheses
| Hypothesis | Initial Confidence | Basis |
|-----------|-------------------|-------|
| H1: [statement] | X% | [prior knowledge or intuition] |
| H2: [statement] | X% | [prior knowledge or intuition] |

## Known Evidence (Pre-Run)
[Any notes or sources already known to be relevant, or "None identified yet."]

## Constraints and Assumptions
[What are you taking as given? What's out of scope?]

---
*Analytical runs will be appended below by the incubation loop*
Step 4: Add to Registry

Read Brain/05-Meta/Thinking/THINKING-REGISTRY.md.

Add a row to the Active Topics table:

| [topic-slug] | [central question] | active | 0 | null |

Update updated and updated_by in frontmatter.

Step 5: Confirm

Report:

  • Topic file created at Brain/05-Meta/Thinking/[topic-slug].md
  • Registry updated - topic is now active
  • First incubation-loop run will apply: ACH Audit (move 0)
  • Estimated convergence: 4-6 runs (~4-6 days at daily schedule)

Mode: crystallize

Purpose: Graduate a converged topic — either as an atomic permanent note (Brain/02-Permanent/, graph-integrated) or as a terminal synthesis report (Brain/05-Meta/Reports/, excluded from semantic indexing). Choose based on whether the conclusion is generative (future reasoning will reuse it) or terminal (snapshot of what was concluded).

Argument: topic slug of a converged topic

If no slug is provided: read the registry, present the converged topics (with one-line conclusions), and ask which to crystallize before proceeding. If prior session context points at a specific topic or pair (e.g., a just-completed analysis that named them), recommend it as the first option. Accept common misspellings of the mode name (e.g., "crystalize").

Step 1: Load Converged Topic

Read Brain/05-Meta/Thinking/[topic-slug].md.

Verify status: converged. If not converged, warn and stop.

Extraction strategy (prefer the canonical block, fall back gracefully):

  1. First, look for ## Final Synthesis - a canonical block appended when the loop transitions a topic to converged. If present, this is the authoritative source. Extract from it:

    • Conclusion (the substantive answer)
    • Confidence and leading hypothesis
    • Key evidence (already curated)
    • Remaining uncertainty
    • Related topics (if any)
  2. If no Final Synthesis block exists (legacy topics from before the standardized format), read only the last 200 lines of the file rather than the full thinking journal (some are 5,000+ lines). Extract:

    • The central question (from frontmatter)
    • The most recent "Current Best Answer" section
    • The most recent "Updated Hypotheses" table for confidence scores
    • Any "Open Questions" flagged in the final run
  3. Read the full file only if the last 200 lines do not contain a "Current Best Answer" or equivalent conclusion.

Step 2: Search for Existing Notes
bash
python3 resources/local-brain-search/search.py "[central question keywords]" --limit 5 --mode spreading 2>/dev/null

Check if a permanent note already exists covering this conclusion. If so, note the overlap and ask whether to update it or create a new note.

Step 3: Choose Destination

Before drafting, decide the destination based on the nature of the conclusion:

SignalDestination
Conclusion is one atomic insight that future reasoning will citeBrain/02-Permanent/ (permanent note)
Conclusion has multiple co-equal findings that belong together (e.g., a four-stage architecture, a stratified framework)Brain/05-Meta/Reports/ (synthesis report)
Conclusion is a snapshot of a moment (geopolitical assessment, project-stack analysis) — decision-input, not future-reasoning substrateBrain/05-Meta/Reports/ (synthesis report)
Conclusion captures a generative principle that will be reasoned FROM in other contextsBrain/02-Permanent/ (permanent note)
Topic is transitory — relevant for current decisions, unlikely to be revisitedBrain/05-Meta/Reports/ (synthesis report)

[APPROVAL GATE] Propose the destination and ask the user to confirm or override:

"Recommend: [destination] because [reason]. Confirm, or override to [other destination]?"

Synthesis reports do not participate in connection-finder or semantic search (the Reports/ folder is excluded from LBS indexing). They are visible in Obsidian and can link OUT to permanent notes via [[wiki-links]], but permanent notes should rarely link IN to reports.

Show full SKILL.md (695 more words)Show less
Step 4: Draft Content

Adapt the draft to the chosen destination:

If permanent note (Brain/02-Permanent/):

  • Written in the user's voice, not as a report
  • Leads with the conclusion, not the process
  • Cites the key evidence that drove the conclusion
  • Notes remaining uncertainty or caveats
  • Atomic — one main insight, ~50-100 lines

If synthesis report (Brain/05-Meta/Reports/):

  • Long-form is appropriate — can be 200-600+ lines
  • Lead with executive summary, then layered detail
  • Multiple findings organized by section (each finding need not be atomic)
  • Cite full evidence base with sources/URLs where applicable
  • Wiki-link OUT to relevant permanent notes; reports are terminal

Propose the draft to the user for review before writing.

[APPROVAL GATE] Present the draft. Ask: "Write this to [chosen destination path]? (Yes / revise the draft / skip crystallization)"

Step 5: Write to Destination

On approval, write the file with proper frontmatter:

yaml
---
created: YYYY-MM-DD
updated: YYYY-MM-DD
created_by: claude-sonnet-4-6
updated_by: claude-sonnet-4-6
agent_version: 01.25
provenance: ai-inferred
---

Provenance (mandatory): Crystallized conclusions are agent-synthesized, so default provenance: ai-inferred. Per CLAUDE.md's guarded boundary, this only becomes endorsed or originated through an explicit endorsement act by the user at the [APPROVAL GATE] above - never set it automatically. If the user states the conclusion is his own thinking, set provenance: endorsed (or originated if it is purely his idea the loop merely formalized).

For synthesis reports, kebab-case the filename; add a date suffix if the report is a dated snapshot (e.g., trinity-production-architecture-2026-05-26.md).

Step 6: Update Topic File

Edit Brain/05-Meta/Thinking/[topic-slug].md:

  • Set status: crystallized
  • Update updated and updated_by
  • Append a final note (adapt to destination):
markdown
---

**CRYSTALLIZED [DATE]** → [[Destination Note or Report Name]]

*Conclusion graduated to [permanent knowledge base | synthesis reports]. Thinking file archived.*
Step 7: Update Registry

In Brain/05-Meta/Thinking/THINKING-REGISTRY.md:

  • Remove the row from Converged table
  • Add a row to Crystallized table:
| [topic-slug] | [[Destination Note or Report Name]] | YYYY-MM-DD |

If the existing Crystallized table column header is still "Permanent Note", update it to "Destination" so it accommodates both atomic notes and synthesis reports.

Update updated and updated_by in frontmatter.

Step 8: Confirm

Report:

  • File written at [full destination path]
  • Topic marked crystallized
  • Registry updated

Mode: retire

Purpose: Remove a topic from active analysis without crystallizing it (topic no longer relevant, question was wrong, superseded by new evidence).

Argument: topic slug

Step 1: Load Topic

Read Brain/05-Meta/Thinking/[topic-slug].md. Show:

  • Current status
  • Run count
  • Current leading hypothesis and confidence
Step 2: Confirm Retirement

[APPROVAL GATE] Ask:

"Retiring '[topic-slug]' will stop it from being processed by the incubation loop. The thinking file will be kept for reference but marked retired. Reason for retiring? (or type 'cancel')"

Capture the reason.

Step 3: Update Topic File

Edit Brain/05-Meta/Thinking/[topic-slug].md:

  • Set status: retired
  • Update updated and updated_by
  • Append:
markdown
---

**RETIRED [DATE]**

*Reason: [reason given]*

*Topic removed from active analysis. Thinking file kept for reference.*
Step 4: Update Registry

In Brain/05-Meta/Thinking/THINKING-REGISTRY.md:

  • Remove the row from whichever table it was in (Active or Converged)
  • Do NOT add to Crystallized table (it wasn't graduated)

Optionally add a Retired section if one doesn't exist:

markdown
## Retired

| Topic Slug | Reason | Retired On |
|------------|--------|------------|

Update updated and updated_by in frontmatter.

Step 5: Confirm

Report:

  • Topic marked retired in Brain/05-Meta/Thinking/[topic-slug].md
  • Removed from registry active/converged table
  • Thinking file preserved at full path for future reference

Move Rotation Reference

When seeding or reviewing, the next analytical move is determined by run_count mod 6:

run_count % 6Move
0ACH Audit — eliminate weak hypotheses
1Bayesian Update — explicit confidence tracking
2Steelman Opposition — dialectical challenge
3Cross-Domain Bridge — consilience from another field
4Implication Check — falsificationism
5Assumption Audit — find load-bearing shaky premises

Convergence Criteria (Reference)

A topic converges when ALL:

  • run_count >= 4
  • Same hypothesis has led for 3+ consecutive runs
  • Confidence delta between last two runs < 5 percentage points
  • No major open questions flagged in last run

The incubation-loop skill checks this automatically after each run.


Error Handling

ErrorRecovery
Registry missingStop; inform user to seed registry via incubation-loop skill
Topic file missing for registry entryLog warning; continue with other topics
Slug already exists (seed mode)Stop; show existing file path
Topic not converged (crystallize mode)Stop with current run count and status
KB search returns emptySkip search step; note in output

Self-Improvement

After completing this skill's primary task, consider tactical improvements:

  • Review execution: Were there friction points, unclear steps, or inefficiencies?
  • Identify improvements: Could step ordering, prompts to the user, or output formatting be clearer?
  • Scope check: Only execution changes — NOT changes to the incubation-loop analytical framework itself
  • Apply improvement (if identified):
    • Edit this SKILL.md with the specific improvement
    • Keep changes minimal and focused
  • Version control:
    • git add .claude/skills/manage-thinking-topics/SKILL.md
    • git commit -m "refactor(manage-thinking-topics): <brief improvement>"

© 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/manage-thinking-topics of Abilityai/cornelius.

Open the folder on GitHubat commit b9bea90

Compare with similar skills

Manage Thinking Topics 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.

Manage Thinking Topics compared with similar skills
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Cheat Seed Topic ConversationXBuilderLAB/cheat-on-content7.2k—~3.5kAutomated safety check: NotesMIT
Scientific Thinking Literature Reviewaffaan-m/ECC276k1 repos~1.3kAutomated safety check: PassMIT
Think Tankdavila7/claude-code-templates32k—~3kAutomated safety check: PassMIT
Seedagenticnotetaking/arscontexta3.5k—~2.4kAutomated safety check: NotesMIT

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Questions about Manage Thinking Topics

What does Manage Thinking Topics do?

Manage the incubation loop topic lifecycle — seed new questions, review status, crystallize converged conclusions, and retire stale topics. Manage Thinking Topics is an agent skill from Abilityai/cornelius.

How do I install Manage Thinking Topics in Claude Code?

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

How do I install Manage Thinking Topics in Codex?

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

Can I use Manage Thinking Topics 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 manage-thinking-topics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/manage-thinking-topics, .gemini/skills/manage-thinking-topics, .github/skills/manage-thinking-topics and .opencode/skills/manage-thinking-topics in your project.

What does Manage Thinking Topics need to run?

Going by SKILL.md and its folder, Manage Thinking Topics needs the command-line tools its instructions call (git and python3). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash.

Does Manage Thinking Topics access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Manage Thinking Topics 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 Manage Thinking Topics use?

Manage Thinking Topics 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 Manage Thinking Topics use?

About 3.9k tokens (SKILL.md is roughly 16k 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 Manage Thinking Topics?

Skills that share tags, products or a category with Manage Thinking Topics: Seed (Q00/ouroboros, 6.2k stars), Cheat Seed Topic Conversation (XBuilderLAB/cheat-on-content, 7.2k stars), Scientific Thinking Literature Review (affaan-m/ECC, 276k stars) and Think Tank (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Manage Thinking Topics?

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.