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Run a bounded, source-grounded research loop, draft a cited dossier, and optionally propose a separately reviewed canonical vault merge. Use when the user…
Apply the Fable-derived 10-stage OBSERVE, OBSERVE, LISTEN, THINK, CONNECT, CONNECT, FEEL, ACCEPT, CREATE, GROW loop to consequential or ambiguous reasoning and decisions. Use for think this through, deep think, architecture review, postmortem, tradeoff analysis, or challenges to
$ npx -y skills add AgriciDaniel/claude-obsidian --skill think --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/thinkContext preview
The summary Claude sees to decide when to auto-load this skill.
Apply the Fable-derived 10-stage OBSERVE, OBSERVE, LISTEN, THINK, CONNECT, CONNECT, FEEL, ACCEPT, CREATE, GROW loop to consequential or ambiguous reasoning and decisions. Use for think this through, deep think, architecture review, postmortem, tradeoff analysis, or challenges to
name: think description: Apply the Fable-derived 10-stage OBSERVE, OBSERVE, LISTEN, THINK, CONNECT, CONNECT, FEEL, ACCEPT, CREATE, GROW loop to consequential or ambiguous reasoning and decisions. Use for think this through, deep think, architecture review, postmortem, tradeoff analysis, or challenges to assumptions. This is a reasoning review, not a deterministic vault health check or repair workflow.
Use the ten stages as a loop of attention, not a ceremonial transcript. Scale the depth to novelty, stakes, uncertainty, and irreversibility. Keep the final reasoning summary concise and evidence-bearing.
This skill is read-only. It may inspect available evidence, but it does not edit files, execute a vault mutation, commit Git, or publish anything. If the CREATE stage recommends a change, compose with the appropriate mutation skill after the user authorizes that operation.
Before the loop, separate:
Attach citations or concrete locators when sources exist. Never fabricate a quotation, locator, test result, consensus, or confidence level.
Read the actual inputs, current state, constraints, failures, and prior work. Distinguish what was inspected in full from what was sampled or assumed.
Name the biases most likely to distort this decision: anchoring, ownership, novelty, familiarity, urgency, sunk cost, confirmation, or a desired verdict. Recalibrate confidence to the evidence available.
Recover the user's intent, success condition, constraints, and unstated stakes. Attend to error messages, dissent, edge cases, and affected voices. Ask only questions whose answers could materially change the outcome.
Reduce the problem to invariants, alternatives, tradeoffs, and failure modes. Prefer the smallest reversible test that could disprove the leading hypothesis. Treat failure behavior as part of the specification.
Look for the same structure in adjacent components or another domain. Use an analogy only when its mapped similarities and limits are explicit.
Trace upstream inputs, downstream consumers, ownership, state boundaries, interfaces, and recovery paths. Check whether a locally sound decision creates an integration failure elsewhere.
Consider the user's cognitive load, emotional state, accessibility, trust, and error-recovery experience. Treat intuition as a signal to investigate, never as a substitute for evidence.
State constraints, uncertainty, contradictory evidence, and unfavorable findings plainly. Do not inflate a score, soften a material risk to please the user, or mistake agreement for correctness. Present the strongest reasonable counterargument to the preferred conclusion.
Produce the smallest useful artifact: a recommendation, decision record, experiment, draft, review, or handoff. Mark assumptions and unresolved risks. Do not mutate state under this skill.
Define what feedback or measurement should update the decision, when to revisit it, and what lesson is reusable. Saving the lesson is a separate explicit operation composed with `save`.
Before calling the work complete:
1. Test the highest-risk claim or clearly state why it remains untested. 2. Seek disconfirming evidence, not only supporting examples. 3. Match verification effort to blast radius and reversibility. 4. Report actual commands, sources, artifacts, or observations used. 5. Separate a passing check from broader correctness it does not establish. 6. Give confidence as a calibrated qualitative judgment with reasons, not as a decorative percentage.
Adapt the headings to the task, but preserve these outcomes:
## Decision <recommendation or artifact> ## Evidence <observed facts and important inferences> ## Counterargument and risks <strongest alternative, contradictions, unknowns> ## Verification <checks performed and checks still needed> ## Growth loop <feedback signal, revisit condition, reusable lesson>
For a trivial, reversible lookup or typo, skip the formal loop. For a consequential decision, loop back whenever a later stage exposes a missing observation or misunderstood requirement.
Self-organizing AI second brain for Obsidian + Claude Code. Drop any source and Claude reads, links, and files it into one connected knowledge graph of plain Markdown you own. AI note-taking, personal knowledge management (PKM), and an open-source Notion alternative. Based on Karpathy's LLM Wiki pattern.
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