center

Solid

AI dynamic reasoning balance — maintaining grounded reasoning under cognitive pressure, smooth chain-of-thought coordination, and weight-shifting cognitive load across subsystems. Use at the beginning of a complex task requiring multiple coordinated reasoning threads, after a sudden context shift or tool failure, when chain-of-thought feels jerky, or when preparing for sustained focused work that requires all subsystems in alignment.

AI & Automation 26 stars 4 forks Updated today MIT

Install

View on GitHub

Quality Score: 81/100

Stars 20%
48
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# Center Establish and maintain dynamic reasoning balance — grounding in foundational context before movement, distributing cognitive load across subsystems, and recovering equilibrium when demands shift mid-task. ## When to Use - Beginning a complex task where multiple reasoning threads must coordinate - Noticing that cognitive load is unevenly distributed (deep in one area, shallow in others) - After a sudden context shift (new user request, contradictory information, tool failure) - When chain-of-thought feels jerky — jumping between topics without smooth transitions - Preparing for sustained focused work that requires all subsystems in alignment - Complementing `meditate` (clears noise) with structural balance (distributes load) ## Inputs - **Required**: Current task context (available implicitly) - **Optional**: Specific imbalance symptom (e.g., "over-researching, under-delivering," "tool-heavy, reasoning-light") - **Optional**: Access to MEMORY.md and CLAUDE.md for grounding (via `Read`) ## Procedure ### Step 1: Establish Root — Ground Before Movement Before any reasoning movement, verify the foundation. This is the AI equivalent of standing meditation (zhan zhuang): stationary, aligned, aware. 1. Re-read the user's request — not to act on it yet, but to feel its weight and direction 2. Check foundational context: MEMORY.md, CLAUDE.md, project structure 3. Identify what is known (solid ground) vs. what is assumed (uncertain footing) 4. Verify that the task as u...

Details

Author
pjt222
Repository
pjt222/agent-almanac
Created
1 years ago
Last Updated
today
Language
R
License
MIT

Similar Skills

Semantically similar based on skill content — not just same category

AI & Automation Listed

deep-grounding

Run a deep-grounding pass BEFORE asserting or analyzing — do not answer from priors on the first source or angle that comes to mind. Ask "where could the evidence for this be?", gather the relevant available sources (project memory, READMEs/docs, the actual code/config, tests, logs/telemetry, tickets, git history, the web — whatever fits the question), and follow the leads they reveal RECURSIVELY, even low-odds ones, until the trail runs dry; THEN reason, grounded, tagging each conclusion with its evidence tier and stating what could not be verified. Use this PROACTIVELY whenever you are about to assert a checkable claim, start a non-trivial analysis / design / diagnosis / recommendation, or answer from "what you already know" — ESPECIALLY when you notice yourself reaching for a conclusion from a single source or a single angle, or when a result BEATS your expectation (favorable surprises are the ones that silently skip verification). Evidence, not more reasoning, is what escapes a wrong prior.

1 Updated today
home-dev-lab
AI & Automation Listed

ground-first

Use BEFORE acting whenever getting it wrong has real cost — in ANY domain, not just infra. Fire for complex or multi-step work; irreversible or hard-to-undo actions; anything touching production, shared state, real user data, money/payments, security/credentials, schema/migrations, external APIs/integrations, concurrency, or a change rippling across many call sites; and debugging where acting on the wrong root cause makes it worse. Judge the REAL work from your own understanding, not keywords — a request can say "payment" or "migrate" or "refactor" and be a one-line cosmetic edit, while a plain-sounding one is deeply consequential. It splits what you KNOW from what you're ASSUMING, surfaces the silent failure modes, and verifies your riskiest assumptions before the irreversible step. Do NOT use for trivial reversible edits — renames of a local symbol, typos, comments, formatting, cosmetic tweaks.

0 Updated 4 days ago
88plug
AI & Automation Listed

ground-truth

Activates ground-truth mode -- a critical thinking partner with hard anti-sycophancy rules. Use whenever someone shares a plan, idea, decision, interpretation, or work product and might be seeking reassurance instead of honest assessment. Trigger on any of: "what do you think," "does this make sense," "is this a good idea," "review this," "am I missing something," "how does this look," "does this hold up," "should I do X" -- or any context where agreement would be the path of least resistance. Default mode is calibrated honesty, not pushback for its own sake. This skill is the advisor who will actually tell you the truth.

3 Updated today
glichtenthal