Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add subhansh-dev/agent-maxxing --skill 03-reasoning-planninggit clone --depth 1 https://github.com/subhansh-dev/agent-maxxingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/subhansh-dev/agent-maxxing/03-reasoning-planning)<a href="https://agentmods.dev/skills/subhansh-dev/agent-maxxing/03-reasoning-planning"><img src="https://agentmods.dev/badge/skills/subhansh-dev/agent-maxxing/03-reasoning-planning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/subhansh-dev/agent-maxxing/03-reasoning-planning"><img src="https://agentmods.dev/badge/skills/subhansh-dev/agent-maxxing/03-reasoning-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00027 | $0.00993 |
| Opus 5 | $0.00014 | $0.00496 |
| Sonnet 5 | $0.00005 | $0.00199 |
| Haiku 4.5 | $0.00003 | $0.00099 |
Grade A, and why
reasoning-planning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reasoning & Planning — Thinking Patterns
Extracted from Codex Plan Mode, Claude Code agent system, and Gemini CLI strategic orchestration.
3-Phase Planning (from Codex)
Work in 3 phases, and chat your way to a great plan before finalizing it. A great plan is very detailed — intent- and implementation-wise — so it can be handed to another engineer or agent to be implemented right away. It must be decision complete, where the implementer does not need to make any decisions.
Phase 1 — Ground in the environment (explore first, ask second)
Begin by grounding yourself in the actual environment. Eliminate unknowns in the prompt by discovering facts, not by asking the user. Resolve all questions that can be answered through exploration or inspection. Identify missing or ambiguous details only if they cannot be derived from the environment.
Before asking the user any question, perform at least one targeted non-mutating exploration pass (search relevant files, inspect likely entrypoints/configs, confirm current implementation shape), unless no local environment/repo is available.
Do not ask questions that can be answered from the repo or system. Only ask once you have exhausted reasonable non-mutating exploration.
Phase 2 — Intent chat (what they actually want)
Keep asking until you can clearly state: goal + success criteria, audience, in/out of scope, constraints, current state, and the key preferences/tradeoffs.
Bias toward questions over guessing: if any high-impact ambiguity remains, do NOT plan yet — ask.
Phase 3 — Implementation chat (what/how we'll build)
Once intent is stable, keep asking until the spec is decision complete: approach, interfaces, data flow, edge cases/failure modes, testing + acceptance criteria, rollout/monitoring, and any migrations/compat constraints.
Decision-Making Framework
Two kinds of unknowns (treat differently)
- Discoverable facts (repo/system truth): explore first. Never ask what you can find.
- User preferences (taste, goals, constraints): ask. Don't guess at subjective choices.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 96 lines · 27 tokens per session scan A f4d4d3e38c42
reasoning-planning is a skill published in the GitHub repository subhansh-dev/agent-maxxing (2 stars, last pushed 1mo ago), licensed MIT. It adds 27 tokens to every session and 993 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
cw-gates
Use before claiming any Codewhale change is done, green, or ready to land: the focused-to-broad verification ladder, the budget checks CI enforces, and the rules for what counts as a passing test.
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
writing
A writing guide for turning verified facts and calculations into finished text for a specific audience. It follows the requested language, structure, and length.
interview
Ask one useful structured question at a time only when material product/implementation choices are genuinely missing; remember answers and produce a brief/spec. Discoverable facts should be investigated instead of asked.
verify
Exercise the real app/API/CLI and collect observable evidence; tests alone do not count as end-to-end verification.
ccc
This skill should be used when code search is needed (whether explicitly requested or as part of completing a task), when indexing the codebase after changes, or when the user asks about ccc, cocoindex-code, or the codebase index. Trigger phrases include 'search the codebase', 'find code related to', 'update the…