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 HybridAIOne/hybridclaw --skill feature-planninggit clone --depth 1 https://github.com/HybridAIOne/hybridclawWrote 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/hybridaione/hybridclaw/feature-planning)<a href="https://agentmods.dev/skills/hybridaione/hybridclaw/feature-planning"><img src="https://agentmods.dev/badge/skills/hybridaione/hybridclaw/feature-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/hybridaione/hybridclaw/feature-planning"><img src="https://agentmods.dev/badge/skills/hybridaione/hybridclaw/feature-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00018 | $0.00602 |
| Opus 5 | $0.00009 | $0.00301 |
| Sonnet 5 | $0.00004 | $0.00120 |
| Haiku 4.5 | $0.00002 | $0.00060 |
Grade A, and why
feature-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 11d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Planning
Use this skill to turn a feature request into an implementation plan that is specific enough to execute without rediscovering the codebase.
Planning Workflow
- Confirm the goal, constraints, and non-goals.
- Inspect the current code paths, types, tests, and similar features.
- Identify the files and system boundaries likely to change.
- Break the work into small sequenced tasks.
- Define validation for each stage and for the final change.
- Capture risks, dependencies, and unanswered questions.
Default Output
When the user asks for a plan and does not specify a format, use:
- Goal
- Current state
- Proposed approach
- Task breakdown
- Validation plan
- Risks and unknowns
- Recommended next action
Task Rules
Each task should have one concrete outcome. Prefer:
- exact file paths instead of vague module names
- explicit commands instead of "run the tests"
- acceptance criteria that can be verified
- clear notes on migrations, docs, config, or rollout work when relevant
If the scope is large, group tasks into milestones, but keep each task small enough that an implementer can finish it without further decomposition.
Codebase Exploration
Before finalizing a plan, inspect the repo for:
- existing patterns that should be preserved
- nearby tests and fixtures
- configuration or schema touchpoints
- user-facing docs or CLI/help text that may need updates
Use the existing codebase to anchor the plan instead of inventing new patterns.
Validation Expectations
Every plan should name the checks needed to prove the change works. Prefer the smallest useful set, for example:
npm run typecheck
npm run lint
npm run test:unit
Replace generic commands with repo-specific ones after inspecting the project.
Working Rules
- Separate required work from optional polish.
- State assumptions when dates, estimates, or dependencies are uncertain.
- Sequence risky or high-uncertainty work before cleanup and polish.
- Name what will not change so scope stays bounded.
- Flag decisions that need user input instead of hiding them in the plan.
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.
- 11d ago First seen · 101 lines · 18 tokens per session scan A e2d020b639c6
feature-planning is a skill published in the GitHub repository HybridAIOne/hybridclaw (132 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 602 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 2026-08-30.
Other skills, from other repositories
lilbee-mcp
Search and manage the user's local lilbee knowledge base over MCP. Use whenever the user has indexed code, docs, PDFs, or web pages into lilbee and you need cited answers, or whenever they ask you to ingest content, swap models, or tune retrieval against their library. Every fact returned cites file and line.…
lilbee-mcp-wiki
Wiki layer for lilbee. Use only when the user explicitly asks about wiki / concept / entity / synthesis pages, or when lilbeestatus shows a built wiki. Requires the lilbee-mcp skill to be active for the underlying MCP connection.
m3-health
Health check — package version, installed payload, chatlog DB row count, per-agent hook state.
m3-help
List all m3-memory slash commands / skills and what they do.
continuum-recipes
Copy-pasteable Continuum patterns — RAG, plan-and-execute, ReAct, multi-tenant agents, FastAPI integration, structured output, prompt-injection scanning, custom containers. Invoke when the user asks "how do I do X with Continuum" and X is a common app pattern rather than a single API question.
m3-forget
Delete a memory permanently. Asks for confirmation first.