Borrowing it
Nothing to install: this file belongs to nickmisasi/planner. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/nickmisasi/planner/main/.claude/skills/research/SKILL.mdgit clone --depth 1 https://github.com/nickmisasi/plannerWrote 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/nickmisasi/planner/research)<a href="https://agentmods.dev/skills/nickmisasi/planner/research"><img src="https://agentmods.dev/badge/skills/nickmisasi/planner/research/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/nickmisasi/planner/research"><img src="https://agentmods.dev/badge/skills/nickmisasi/planner/research.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 | $0.00037 | $0.00823 |
| Opus 5.5 | $0.00015 | $0.00329 |
| Sonnet 5.5 | $0.00007 | $0.00165 |
| Haiku 4.5 | $0.00004 | $0.00082 |
Grade A, and why
research 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 2mo 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conduct a focused research session to inform planning.
Instructions
When the user invokes /research, follow these steps:
1. Ensure Context is Loaded
Research should be tied to a project and ideally an idea. If no context is loaded, trigger /continue first. If the user wants project-level research (not idea-specific), that's fine — results go in context.md.
2. Define Research Scope
Use AskUserQuestion to clarify what we're researching. Options:
- Codebase analysis — Read and understand code in a target repo
- Web research — Look up docs, APIs, libraries, blog posts, technical references
- Product research — Competitive analysis, user patterns, product decisions
- Mixed — Combination of the above
Also ask:
- Specific questions — What exactly do we need to find out?
- Depth — Quick survey or deep dive?
3. Conduct the Research
For codebase analysis:
- Use
ghCLI to browse the target repo (fromproject.yamlrepos list) - Clone if deep analysis is needed:
git clone <url> /tmp/<repo-name> - Use Grep, Glob, and Read tools to navigate
- Focus on architecture, patterns, and relevant code paths
- Note file paths and line numbers for key findings
For web research:
- Use WebSearch and WebFetch tools
- Search for relevant documentation, blog posts, and discussions
- Look for prior art and how others have solved similar problems
- Check library docs, API references, and changelogs
For product research:
- Search for competitive products and how they handle similar features
- Look for user feedback, forum discussions, and feature requests
- Check GitHub issues/discussions on relevant projects
4. Record Findings
Update the appropriate file:
- Idea-level research: Write findings to
projects/<project>/ideas/<idea>/research.md - Project-level research: Write findings to
projects/<project>/context.md
Structure findings clearly:
- Group by topic/question
- Include sources (URLs, file paths, commit hashes)
- Highlight key takeaways
- Note how findings impact the spec or planning
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.
- 2mo ago First seen · 98 lines · 37 tokens per session scan A a21710fed777
research is a skill published in the GitHub repository nickmisasi/planner (5 stars, last pushed 6mo ago), licensed MIT. It adds 37 tokens to every session and 823 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
research compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| researchnickmisasi/planner | Skill | 5 | 6mo ago | MIT | A | 37 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |