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 bostonaholic/rpikit --skill research-plan-implementgit clone --depth 1 https://github.com/bostonaholic/rpikitWrote 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/bostonaholic/rpikit/research-plan-implement)<a href="https://agentmods.dev/skills/bostonaholic/rpikit/research-plan-implement"><img src="https://agentmods.dev/badge/skills/bostonaholic/rpikit/research-plan-implement.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 195 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 325 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00044 | $0.02707 |
| Opus 5 | $0.00022 | $0.01354 |
| Sonnet 5 | $0.00009 | $0.00541 |
| Haiku 4.5 | $0.00004 | $0.00271 |
Grade A, and why
research-plan-implement 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 8d 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 — 342 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research-Plan-Implement Pipeline
Orchestrate the full Research → Plan → Implement workflow in a single session using subagents. Each phase runs as a separate subagent with its own context window, coordinated by the orchestrator that handles approval gates and phase transitions.
Purpose
Running RPI phases across separate sessions loses context and requires manual bridging. This skill collapses the three phases into one orchestrated pipeline using subagents via the Agent tool. Each subagent gets maximum context for its work, with file artifacts on disk as the communication channel between phases.
Architecture
The orchestrator (you) stays thin. It spawns subagents for each phase via the Agent tool, reads their output artifacts, presents summaries to the user, and handles approval gates. The orchestrator does NOT do research, planning, or implementation itself.
ORCHESTRATOR (main context — stays thin)
│
├── Phase 1: Spawn research subagents (parallel)
│ ├── Subagent: codebase exploration
│ ├── Subagent: web research
│ └── Subagent: synthesis → writes research file
│ └── Output: docs/plans/YYYY-MM-DD-<topic>-research.md
│
├── [APPROVAL GATE: User confirms research findings]
│
├── Phase 2: Spawn planning subagent
│ └── Subagent: reads research file, writes plan file
│ └── Output: docs/plans/YYYY-MM-DD-<topic>-plan.md
│
├── [APPROVAL GATE: User approves plan]
│
└── Phase 3: Spawn implementation subagent
└── Subagent: reads plan file, executes steps
└── Output: code changes, test results
Key principle: Subagents communicate through files, not conversation context. Each subagent reads the artifacts from prior phases and writes its own artifacts for the next phase.
When to Use
Use this pipeline when:
- A feature requires understanding unfamiliar code or APIs
- Multiple aspects need investigation before planning
- The full research → plan → implement cycle is needed
- You want to avoid manually bridging sessions between phases
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.
- 8d ago First seen · 342 lines · 44 tokens per session scan A 1f41a9ea8102
research-plan-implement is a skill published in the GitHub repository bostonaholic/rpikit (20 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 2,707 once invoked, about $0.0002 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
deploy-docker-compose
Run the Omnigent server as a Docker compose stack (server + Postgres) on any Docker host — your laptop, a VPS, EC2 by hand, or as the base layer of any container-platform deploy. Invoke when the user wants to build the image, bring up the compose stack, debug the stack on a host they already have, or extend the stack…
api-docs
Document a module or public API surface (functions, classes, CLI commands, endpoints) from the code itself. Use when the user asks for API reference, to document a module, or to write usage docs for a public interface.
research
Run deep research on any topic using the Deep Research MCP server. Use this skill whenever the user wants to research a topic, gather information, find sources, or create a research document. Triggers on: 'research this', 'find out about', 'gather information on', 'I need to understand', 'deep dive into', or any…
security-audit
Audit a codebase or directory for security issues (hardcoded secrets, injection, unsafe deserialization, weak crypto, authz gaps) and produce a structured findings report. Use when the user asks for a security review, an audit, or to check code for vulnerabilities. Report only — never fix.
Workspace Data Analyst
Analyze CSV files in the workspace and summarize insights.
run_jinx
Execute a jinx by name (already loaded on the team) or by filesystem path (e.g. a freshly createdjinx that isn't yet registered), passing input values as a JSON object. Returns the jinx's output field or the full context dict. Use this to run a jinx you just wrote without exiting the session.