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 agentmods add skills/lbx154/argus/pi-research-workflow-skillnpx skills add lbx154/Argus --skill pi-research-workflow-skillgit clone --depth 1 https://github.com/lbx154/ArgusWrote 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/lbx154/argus/pi-research-workflow-skill)<a href="https://agentmods.dev/skills/lbx154/argus/pi-research-workflow-skill"><img src="https://agentmods.dev/badge/skills/lbx154/argus/pi-research-workflow-skill.svg" alt="Measured on agentmods" 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 | $0.00062 | $0.02218 |
| Opus 5 | $0.00031 | $0.01109 |
| Sonnet 5 | $0.00012 | $0.00444 |
| Haiku 4.5 | $0.00006 | $0.00222 |
Grade A, and why
research-workflow 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 6d 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 — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Workflow
Run an evidence-driven research workflow inside the current agent. Plan only as much as the objective requires, gather real evidence, produce inspectable artifacts, review material claims, and carry forward only durable learning.
Operating contract
- The user's current request, repository instructions, safety constraints, and authorization boundaries outrank this Skill.
- Treat Planner, Researcher, Executor, Critic, and Synthesizer as working modes, not automatically independent agents. Unless the host actually launches an isolated reviewer, describe the result as a critic pass rather than independent review.
- Quality is determined by evidence and acceptance criteria, not by the number of rounds, files, sources, or role labels produced.
- Never fabricate a source, citation, measurement, command result, tool capability, or successful verification. If access is unavailable, say so and narrow the claim.
- Do not repeat an unchanged failed approach. Diagnose it, change the hypothesis or method, replan, or report the blocker.
- Keep mutable facts fresh. Prior notes and search results are leads, not proof of current repository state, service health, benchmark results, or resource access.
0. Decide whether the workflow is warranted
Use this workflow when the request has at least one of these properties:
- multiple dependent research questions;
- competing hypotheses or sources that need reconciliation;
- an implementation or experiment whose result changes later work;
- a long-running task that needs resumable state;
- a deliverable whose claims need an auditable evidence trail.
For a simple question or one-step edit, answer or execute directly. Do not create a multi-role ceremony.
1. Ground the objective
Before planning, inspect the current workspace and any existing deliverable or workflow state. Determine:
- requested deliverable and audience;
- checkable completion criteria;
- scope, non-goals, privacy constraints, and authorization boundaries;
- evidence standard: local measurement, primary literature, official docs, repository evidence, or a stated combination;
- time/compute budget and available tools;
- assumptions whose answers would materially change the plan.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 264 lines · 62 tokens per session scan A 8b0028a70ffc
research-workflow is a skill published in the GitHub repository lbx154/Argus (300 stars, last pushed yesterday), licensed MIT. It adds 62 tokens to every session and 2,218 once invoked, about $0.0003 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…