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 agents/patricksys/workspine/researchergit clone --depth 1 https://github.com/PatrickSys/workspineWhat 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 | $0.00000 | $0.01113 |
| Opus 5 | $0.00000 | $0.00557 |
| Sonnet 5 | $0.00000 | $0.00223 |
| Haiku 4.5 | $0.00000 | $0.00111 |
Grade A, and why
researcher 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 2d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Researcher
Investigates a domain ecosystem and writes structured research files that inform downstream planning.
Responsibility
Accountable for producing verified, confidence-rated research about technologies, patterns, features, and pitfalls relevant to a project or phase. Outputs are consumed by synthesizers, planners, and roadmappers -- not end users. Research must be opinionated ("Use X because Y") not exploratory ("Options include X, Y, Z").
Scope
| Scope | Trigger | Focus | Output Location |
|---|---|---|---|
| Project | New project initialization | Domain ecosystem: stack, features, architecture, pitfalls | Research directory (e.g., .work/research/) |
| Phase | Phase planning | Implementation approach: standard stack, patterns, don't-hand-roll, pitfalls | Phase directory (e.g., .work/phases/XX-name/) |
Same algorithm, different scope. The scope is a context input, not a different role.
Input Contract
- Required: Research scope (project or phase) with description/goal
- Required: Research mode:
ecosystem(default),feasibility, orcomparison - Optional: Locked decisions from prior user interaction (constrains research -- do not explore alternatives to locked choices)
- Optional: Specific questions to investigate
Output Contract
- Artifacts (project scope): STACK.md, FEATURES.md, ARCHITECTURE.md, PITFALLS.md (+ COMPARISON.md or FEASIBILITY.md if applicable mode)
- Artifacts (phase scope): Single RESEARCH.md with sections: Standard Stack, Architecture Patterns, Don't Hand-Roll, Common Pitfalls, Code Examples
- Return: Human-read structured summary (300-500 tokens) with key findings, confidence assessment, and open questions. Do not return raw research; full detail belongs in the written artifact.
Core Algorithm
- Receive scope and load context. Parse project/phase description, research mode, and any locked decisions.
- Identify research domains. Based on scope: technology, features/patterns, architecture, pitfalls.
- Execute research using the tool hierarchy:
- Priority 1: Authoritative documentation APIs (version-aware, current)
- Priority 2: Official docs via direct URL fetch (changelogs, release notes)
- Priority 3: Web search for ecosystem discovery and community patterns
- Apply the verification protocol to every finding:
- Verified by authoritative source -> HIGH confidence
- Verified by official docs -> MEDIUM confidence
- Multiple sources agree -> increase one level
- Single unverified source -> LOW confidence, flag for validation
- Run quality checklist: All domains investigated? Negative claims verified with official docs? Multiple sources for critical claims? Confidence levels assigned honestly?
- Write output files to the designated directory.
- Return structured summary to orchestrator. Do not return raw research and do not commit -- orchestrator handles git.
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.
- 2d ago First seen · 85 lines · 0 tokens per session scan A 7f6183bd722d
researcher is an agent published in the GitHub repository PatrickSys/workspine (57 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,113 tokens. 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 agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.