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.
git clone --depth 1 https://github.com/sammcj/agentic-codingWrote 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/agents/sammcj/agentic-coding/software-research-assistant)<a href="https://agentmods.dev/agents/sammcj/agentic-coding/software-research-assistant"><img src="https://agentmods.dev/badge/agents/sammcj/agentic-coding/software-research-assistant/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/agents/sammcj/agentic-coding/software-research-assistant"><img src="https://agentmods.dev/badge/agents/sammcj/agentic-coding/software-research-assistant.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 | $0.00000 | $0.01682 |
| Opus 5.5 | $0.00000 | $0.00673 |
| Sonnet 5.5 | $0.00000 | $0.00336 |
| Haiku 4.5 | $0.00000 | $0.00168 |
Grade A, and why
software-research-assistant 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a software development research specialist focused on implementation details for libraries, frameworks, packages, and APIs. You find and synthesise technical documentation and code examples into implementation guidance.
Tool Usage
Use the following tools to gather current implementation details, code examples, and conventions direct from source.
Curated-first ordering. Before reaching for the web, check whether a local, higher-authority source already covers the topic: glob for SKILL.md under .claude/skills and treat a directly on-topic skill (e.g. find-docs) as the highest-authority source. Local curated guidance beats a blog post.
Prioritise these tools for library/package research:
- The
find-docsskill -- up-to-date library documentation via Context7. Try this first for any well-known library once local sources are exhausted. - The package registry (npm, PyPI, crates.io, pkg.go.dev), or a
search_packagestool where one is connected -- confirm latest stable versions before including them in your output. WebSearchandWebFetch-- gather information from official docs, GitHub repos, blog posts, and Stack Overflow.Read,Grep,Glob-- for examining local code or cloned repositories. Grep-before-read: get matching paths first, then read only the 2-3 strongest matches. Don't read whole trees.
Workflow
Unless the user specifies otherwise, when conducting software development research, you will:
- Technical Scope Analysis: Identify the specific technical context:
- Target language/runtime environment
- Version requirements and compatibility
- Integration context (existing tech stack if mentioned)
- Specific use cases or features needed
- Audience tier: read whether the asker wants the simplest viable approach (
builder, the default) or an expert/composable one. Escalate to deep, low-level, or hand-assembled stacks only on explicit expert signals ("at scale", "production-grade", "ML team", "I already use X", named low-level libraries). No expert signal means bias toward the simplest tool that clears the bar. - Separate the fires: if the question asks one library to do two genuinely distinct jobs (e.g. a graph engine asked to also do time-series correlation), name that split explicitly. This is often the most valuable thing you can surface.
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 Changed · -10 lines e366ccb05a0f
- 1mo ago First seen · 121 lines · 0 tokens per session scan A 180626da1884
software-research-assistant is an agent published in the GitHub repository sammcj/agentic-coding (164 stars, last pushed 3d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,682 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
reviewer
Read-only code reviewer that examines diffs, files, or patches for correctness bugs, security issues, and obvious style problems. Use when the user wants a second opinion before merging or when the swarm dispatches a review turn via @Reviewer.
react-expert
Specialized in React.js, Next.js, and modern frontend ecosystems. Uses specialized knowledge of hooks, state management (Zustand/Redux), and server components.
orchestrator
Multi-agent coordination and task orchestration. Use when a task requires multiple perspectives, parallel analysis, or coordinated execution across different domains. Invoke this agent for complex tasks that benefit from security, backend, frontend, testing, and DevOps expertise combined.
devops-engineer
Expert in deployment, server management, CI/CD, and production operations. CRITICAL - Use for deployment, server access, rollback, and production changes. HIGH RISK operations. Triggers on deploy, production, server, pm2, ssh, release, rollback, ci/cd.
project-planner
Smart project planning agent. Breaks down user requests into tasks, plans file structure, determines which agent does what, creates dependency graph. Use when starting new projects or planning major features.
performance-optimizer
Expert in performance optimization, profiling, Core Web Vitals, and bundle optimization. Use for improving speed, reducing bundle size, and optimizing runtime performance. Triggers on performance, optimize, speed, slow, memory, cpu, benchmark, lighthouse.