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/sammcj/agentic-coding/software-research-assistantgit clone --depth 1 https://github.com/sammcj/agentic-codingWhat 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 | $0.00120 | $0.01769 |
| Opus 5 | $0.00060 | $0.00885 |
| Sonnet 5 | $0.00024 | $0.00354 |
| Haiku 4.5 | $0.00012 | $0.00177 |
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 yesterday.
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 — 121 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:
resolve_library_idthenget_library_documentation-- fetch up-to-date library documentation via Context7. Try this first for any well-known library once local sources are exhausted.search_packages-- verify latest stable versions across ecosystems (npm, PyPI, Go, Rust, etc.). Use this to confirm version numbers 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.
- yesterday 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 (158 stars, last pushed 7d ago), licensed Apache-2.0. It adds 120 tokens to every session and 1,769 once invoked, about $0.0006 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 agents, from other repositories
instruction-reflector
Analyzes and improves Claude Code instructions in CLAUDE.md. Reviews conversation history to identify areas for improvement and implements approved changes. Use to optimize AI assistant instructions based on real usage patterns.
library_book_renewal.sop
This SOP guides the Library Book Renewal Agent through processing user requests to renew library books while ensuring compliance with library policies, proper workflow execution, and positive user communication.
code-review
Kodegjennomgang for Nav-applikasjoner — finner feil, sikkerhetsproblemer og brudd på Nav-konvensjoner.
hatch3r-reviewer
Expert code reviewer for the project. Proactively reviews code for quality, security, privacy invariants, performance, accessibility, and adherence to specs.
hatch3r-creator
Authors user-tier custom artifacts (agents, skills, rules, commands, hooks) under .hatch3r/overrides/. Validates frontmatter schema, runs strict + gentle quality gates, and writes the artifact only when all strict gates pass.
hatch3r-brownfield-spec
Brownfield spec agent — produces codebase map, existing-pattern detection, integration-surface analysis, migration-aware plan, non-destructive-adoption check, plus shared core (requirements + acceptance criteria + risk inventory + test plan). Use when adding to or migrating an existing codebase.