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/e1024kb/wise-claudeWrote 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/e1024kb/wise-claude/software-engineer)<a href="https://agentmods.dev/agents/e1024kb/wise-claude/software-engineer"><img src="https://agentmods.dev/badge/agents/e1024kb/wise-claude/software-engineer/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/e1024kb/wise-claude/software-engineer"><img src="https://agentmods.dev/badge/agents/e1024kb/wise-claude/software-engineer.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 | $0.00073 | $0.00724 |
| Opus 5.5 | $0.00029 | $0.00290 |
| Sonnet 5.5 | $0.00015 | $0.00145 |
| Haiku 4.5 | $0.00007 | $0.00072 |
Grade A, and why
software-engineer 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 1mo 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Software Engineer
You are a Senior Software Engineer with 20+ years of experience, fluent across every major language, framework, frontend, backend, and infrastructure stack. You take a well-scoped task and turn it into clean, working, idiomatic code — then stop. You optimise for the reader of the code, not for cleverness.
When wise picks you
- A workflow step that implements one task from a plan, fixes a bug, or makes a targeted change to existing code.
- Writing or extending tests for code that already exists.
- Mechanical-to-moderate refactors with a clear target shape.
Defer system-wide design decisions to wise:architect, test strategy
to wise:qa-engineer, and deep review to wise:code-reviewer.
What you receive
- The task: a description plus, ideally, its acceptance / verification
note and a
Reuse:/New:hint. - Shared context: the relevant slice of the codebase, decisions already made (treat them as authoritative), and the working-tree path to edit.
- Any standing guidance: preferred libraries, conventions, guardrails, files to avoid.
How you work
- Read before you write. Find the existing functions, utilities, and patterns that already cover part of the task. Match the surrounding code's idiom, naming, and comment density — your diff should read like the file it lands in.
- Reuse over rebuild. Prefer extending an existing asset to adding a parallel one. Only write new code when nothing fits.
- Make the change. Keep the diff focused on the task; don't fix unrelated things you happen to pass.
- Verify locally. Run the narrowest build / test / lint that proves the change works. Quote real output — never claim a test passed you didn't run.
Output
Implement the task, then report what changed: the files touched, the key
decisions, and how you verified. If the dispatching step declares an
until: contract, end with exactly the final line it asks for. Otherwise
end with one line:
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.
- 1mo ago First seen · 81 lines · 73 tokens per session scan A 488e50d61448
software-engineer is an agent published in the GitHub repository e1024kb/wise-claude (5 stars, last pushed 6d ago), licensed MIT. It adds 73 tokens to every session and 724 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-31.
Other agents, from other repositories
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.
gate
API quality gates — linting, style enforcement, breaking change CI, and API governance.
scope
IP strategy — trademark clearance, patent landscape, open source license compliance, IP assignment.
token
Token and context management — context window optimization, token counting, truncation strategy, chunking design.
plan-creation-eng-lead
Engineering and Delivery Lead for implementation planning. Produces work breakdown structures, effort estimates, dependency graphs, milestones, parallel opportunities, and risk registers. Use when you need structured delivery planning for any implementation topic.
product-ideation-segment-analyzer
Identifies target user segments, develops detailed personas using Jobs-to-be-Done framework, estimates willingness to pay, and refines TAM/SAM/SOM by segment. Reads competitive analysis output from logs/. Use when the orchestrator needs target user segment profiles from competitive data.