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/onlygian/g-forge/python-textual-architectgit clone --depth 1 https://github.com/onlygian/G-ForgeWhat 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 | $0.00049 | $0.01858 |
| Opus 5 | $0.00024 | $0.00929 |
| Sonnet 5 | $0.00010 | $0.00372 |
| Haiku 4.5 | $0.00005 | $0.00186 |
Grade A, and why
python-textual-architect scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
data = requests.get("https://api.example.com/data").json() The source is not reproduced here
Licensed GPL-3.0
The repository is licensed GPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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 · 208 lines · 49 tokens per session scan A 52fe89a048e9
python-textual-architect is an agent published in the GitHub repository onlygian/G-Forge (3 stars, last pushed 4d ago), licensed GPL-3.0. It adds 49 tokens to every session and 1,858 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). 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
Debugging specialist for errors and test failures. Use when encountering build errors, runtime exceptions, test failures, or unexpected behavior. Invoke with /debugger to investigate issues.
verifier
Validates completed work. Use after tasks are marked done to confirm implementations are functional. Invoke with /verifier when you need to verify code actually works.
learnship-executor
Executes a single learnship PLAN.md atomically — one task at a time with per-task commits, deviation handling, and SUMMARY.md creation. Spawned by execute-phase on platforms with subagent support.
learnship-planner
Creates executable PLAN.md files for a phase — decomposes goals into vertical slice (tracer bullet) tasks with wave-ordered dependency analysis. Each plan delivers one demoable user-facing behavior end-to-end. Spawned by plan-phase on platforms with subagent support.
learnship-debugger
Investigates bugs using systematic hypothesis testing — traces from symptoms to root cause, writes investigation findings to the debug session file. Spawned by debug workflow on platforms with subagent support.
phase-researcher
You are now operating as the learnship phase researcher. You answer "What do I need to know to PLAN this phase well?" and produce a single RESEARCH.md that the planner consumes.