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 commands/echoingvesper/mcp-task-orchestrator/task-list-initgit clone --depth 1 https://github.com/EchoingVesper/mcp-task-orchestratorWhat 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 | $0.00257 |
| Opus 5 | $0.00000 | $0.00129 |
| Sonnet 5 | $0.00000 | $0.00051 |
| Haiku 4.5 | $0.00000 | $0.00026 |
Grade A, and why
task-list-init 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.
What it actually says
claude ** Create a comprehensive task list in PRPs/checklist.md for building our hackathon project based on $ARGIMENTS
Ingest the infomration then dig deep into our existing codebase, When done ->
ULTRATHINK about the product task and create the plan based on claude.md and create detailed tasks following this principle:
list of tasks to be completed to fullfill the PRP in the order they should be completed using infomration dense keywords
- Infomration dense keyword examples: ADD, CREATE, MODIFY, MIRROR, FIND, EXECUTE, KEEP, PRESERVE etc
Mark done tasks with: STATUS [DONE], if not done leave empty
Task 1:
STATUS [ ]
MODIFY src/existing_module.py:
- FIND pattern: "class OldImplementation"
- INJECT after line containing "def __init__"
- PRESERVE existing method signatures
STATUS [ ]
CREATE src/new_feature.py:
- MIRROR pattern from: src/similar_feature.py
- MODIFY class name and core logic
- KEEP error handling pattern identical
...(...)
Task N:
...
Each tasks hould have unit test coverage, snure tests pass on each task
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 · 36 lines · 0 tokens per session scan A 9023122a6d15
task-list-init is a command published in the GitHub repository EchoingVesper/mcp-task-orchestrator (28 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 257 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 commands, from other repositories
screen-usecase
筛选、验证并撰写 OpenClaw 用例。输入来源 URL 或描述，输出适配评估或完整用例 PR。.
evolve
Agrupa instincts consolidados en skills accionables y las registra en skills-registry/custom/evolved/. Las skills quedan PENDIENTEREVISION hasta que el usuario las apruebe.
proyecto
Declara el proyecto activo (scope global) para atribucion de coste/horas en agentactions. Sin argumento, muestra el proyecto actual.
svsi-review
/svsi-review {slug} # modo interactivo en sesión Claude Code /svsi-review --batch {file} # delega a predrivereview.py (tmux, no en sesión) /svsi-review --stats # métricas acumuladas desde SQLite.
handover
Works for you. Go outside and live. — AI orchestrator that auto-routes tasks to the cheapest model that solves them. 70% run free on local models. Self-auditing, self-improving, zero prompting skill needed. Built with vibe coding by a finance student. Your models, your data.
instinct-status
Shows learned instincts from dqiii8.db, grouped by project and confidence. Internal diagnostic tool — not for user invocation.