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 skills add singula-ai/alego --skill editing-cordis-compositionsgit clone --depth 1 https://github.com/singula-ai/alegoWrote 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/skills/singula-ai/alego/editing-cordis-compositions)<a href="https://agentmods.dev/skills/singula-ai/alego/editing-cordis-compositions"><img src="https://agentmods.dev/badge/skills/singula-ai/alego/editing-cordis-compositions/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/skills/singula-ai/alego/editing-cordis-compositions"><img src="https://agentmods.dev/badge/skills/singula-ai/alego/editing-cordis-compositions.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 | $0.00069 | $0.03173 |
| Opus 5 | $0.00034 | $0.01587 |
| Sonnet 5 | $0.00014 | $0.00635 |
| Haiku 4.5 | $0.00007 | $0.00317 |
Grade A, and why
editing-cordis-compositions 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 6d 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.
This is a copy
95% identical to editing-cordis-compositions — 20 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Editing Cordis compositions
Every capability in this harness is a plugin row in a cordis.yml. There is no separate configuration language: changing what an agent can do means changing which rows are composed for it.
Off-limits
Never edit, delete, or overwrite a preset that ships with the deployment — the agent-presets directory beside the deployment's own config, which supplies standard, ptc, minimal, and cordis. Never escalate the sandbox to reach it, even when a change there looks quicker. An upgrade overwrites that install, and corrupting cordis disables preset authoring itself. Reading a shipped composition is the intended way to start; writing to one is not, and neither is editing the host composition to work around a preset limitation.
To change what a shipped preset does, copy it and edit the copy. Locally authored presets under the user root are yours to create, edit, and delete.
Decide the plane first
Two planes, and the choice is not about how "agent-related" something feels — it is about whether the thing must be shared.
Host composition. The registries themselves (tools, systemPrompt, agents, agent-loop, sessions), anything crossing sessions (persistence, session query, storage, settings, credentials, telemetry), the sandbox and approval stack, the model route, and the subagent registry with its spawn/fork backends. One instance for the process.
Agent preset. What one session contributes to those registries: its tool plugins, its persona and prompt sections, its compaction policy. One instance per session, mounted under that session's scope and unwound with it.
A service with a consumer outside the agent plane cannot move into a preset. subagents is the worked example: the registry answers cross-session queries for the host api-proxy, so a per-session copy both starves that host row — it waits forever for a service nothing provides — and collides on the second session, since a provider name registers once. The preset contributes the delegation tools; the registry and its backends stay host-side.
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.
- 6d ago Changed 91c511d38613
- 12d ago First seen · 166 lines · 69 tokens per session scan A 280ab1649ec7
editing-cordis-compositions is a skill published in the GitHub repository singula-ai/alego (109 stars, last pushed yesterday), licensed MIT. It adds 69 tokens to every session and 3,173 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to editing-cordis-compositions, differing in 20 lines, and is treated as a copy.
Other skills, from other repositories
ctx
Codebase intelligence and evidence-driven governance with the indexed ctx CLI. Use when exploring an unfamiliar repository, locating symbols or callers, checking for existing implementations, estimating change impact, enforcing architecture rules, scoring a branch, finding hotspots or duplication, or analyzing…
ctx
Codebase intelligence and evidence-driven governance with the indexed ctx CLI. Use when exploring an unfamiliar repository, locating symbols or callers, checking for existing implementations, estimating change impact, enforcing architecture rules, scoring a branch, finding hotspots or duplication, or analyzing…
todos
This chat has a shared, live TODO plan — your tasks for the conversation, which the user also edits. Read this skill and reach for the todo tools whenever a request takes more than a couple of steps. It covers the plan model (group = task, items = its steps; loose items are the user's lane), how to work it: propose…
writing-workflow-skills
Use when adding a new workflow skill to pi-thinkrail-workflow, changing an existing workflow skill's role, trigger, handoff, or structure, or checking a workflow skill against the workflow system's rules. Not for authoring general-purpose skills outside this package.
ai-ml-development
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
case-interview-practice
Interactive consulting case interview practice with structured frameworks, feedback mechanisms, and progressive difficulty. Use when preparing for management consulting interviews, case competitions, or business problem-solving exercises.