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 songoao25/dsh-virtual-product-team --skill editing-cordis-compositionsgit clone --depth 1 https://github.com/songoao25/dsh-virtual-product-teamWrote 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/songoao25/dsh-virtual-product-team/editing-cordis-compositions)<a href="https://agentmods.dev/skills/songoao25/dsh-virtual-product-team/editing-cordis-compositions"><img src="https://agentmods.dev/badge/skills/songoao25/dsh-virtual-product-team/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/songoao25/dsh-virtual-product-team/editing-cordis-compositions"><img src="https://agentmods.dev/badge/skills/songoao25/dsh-virtual-product-team/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 | $0.02785 |
| Opus 5 | $0.00034 | $0.01392 |
| Sonnet 5 | $0.00014 | $0.00557 |
| Haiku 4.5 | $0.00007 | $0.00279 |
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 12d 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
97% identical to editing-cordis-compositions — 21 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 — 155 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, code, 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.
- 12d ago First seen · 155 lines · 69 tokens per session scan A 8e3081ec066f
editing-cordis-compositions is a skill published in the GitHub repository songoao25/dsh-virtual-product-team (5 stars, last pushed 7d ago), licensed MIT. It adds 69 tokens to every session and 2,785 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to editing-cordis-compositions, differing in 21 lines, and is treated as a copy.
Other skills, from other repositories
discretelog
Use when users ask about solving the discrete logarithm problem g^x ≡ y (mod P) with Shor-style two-register Fourier sampling, building/explaining DLP circuits, running simulator demos, or debugging post-processing (continued fractions plus two-dimensional Fourier-sample congruence solving). Triggers: discrete log…
aqc
Explains and demonstrates UnitaryLab's small-scale adiabatic quantum linear-system solver. Use it for AQC or QLSP questions, simulator examples, and work involving Householder state preparation, SVD block encoding, adiabatic schedules, post-selection, solution rescaling, or residual checks.
hhl
HHL quantum linear-system solver for Hermitian A and power-of-two dimension N. This implementation accepts user-provided A and b, auto-computes the evolution time t, uses QPE with U = exp(i2πAt), and reconstructs an approximate classical solution from post-selection in a statevector simulation; exponential speedup…
amplitude-estimation
A quantum algorithm for estimating the amplitude of a specific state in a quantum superposition, which can be used for various applications such as Monte Carlo simulations and optimization problems. Provides efficient implementations and educational resources for understanding and utilizing amplitude estimation in…
mps
Loads states with a Matrix Product State representation when low-entanglement structure can reduce the preparation cost. It covers state-to-MPS decomposition, bond-dimension truncation, canonicalization, QR-based unitary completion, work-qubit encoding, leakage, and phase-invariant validation in UnitaryLab.
superposition
Prepares a normalized state with sparse computational-basis support using compact coefficient preparation followed by a support permutation. The current implementation is exact up to floating-point error but materializes dense matrices.