customize

Original：🇺🇸 English

Translated

Configure the project's skill stack and supervision preferences. Reads the curated registry in `skillpacks/skill_dictionary.yaml`, asks a short preset-first set of questions about workflow, dependency tolerance, autonomy style, and resource policy, then writes or updates `.co-researcher/skills.yaml`. Trigger phrases: "customize my stack", "configure skillpacks", "set up my skills", "choose presets", "configure supervision and packs", "personalize this project".

3installs

Sourcezy-ning/oh_my_co-researcher

Added on2026-05-27

NPX Install

npx skill4agent add zy-ning/oh_my_co-researcher customize

SKILL.md Content

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Customize

Use this skill when the user wants to choose a preferred stack for the current project instead of manually copying many external skills.

Goals

customize

should:

Read the curated registry in
```
skillpacks/skill_dictionary.yaml
```
.
Recommend packs and subsets by capability, not just by repo name.
Configure supervision policy alongside skill selection.
Write the result to
```
.co-researcher/skills.yaml
```
.
Keep the result easy to read and edit manually.

If

.co-researcher/skills.yaml

is absent, create it. If present, preserve unrelated fields where possible.

Use

templates/skills.yaml.template

as the starting shape when you need to create the file from scratch.

When expanding a preset into the full file, merge: preset's

packs

and

supervision.mode

/

risk_profile

take precedence; all other fields (

preferences

, full

supervision

subtree) are filled from template defaults unless the user explicitly changed them.

Inputs

Registry sources

Read:

```
skillpacks/skill_dictionary.yaml
```
```
skillpacks/presets/*.yaml
```

These are the source of truth for available packs, curated subsets, and preset bundles.

Existing project state

If present, also read:

```
.co-researcher/skills.yaml
```
```
RESEARCH.md
```

Use them to infer whether the project is literature-heavy, experiment-heavy, publication-focused, or still exploratory.

Interaction model

Use a short preset-first flow. Ask all questions together when possible.

Required questions

Workflow type

```
core-only
```
```
low-dependency
```
```
literature-heavy
```
```
experiment-heavy
```
```
academic-rigor
```
```
balanced
```
```
custom
```

Dependency tolerance
- minimal
- balanced
- best-in-class even if heavier
Autonomy style
- manual
- checkpointed
- autonomous
- wild
Resource policy
- local only
- APIs allowed
- remote compute allowed
Preference knobs
- minimize overlap?
- prefer subset installs?
- allow experimental packs?

Existing config editing

If

.co-researcher/skills.yaml

already exists, begin by asking:

keep current selection and only adjust supervision?
adjust packs only?
adjust both?

Recommendation rules

Capability-first mapping

If the user wants stronger literature search/synthesis, prioritize

aris/research-lit

,

aris/research-refine

,

feynman/alpha-research

, or

academic-research-skills/deep-research

.

If the user wants stronger experiment planning, prioritize
```
aris/experiment-plan
```
.
If the user wants claim/interpretation checking, prioritize
```
aris/result-to-claim
```
.
If the user wants academic rigor in writing/review, prioritize
```
academic-paper-reviewer
```
.
If the user wants compute-heavy execution, surface NanoResearch as a niche/heavy option rather than a default.

Dependency warnings

```
feynman/alpha-research
```
requires AlphaXiv authentication (
```
feynman alpha login
```
). Always surface this when recommending
```
literature-heavy
```
or any feynman skill.
```
nanoresearch/project-experiment
```
requires local GPU or cluster access. Always surface this when recommending
```
experiment-heavy
```
with NanoResearch.

Default posture

Always keep
```
core
```
enabled.
Prefer curated subsets over full-pack installs.
Avoid recommending heavy overlapping packs unless the user explicitly wants them.

Preset choice

Prefer a preset when the user's answers fit one cleanly. Fall back to custom pack selection only when necessary.

balanced

should map to the

balanced

preset in

skillpacks/presets/balanced.yaml

.

Output file

Write or update:

text

.co-researcher/skills.yaml

Expected shape:

yaml

version: 1
profile: balanced

selection:
  preset: balanced

enabled_packs:
  core:
    enabled: true
  aris:
    enabled: true
    mode: subset
    selected_skills:
      - research-lit
      - research-refine
      - experiment-plan
      - result-to-claim

preferences:
  minimize_overlap: true
  prefer_low_dependency: true
  prefer_subset_installs: true
  allow_experimental_packs: false

supervision:
  mode: checkpointed
  risk_profile: balanced
  approval_policy:
    install_new_skills: ask
    update_registry: ask
    modify_config: ask
    launch_experiments: ask
    use_external_api: ask_first_use
    run_long_review_loops: ask
    create_commits: ask
  resource_policy:
    local_only: false
    cloud_allowed: false
    cluster_allowed: false
  budget_policy:
    enabled: true
    api_budget_level: medium
  execution_limits:
    max_review_rounds: 2
    require_confirmation_for_new_pack_imports: true

Required behavior after recommendation

Before writing the file:

Show the recommended preset or custom stack.
Show selected packs and selected skills.
Show supervision mode and any notable approval/resource settings.
Explain tradeoffs briefly.
Ask for confirmation.

After writing the file:

Tell the user where the config was written.
Summarize the chosen stack.
Mention that the file is intended to be hand-editable.

Constraints

Do not install external skills silently.
Do not mutate
```
skillpacks/skill_dictionary.yaml
```
; that belongs to
```
evolve
```
.
Do not recommend full-pack imports unless the user clearly asks for them.
Prefer simple, project-local configuration over hidden session-only state.