agentsclimarketplace

Absorb

Skill andresnator/agents-orchestrator/skills/absorb

Install
npx -y skills add andresnator/agents-orchestrator --skill absorb

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Analyze one or more external projects (git URL or local path) to extract AI-harness practices, agent patterns, or instruction logic and contrast them against this repository's harness. Not for application code review.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

5.0 KB, as published. Nobody here has run it

Absorb

Activation Contract

Use this skill when the user wants to inspect an external repository or local project to learn from its AI-harness setup: agents, skills, prompts, orchestration rules, or instruction patterns.

Do not use it for application code review, bug hunting, architecture review of the product itself, or test-quality audits.

Required Input

  • One or more targets: git URL(s) and/or local path(s).
  • Optional focus: skills, agents, prompts, workflows, installer patterns, or comparison themes.

If no valid target is provided, ask for it before continuing.

Hard Rules

  • Analyze harness and agent configuration, not the external project's business code quality.
  • Verify practices from source files, activation points, or wiring; do not promote README claims as proven behavior.
  • Contrast every candidate practice against this repository before recommending adoption.
  • Prefer repo-native context for the contrast step: AGENTS.md, .ai/atl/skill-registry.md, domains/*/README.md, skills/*/SKILL.md, and relevant local artifacts.
  • When available, delegate read-heavy discovery to subagents or researcher passes instead of relying on memory.
  • Never auto-adopt, auto-commit, or silently edit this repository as a result of the audit.

Workflow

1. Scope and resolve targets

For each target:

  • If it is a local path, verify it exists.
  • If it is a git URL, clone or update it only with user awareness and to a clearly stated local destination.
  • Confirm the resolved target list before deep analysis.

2. Map each external harness

For each resolved target, extract:

  • AI entrypoints such as AGENTS.md, CLAUDE.md, command folders, agent folders, skill folders, config files, prompt files, and AI-related plugins.
  • A repo counts as a valid harness even without AGENTS.md, agents/, or skills/ trees: its AI surface may live primarily in plugins, runtime config handlers, and maintenance scripts.
  • How each candidate practice is triggered or wired.
  • Which practices are verified versus aspirational.

For every candidate practice, capture:

FieldWhat to record
PracticeClear name
MechanismFile path and concrete evidence
StatusVerified / partial / aspirational
StrengthWhy it helps
WeaknessLimitation or trade-off

3. Contrast with this repository

For each candidate practice:

  • Check whether this repository already has an equivalent or better approach.
  • Mark no adoption needed when the current harness already covers the need well.
  • Carry forward only genuinely better or meaningfully missing practices.

Also record at least three Our Advantages points to avoid cargo-cult adoption.

4. Adversarial filter

Challenge each surviving practice:

  • Is it truly verified?
  • Is it better, or just different?
  • Does it fit this repo's OpenCode-only, compact, contract-focused conventions?
  • Is the adoption cost proportional to the benefit?

Keep only ADOPT or CONDITIONAL items in the shortlist.

5. Deliver

Always write the report to:

.ai/absorb/YYYY-MM-DD-<slug>.md

where <slug> is a short kebab-case name of the analysis focus or target (fallback: external-practices). Create .ai/absorb/ if it does not exist.

The report should include:

  1. Sources analyzed.
  2. One section per external project.
  3. Verified strengths.
  4. Our Advantages.
  5. Ranked adoption shortlist with target file paths in this repository.
  6. Immediate text-only wins vs. larger follow-up work.

Only when the user explicitly asks not to persist, skip the file and return the same structure as a concise chat summary.

Red Flags

  • The provided path does not exist.
  • The user asks for general code quality review instead of harness analysis.
  • No AI-config surface is found in the external project.
  • Candidate practices are documented but not wired anywhere.

Surface these explicitly; do not fabricate findings.

Verification

  • Targets were resolved before deep analysis.
  • Each recommended practice has file-level evidence.
  • At least one no adoption needed or Our Advantages item was recorded.
  • The shortlist excludes unverified README-only claims.
  • The report file exists at the stated path (unless the user explicitly requested chat-only).
  • No commit or automatic adoption was performed.

Output Contract

Return:

  • resolved targets;
  • key verified practices;
  • rejected or conditional practices with reasons;
  • our advantages;
  • shortlist with proposed target paths in this repository;
  • report path (always, unless the user opted out of persistence).

Attribution

Created by abdi. Adapted for this repository by andresnator.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.