agentsclimarketplace

Capture

Skill gokhanamal/ai-context/skills/capture

Reusable AI context and installable skills for coding agents, broadly installable via skills.sh.

Install
npx -y skills add gokhanamal/ai-context --skill capture

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

  • 1 stars1 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

This skill should be used when a reusable insight, command, fix, or decision may need to be captured into the right artifact: an existing skill, a new skill, a lesson, or a solution document. It is especially useful after confirmed fixes, finalized decisions, explicit 'remember this' moments, or corrected CLI usage.

SKILL.md

3.3 KB, as published. Nobody here has run it

Capture

Detect when a recent moment is reusable knowledge, recommend the best place to capture it, ask for approval, and then update the approved destination safely.

Trigger

Activate only when at least one of these is true:

  • The user invokes this skill directly
  • The conversation includes an explicit capture cue such as:
    • "remember this"
    • "this should be a skill"
    • "we always forget this"
    • "the correct command is"
  • A workflow reaches a checkpoint such as:
    • a fix is confirmed
    • a decision is finalized
    • a non-obvious command or parameter is established

Do not activate continuously or speculatively. This skill is a checkpoint helper, not ambient monitoring.

Instructions

  1. Confirm the trigger moment and identify the candidate knowledge from recent context.
  2. Read references/target-selection.md to decide whether the best destination is:
    • an existing skill
    • a new skill
    • tasks/lessons.md
    • docs/solutions/...
    • ignore
  3. Before recommending a destination, check for an existing capture to avoid duplication.
    • Search matching skills with rg
    • Check tasks/lessons.md
    • Check docs/solutions/ if it exists
  4. Recommend one destination with a short reason and confidence statement.
    • If confidence is high for an existing skill, name that skill directly.
    • If the decision is ambiguous, present the top two options instead of bluffing certainty.
  5. Ask for approval using plain numbered choices so the flow works across platforms.
    • Example:
      1. Approve recommended destination
      2. Choose a different destination
      3. Ignore for now
  6. If the user approves a write, read references/edit-policy.md and follow the target-specific rules.
    • Existing skill: apply append-only updates directly after approval, but preview structural changes first.
    • New skill: create a minimal package under skills/<skill-name>/ plus a root README.md entry.
    • Lessons: append one short rule to tasks/lessons.md.
    • Solution docs: create docs/solutions/ on demand and write one compact solution document.
  7. Keep the update focused.
    • Prefer one primary destination per moment.
    • Add cross-links only when they materially help.
    • Do not write to multiple destinations unless the user explicitly asks.
  8. Summarize what changed and reference the edited files.

Guardrails

  • Do not write anything before approval.
  • Do not force a skill update when a lesson is the cleaner fit.
  • Do not rewrite a skill's core workflow without showing a preview first.
  • Do not create a new skill for a one-off project fact.
  • Do not claim certainty when the destination is genuinely unclear.

References

  • references/target-selection.md - destination heuristics, confidence rules, and duplicate checks
  • references/edit-policy.md - mixed policy for skill edits plus write templates for all destinations
  • references/examples.md - sample trigger moments, approvals, and destination outcomes

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.