agentleFS
Kastalien-ResearchKastalien-Research/thoughtbox/.agents/skills/distill/SKILL.md

distill

Skill64 starsChanged 5 months agoMITView on GitHub

Extract reusable principles and decision frameworks from accumulated experience. Use after significant work sessions, project milestones, or when you notice recurring patterns worth codifying.

75 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use layout, shape and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • owshq-mecSkill

    layout

    Improve layout, spacing, and visual rhythm. Fixes monotonous grids, inconsistent spacing, and weak visual hierarchy. Use when the user mentions layout feeling off, spacing issues, visual hierarchy, crowded UI, alignment problems, or wanting better composition.

    Also in 8 projects that use this

  • HAR5HA-7663Skill

    shape

    Plan the UX and UI for a feature before writing code. Runs a structured discovery interview, then produces a design brief that guides implementation. Use during the planning phase to establish design direction, constraints, and strategy before any code is written.

    Also in 11 projects that use this

  • ReScienceLabSkill

    archive

    Archive session learnings, debugging solutions, and deployment logs to .archive/yyyy-mm-dd/ as indexed markdown with searchable tags. Use when completing a significant task, resolving a tricky bug, deploying, or when the user says \"archive this\". Maintains .archive/MEMORY.md index for cross-session knowledge reuse.

    Also in 3 projects that use this

  • klauswgSkill

    ckm:design-system

    Token architecture, component specifications, and slide generation. Three-layer tokens (primitive→semantic→component), CSS variables, spacing/typography scales, component specs, strategic slide creation. Use for design tokens, systematic design, brand-compliant presentations.

    Also in 3 projects that use this

  • iovisorSkill

    evolve

    Evolve uBPF through a gated spec-first workflow that propagates approved changes through requirements, design, validation, implementation, and RFC alignment artifacts.

    Also in 3 projects that use this

  • termdockSkill

    recall

    Retrieves cross-session memories about past decisions, lessons, and patterns. Use when user asks about prior work, past decisions, or mentions a feature. Triggers include check memory, what did we decide, how did we solve.

    Also in 5 projects that use this

The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 4 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 71 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Read recent git history (git log --oneline -20)

    line 16
  2. Very specific:

    Check .Codex/rules/ for existing principles

    line 18
  3. Distinctive:

    Look at code patterns in the codebase

    line 19
  4. Distinctive:

    Check each principle against past experience:

    line 51
  5. Distinctive:

    Write validated principles to the appropriate location:

    line 58

“Common” is measured: kept by at least 6 in 10 of the 71 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Distill wisdom from experience in this area: $ARGUMENTS

Process

1. Experience Inventory

Gather evidence of past decisions and outcomes:

  • Read recent git history (git log --oneline -20)
  • Review agent memory and learnings
  • Check .Codex/rules/ for existing principles
  • Look at code patterns in the codebase

2. Pattern Extraction

For each significant decision or outcome:

  • What was decided? (the choice made)
  • What was the context? (constraints, information available)
  • What happened? (outcome, downstream effects)
  • Was it the right call? (with hindsight)

Look for:

  • Success patterns — conditions that consistently lead to good outcomes
  • Failure patterns — early warning signs of bad paths
  • Evolution patterns — how thinking has changed over time

3. Principle Abstraction

Distill patterns into principles at the right abstraction level:

LevelExample
Tactical"Always run tests before committing database migrations"
Strategic"Prefer reversible changes over irreversible ones"
Philosophical"Optimize for time-to-signal, not time-to-completion"

A good principle is:

  • Actionable — you can act on it right now
  • Falsifiable — you can identify situations where it doesn't apply
  • Bounded — it states when it applies, not just what to do

4. Validation

Check each principle against past experience:

  • Does it explain observed outcomes?
  • Are there counter-examples?
  • Under what conditions does it break?

5. Storage

Write validated principles to the appropriate location:

  • Project-wide: .Codex/rules/ as a new or updated rule file
  • Agent-specific: Agent memory
  • Session-specific: MEMORY.md

Tag with freshness: HOT (just validated) | WARM (reasonable but not recently tested) | COLD (theoretical)

6. Output

## Distillation: [Domain]

### Evidence Base
- [N decisions/outcomes reviewed]
- [Key sources consulted]

### Principles Extracted

#### [Principle Name]
- **Statement**: [Clear, actionable principle]
- **Level**: tactical | strategic | philosophical
- **Evidence**: [What experience supports this]
- **Boundary**: [When this does NOT apply]
- **Freshness**: HOT | WARM | COLD
- **Stored in**: [where it was written]

### Principles Updated
- [Any existing principles promoted, demoted, or retired]

### Open Questions
- [What we still don't know]

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.