agentleFS
tt-a1itt-a1i/archify/.agents/skills/simplify-codebase/SKILL.md

simplify-codebase

Skill89k starsChanged 19 days agoApache-2.0View on GitHub

Simplification audit or authorized codebase simplification whose stated objective is to remove accidental complexity. Use for evidence-backed deletion or consolidation of dead code, duplicate state, redundant APIs or layers, ownerless abstractions, obsolete compatibility or design records, and over-engineering in any language; also use for 代码简化 or 熵回收. Do not use for general code review, onboarding, style-only refactoring, or performance tuning.

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 6 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Focused: when the user names a subsystem, symbol, state machine, dependency, or suspected duplication.

    line 21
  2. Distinctive:

    Broad: when the request spans the repository or asks for multiple candidates.

    line 22
  3. Distinctive:

    Read the repository's instructions, architecture and decision records, manifests, test guidance, and generated-file conventions.

    line 28
  4. Distinctive:

    Use repository-native search, compiler and linter output, dependency metadata, and history as discovery instruments.

    line 43
  5. Distinctive:

    If the user requests a simplification proposal, local cleanup annotation, or design-record consolidation, or if a selected change …

    line 75
  6. Distinctive:

    If the user asks to combine findings from another branch, pull request, task, or agent run, read integrating-findings.md.

    line 77

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “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

Simplify Codebase

Reduce the number of concepts and obligations a codebase must keep coherent. Line-count reduction is supporting evidence, not the objective. A successful run may conclude that the inspected surface is already justified.

Select mode and scope

First choose the authority mode:

  • Survey for simplification audit, investigation, or candidate-finding requests. Remain read-only and return ranked evidence.
  • Change for explicit simplify, remove, consolidate, refactor, or repository-documentation edit requests. Prove each cut, implement it within the authorized scope, and validate the surviving contract.

Then choose the coverage scope:

  • Focused when the user names a subsystem, symbol, state machine, dependency, or suspected duplication. Cover that boundary thoroughly before expanding outward.
  • Broad when the request spans the repository or asks for multiple candidates. Partition the system and account for every in-scope domain.

Deleting a reachable capability, supported interface, stored representation, or compatibility path is a product decision. Describe the consequence and obtain direction unless the user has already chosen it.

Establish the contract

  1. Read the repository's instructions, architecture and decision records, manifests, test guidance, and generated-file conventions.
  2. Inspect version-control state and preserve unrelated work. Identify vendored, generated, migration, fixture, and public-package surfaces before classifying them.
  3. Trace the runtime path from entrypoints through configuration, registration, dispatch, persistence, processes, and wire boundaries. Record public, dynamic, persisted, generated, and compatibility-sensitive contracts.
  4. In Change mode, discover the repository's real verification commands and capture a proportional baseline when feasible. A failing baseline narrows what the final checks can prove.

Preserve authorization, trust-boundary validation, security isolation, accessibility essentials, data-loss prevention, stored-format compatibility, and cleanup that establishes quiescence. Treat changes to these protections as their own explicitly authorized objective, not an incidental simplification.

The contract map is complete when all in-scope entrypoints and authority boundaries are enumerated, each is traced far enough to name its owner and observable contract, and every uninspected or externally unknowable surface is recorded as a blind spot.

Cover the relevant surface

For every Broad engagement, and for Focused work involving dynamic architecture or dependency substitution, read investigation.md. Build a coverage map before ranking findings; the first plausible deletion must not end the survey.

For concurrency, cancellation, readiness, cleanup, defensive copies, validation, authorization, security isolation, accessibility, data-loss prevention, or cross-process data, also read boundaries-and-lifecycle.md.

Use repository-native search, compiler and linter output, dependency metadata, and history as discovery instruments. Treat their findings as leads until runtime consumers and contracts have been examined.

Build a proof record

For every in-scope lead that reaches consumer-map evidence or could retire a meaningful contract, record:

Candidate: the exact contract, representation, or layer to remove or merge
Burden: the concepts, synchronization, publication, or testing cost it creates
Reachability: production, non-production, dynamic, external, and persisted consumers
Rationale: why it exists and whether that reason remains current
Cut: declarations, implementations, branches, artifacts, docs, and dependencies affected
Consequence: observable capability or compatibility behavior surrendered
Confidence / risk: evidence strength, uncertainty, blast radius, and reversibility
Proof: the smallest check that would expose an incorrect cut
Net effect: maintenance concepts removed minus replacement or migration machinery added

Prove cut boundaries below file granularity when the candidate shares an artifact with surviving consumers. Account for candidate-exclusive selectors, members, fields, keys, registry entries, generated fragments, and fixtures without disturbing the surviving owners.

Keep or downgrade the candidate when a real consumer exists, dynamic reachability remains unresolved, a current decision still owns the design, the change merely relocates complexity, the result is outside scope or retires no meaningful obligation, or the available check cannot distinguish success from accidental breakage.

Rank confidence separately from benefit. A high-value guess does not outrank a smaller proved cut.

Candidate proof is complete when every qualifying lead is classified as ranked, rejected, or unresolved, and every unresolved lead names the fact required to decide it.

Decide and act

In Survey mode, stop after reporting the ranked evidence. Include important rejected candidates when the rejection teaches something or identifies a concrete missing fact.

In Change mode, read execution-and-recovery.md and select the strongest authorized cut. One ownership boundary is the default batch size, not a run limit: for an explicitly requested set of cuts, finish and validate each boundary before starting the next.

If the user requests a simplification proposal, local cleanup annotation, or design-record consolidation, or if a selected change invalidates an ADR, RFC, design note, or architectural inventory, read decision-records.md. Do not turn an ordinary code audit into a repository-wide documentation purge.

If the user asks to combine findings from another branch, pull request, task, or agent run, read integrating-findings.md. Preserve evidence, not finding counts.

Deliver the result

For a survey, report coverage, ranked proof records, rejected or unresolved high-value leads, and the next fact needed for each uncertainty.

For a change, complete the validation and operation receipt defined in execution-and-recovery.md. Report each validation layer separately; a narrow green check does not establish broader runtime, deployment, or user acceptance.

Download now (ZIP)

Trending

Steady: 0 new projects took it in the last 4 weeks, none the 4 weeks before.thin data

Week of Aug 30: 0 new projectsWeek of Sep 6: 0 new projectsWeek of Sep 13: 0 new projectsWeek of Sep 20: 0 new projectsWeek of Sep 27: 0 new projectsWeek of Oct 4: 0 new projectsWeek of Oct 11: 0 new projectsWeek of Oct 18: 0 new projects
Aug 2026Sep 2026Oct 2026
New projects taking a version of this file each week, over the 8 weeks our latest run read: only 0 in all, so this is a hint, not a trend.

Who's talking about tt-a1i/archify

The biggest 5 of 111 mentions linking to the repository that our crawl found. Each line is their own headline.

See all 111

Talked about on GitHub, Bluesky, YouTube, the web and Hacker News (111 times).

On GitHub · 50

45 more on GitHub

On Bluesky · 25

20 more on Bluesky

On YouTube · 23

18 more on YouTube

On the web · 12

7 more on the web

On Hacker News · 1

  • Sep 7, 2026Archify1 point · 1 comment
  • Other versions4 projects use a version of this.

    Word for word · 0

    None found.

    Edited · 1

    1. tt-a1i/simplify-codebase★ 47474% same

    Wrote their own version · 3

    1. PANYUXIN6/pyx-skills★ 8same name
    2. dceoy/ai-coding-agent-skills★ 4same name
    3. betterlmy/agent-skills★ 2same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repository6 other files it gives its agents.

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.