agentleFS
Anuj7411Anuj7411/sipcode/skills/impact/SKILL.md

impact

Skill46 starsChanged 31 days agoMITView on GitHub

A/B compare the user's actual token spend before and after they installed Sipcode's optimizers — on their own session data. The honesty-first savings verifier. Use when the user asks "is sipcode actually saving me tokens?", "show me the impact", "prove sipcode is working", or any variant of "is this thing worth running?

16 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use debug and docs.

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.
  • AnthropicSkill

    debug

    Structured debugging session — reproduce, isolate, diagnose, and fix. Trigger with an error message or stack trace, "this works in staging but not prod", "something broke after the deploy", or when behavior diverges from expected and the cause isn't obvious.

    Also in 4 projects that use this

  • Microsoft AzureSkill

    docs

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 16 edited versions we read keep these.

  • 0

    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. Very specific:

    Call the sipcode:verify_sipcode_impact MCP tool to produce a structured before/after comparison.

    line 7

“Common” is measured: kept by at least 6 in 10 of the 16 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

Sipcode — Impact (the savings verifier)

Call the sipcode:verify_sipcode_impact MCP tool to produce a structured before/after comparison.

Critical: respect the integrity contract. The tool's response is structured with these key fields:

  • status — one of: "measured", "insufficient-post-data", "no-install-marker", "no-baseline", "no-post-sessions", "window-asymmetry-<N>d-vs-<M>d"
  • delta — present (numbers) ONLY when status === "measured". Null in every other case by design. When delta is null, DO NOT compute a savings number from before/after totals. The windows aren't comparable.
  • allTime — present only when no marker exists. Contains the user's total session count across all time, so the user sees their data exists even when no A/B split is possible.
  • warningReason — explains why delta is null (e.g., "window-asymmetry-39d-vs-2d", "insufficient-post-data-2d-vs-min-3d").
  • headline — the canonical one-line summary; trust this and surface it.

When status === "measured": lead with the output-ratio change (the only normalization-resistant metric). Mention token + dollar savings as secondary. Include the window-length caveat.

When status !== "measured": present the headline and the next-step recommendation from the notes field. Do not invent a savings number. The tool's delta: null is structurally enforced — do not work around it.

If the user pushes back and asks you to compute a number anyway, refuse and explain the structural reason: the comparison windows aren't fair, so any delta would be misleading. Suggest they pass since: "YYYY-MM-DD" to set a manual pivot, or run sipcode rules --install to start measuring forward.

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.