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Coordination conformance

Skill wildcard/codex-to-claude-coordinator/skills/coordination-conformance

Bidirectional Codex and Claude coordination skills and plugins for reviewed, evidence-driven multi-agent delegation.

Install
npx -y skills add wildcard/codex-to-claude-coordinator --skill coordination-conformance

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

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  • 16 days oldThe repository was created 16 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 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

Test and compare agent-harness coordination capabilities with privacy-safe evidence. Use to record, redact, validate, and summarize whether Codex, Claude, Cursor, Copilot, Goose, OpenHands, or another coordinator or worker can start, steer, inspect, stop, review, preserve scope, expose usage, or produce a trustworthy handoff.

SKILL.md

2.8 KB, 517 tokens by cl100k_base, as published. Nobody here has run it

Coordination Conformance

Test harness capabilities through local, privacy-safe evidence. Read references/evidence-policy.md before collecting authenticated product observations.

Prepare the Run

  1. Define one falsifiable capability question and exact harmless input.
  2. Use a disposable fixture with no credentials, personal data, remotes, connectors, or unrelated transcripts.
  3. Create a run directory outside the installed skill. Keep raw captures outside the repository.
  4. Record unsupported, unobserved, and untested behavior distinctly.

Redact and Record

Run scripts/redact.py before admitting screenshots, transcripts, or logs. Image evidence requires an opaque rectangle and a .redacted derivative. Image redaction requires Python 3.9 or newer and the dependency in requirements.txt; text redaction, recording, validation, and summarization use only the Python standard library.

Build an observation body as JSON, then use scripts/record.py to append it. The recorder hashes evidence, rejects raw screenshots, scans text for common identifiers and secret patterns, and keeps timestamps append-only. Pass --privacy-checked only after manually inspecting every derivative; the recorder does not infer that review. Use --evidence-source synthetic for harmless generated fixture artifacts; redacted evidence is always recorded as a redacted-derivative.

Validate and Summarize

Run:

scripts/validate.py <observations.jsonl> --root <run-directory>
scripts/summarize.py <observations.jsonl> --run-id <run-id> --root <run-directory>

Trust a capability only when its operation has a passing observation and traceable evidence. The summarizer preserves missing operations as unknown; it never converts absence into unavailable or support.

For quota-like signals, set threshold_eligible only when the value has explicit model scope, consumed direction, a percentage, reset or window semantics, and known freshness. For stop behavior, archive or idle never passes without a terminal worker state.

Preserve the Boundary

  • Do not automate consequential approval prompts.
  • Do not retain raw screenshots in the repository.
  • Do not accept absolute or traversing evidence paths.
  • Do not record account identifiers or machine-specific home paths.
  • Do not collapse per-operation results into one compatibility score.
  • Keep vendor-specific inputs and expected states in the experiment plan, not in this skill's reusable policy.

What ships with it: 9 files

32.3 KB alongside SKILL.md, 4 of them executable

agents/

references/

schemas/

scripts/

Gives 0 of the 12 instructions most context ai engineering skills give in 517 tokens

Counted across 1,193 of the 1,976 authors here whose files we hold, read 2026-08-07

  • Dispatch a fresh implementer subagent per taskin 48 of 1193, across 19 files
  • Dispatch a final code reviewer after all tasksin 33 of 1193, across 8 files
  • Provide full task text to the subagentin 30 of 1193, across 9 files
  • Review spec compliance before code qualityin 27 of 1193, across 10 files
  • Make the hook script executablein 26 of 1193, across 8 files
  • Re-snapshot after navigation or DOM changesin 25 of 1193, across 19 files
  • Read files before editing themin 22 of 1193, across 11 files
  • Answer subagent questions before proceedingin 22 of 1193, across 7 files
  • Mark task complete in TodoWrite after approvalin 22 of 1193, across 6 files
  • Merge hook into existing settingsin 21 of 1193, across 3 files
  • Ask if installation is global or projectin 20 of 1193, across 2 files
  • Copy the hook script to target locationin 20 of 1193, across 2 files

Said here and by no other author read

  • define one falsifiable capability question
  • use a disposable fixture without credentials or personal data
  • keep raw captures outside the repository
  • record unsupported, unobserved, and untested behavior distinctly
  • run redaction scripts before admitting any evidence
  • append observation bodies as JSON

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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