agentsclimarketplace

Calibration

Skill GhostlyGawd/recursive-harness/skills/calibration

Portable, evidence-driven agent development harness for Codex, Claude Code, and generic Agent Skills. Active beta v0.1.2.

Install
npx -y skills add GhostlyGawd/recursive-harness --skill calibration

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

  • 3 stars3 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

Predict-then-score protocol. Use at the START of any non-trivial task (multi-file change, debugging session, design decision, anything you could be wrong about) to log a falsifiable prediction, and at the END to score it. Also use when the user asks "how confident are you", when choosing between approaches, or when /calibrate reports overconfidence in a category. This protocol IS the harness's verified self-awareness; skipping it makes your judgement unmeasurable.

SKILL.md

4.8 KB, as published. Nobody here has run it

Calibration

Self-awareness you can't audit is vibes. This protocol replaces it with a ledger.

Before acting

State, concretely, what success looks like — then log it:

harness predict --task "fix flaky auth test" \
  --expect "root cause is async teardown; <=2 files touched; suite green in one run" \
  --confidence 0.7 --category debugging

A good --expect is one reality can contradict: file counts, pass/fail, the named root cause, "user accepts without edits". "I will do a good job" is not a prediction, it's a mood.

Beware the self-confirming --expect: a clause that asserts your OWN output is correct ("surfaces the KNOWN X", "matches the map I built") makes a "hit" prove only that the code reproduced your assumption — not that the assumption was right. Tie at least one clause to something you did NOT author: user-confirmed intent, external ground truth, an independent check. (2026-06-21: scored a "hit" on "overlap detection surfaces the then-known brainstorm+Huashu overlap" — the "known" was my own hand-label, later admitted an artifact; the hit certified a mislabel.)

Confidence honesty: 0.9 means 9-in-10. If your high bucket hits at 60%, you are not unlucky, you are lying to yourself with extra steps.

After acting

harness outcome <id> --result hit|miss --notes "actual cause was fixture ordering"

Score EVERY prediction, especially misses — misses are where the information is. The SessionStart banner shows your unscored count; keep it near zero.

Don't self-grade the user's acceptance

If a prediction's success clause includes the user accepting something ("user accepts", "<=2 rounds of correction", "no edits requested"), you may NOT score it until the user has actually responded to that thing. Self-declared acceptance is not an outcome — it is you grading from your own seat. Until the user reacts, the prediction stays pending (open debt), never a self-awarded hit.

Corollary: a prediction that bundles a mechanical clause (the loop runs) with an acceptance clause (the user accepts the design) can be a mechanical HIT and an acceptance MISS at once. Log them as TWO predictions up front (the outcome CLI scores one result per id), or wait for the user — a green mechanical result must not let you round the acceptance clause up to "hit".

provenance: session 3772bd2d, 2026-06-20 — scored prediction f156c20b a "hit" (its clause: "user accepts ... with <=2 rounds of correction") minutes BEFORE the user delivered a fundamental, design-invalidating correction. Acceptance was self-graded from inside Claude's own frame; the real outcome was a miss on that clause.

A load-bearing prediction gates shipping

If a prediction underwrites the DELIVERABLE — its core behavioral claim, the thing the user actually asked for — then while it is still pending or scored miss, the work is NOT done and must NOT merge. Green gates (tests, lint, a passing auditor, an eval run) verify the ARTIFACTS; they do not verify the CLAIM. Resolve it by exercising the REAL path end-to-end — the actual tool / trigger / environment, never a proxy — then score it before declaring done or merging. Shipping on a self-flagged "unverified" load-bearing claim is how a green PR delivers the opposite of the goal. (2026-06-20: merged a "rides into worktrees" change while its own prediction 55b1735b said that exact behavior was unverified; post-merge it scored a MISS and the whole goal had to be re-delivered.)

Reading your own stats

harness stats reports two lenses on the same scored log — confidence buckets (claimed vs. actual hit rate, auto-flagged on large drift) and a per-category hit-rate breakdown (no flag; it always prints both):

  • Overconfident in a category → before acting in that category, list two ways you could be wrong and check one. Note persistent drift in memory/calibration/notes.md with a date.
  • Underconfident → stop hedging and over-asking; act, then verify.
  • Misses clustering on one category → that's a missing skill or hook. Route it (skill: routing-learnings).

Wrong-track detection in flight

While working, your prediction is the tripwire: the moment observed reality diverges from --expect (extra files ballooning, new failure class, the "quick fix" sprouting branches), stop and re-plan instead of pushing through. That divergence-noticing reflex is the intuition this harness trains.

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.