Skip to content
Agentmart
Submit a tool

harness-effectiveness

0-arg harness-effectiveness report.

Skill by sh-ai-x

IndexedMIT★ 1
Write a review

Chalk Labs

Need help deploying harness-effectiveness?

Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.

Book a call

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/sh-ai-x-harness-effectiveness

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Get the weekly diff

The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.

We use your email only for this newsletter. Privacy

About

0-arg harness-effectiveness report. Wraps `lib.harness_effectiveness.build_report` and prints the four-component (prevention / first-pass / recovery / measurement-integrity) scorecard as JSON + a one-line status verdict. The measurement-integrity component also reports a nested stability submetric (issue

Use cases: documents, frontend

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/harness-effectiveness

Tools it pre-approves 2

From allowed-tools in SKILL.md: the agent may use these without asking you each time.

  • Read
  • Bash

What's inside

Skill name
harness-effectiveness
Description
0-arg harness-effectiveness report. Wraps `lib.harness_effectiveness.build_report` and prints the four-component (prevention / first-pass / recovery / measurement-integrity) scorecard as JSON + a one-line status verdict. The measurement-integrity component also reports a nested stability submetric (issue
License
MIT
Allowed tools
Read, Bash
Instructions
205 lines in SKILL.md · view on GitHub

Before installing

  • Pre-approves any shell command

Files (1)

  • SKILL.md

SKILL.md (first lines)

# /dev-kit:harness-effectiveness — workflow-effectiveness scorecard

## What it does

Invokes the deterministic `lib.harness_effectiveness.build_report(root)` reducer and prints the resulting JSON. The reducer consumes structured TraceLog events (guard actions, first-write verification, repair events, control/treatment cohorts, measurement-integrity signals) already emitted by the harness; **no LLM judge runs**, no transcript replay, no case fixture. Missing evidence is reported as `INSUFFICIENT_EVIDENCE` per component — it is never converted to a passing or zero score.

This is the standalone counterpart to the table that `/dev-kit:evaluate` embeds at the bottom of `.dev-kit/eval-report.md`. The difference is intent: `/dev-kit:evaluate` runs the 12-case judge pass + the 4-component report together; `/dev-kit:harness-effectiveness` runs only the 4-component reducer (cheap, sub-second, no API spend).

## Inputs (resolved at runtime, NOT user args)

| Variable | Source |
|---|---|
| `ROOT` | `Path(".").resolve()` — the worker's current worktree |
| `EVENTS` | `lib.trace_log.read_events(root)` — every JSONL event the harness has emitted into the worktree's TraceLog |
| `COMPONENT_WEIGHTS` | `lib.harness_effectiveness.COMPONENT_WEIGHTS` — single source of truth |

No flags. The slash is 0-arg by design.

## Four components

| Component | Weight | What it scores |
|---|---:|---|
| `prevention_quality` | 0.25 | guard block-rate vs. ground-truth-labelled guard events |
| `first_pass_quality` | 0.25 | write → first-verification-pass rate |
| `recovery_quality` | 0.31 | median iterations to recover from a verification error |
| `measurement_integrity` | 0.19 | TraceLog event_id uniqueness, schema-version compliance, dedup; nested `stability` submetric (issue #663) reports agent / model / provider swap behaviour |

`learning_quality` (treatment-vs-control cohort divergence) was removed
because its evidence class is unreachable from current producers — the
cohort-tagging instrumentation isn't wired into the build loop, so the
component reported `INSUFFICIENT_EVIDENCE` permanently. The remaining
four shippable components carry the unit weight and are observable
from evidence already on the production path.

Each component returns:
```
{
  "score": <float|null>,            # null when coverage < 0.90 or no evidence
  "weight": <float>,                # from COMPONENT_WEIGHTS
  "status": "OK" | "DRIFT_WARNING" | "ROT" | "INSUFFICIENT_EVIDENCE",
  "coverage": <float 0..1>,
  "submetrics": { ... },
  "evidence_event_ids": [ ... ],
  "findings": [ "<one-line reason>" ],
  "config_version": "harness-effectiveness-v1"
}
```

`overall_score` is `null` when **no** component has a score (i.e.
when every component reports `INSUFFICIENT_EVIDENCE`). Otherwise it
is the weighted sum of the scored components' scores, with both the
numerator and the divisor restricted to the scored set so a
partially-scored corpus reports a meaningful overall instead of
collapsing to `None`.

## Incremental measurement envelope (PR #817)

The 4-component reducer above remains the canonical
`harness-effectiveness` contract. A complementary bounded envelope is
available from `lib.effectiveness_collection.collect(root)` and is
exposed by this skill under the `envelope` field of the printed JSON.
The envelope:

* Carries `schema_version: 1` and `contract_version:
  "effectiveness-collection-v1"`.
* Reports `counts.{enrolled,closed,unresolved,paired,missing_start,
  missing_terminal,conflicting_terminal,unexpected_units,success,
  distinct_units}`.
* Reports `ratios.coverage` (paired / closed) and `ratios.success`
  (completed / paired).
* Reports `readiness` ∈ `READY` / `INSUFFICIENT_EVIDENCE` / `DEGRADED` /
  `COLLECTION_ERROR` / `NO_OPPORTUNITY` / `UNSUPPORTED` /
  `COVERAGE_BELOW_POLICY`.
* Identifies its `origin` (`runtime` or `ci-probe`) and `retention_floor`
  (oldest cohort still in the journal).
* Lists `findings` and `bounded_errors` separately so the reducer can
  surface what is missing vs what is broken.

The envelope is read-only with respect to the existing
`harness_effectiveness` reducer. Callers that want a hard gate check
the envelope's `readiness == "READY"`; callers that want quality
scores use the 4-component report above.

## Stability submetric (issue #663)

Nested under `components.measurement_integrity.submetrics.stability`. It is a
fifth dimension that is *not* a top-level weight — the four-component
`overall_score` formula is unchanged. The submetric carries:

- `coverage` (float 0..1) — minimum of the five dimension sub-coverage ratios
- `score` (`None` when coverage < 0.90 or evidence is missing; otherwise a
  float in [0, 100]). `None` is distinct from `0.0` — the reducer never
  collapses missing evidence into a zero score.
- `status` — one of `OK` / `DRIFT_WARNING` / `ROT` / `INSUFFICIENT_EVIDENCE`
- `findings` — list of one-line reasons (e.g. "agent/provider/model
  identity not recorded on any event")
- `evidence_event_ids` — event_ids the submetric saw, sorted + deduped
- `submetrics` — per-dimension breakdown:
  - `agent_identity_coverage`: fraction of events carrying agent / provider / model
  - `replay_compatibility`: fraction of events with monotonic timestamps
  - `agent_provider_neutrality`: fraction of events free of agent/provider/model keys in evidence_ref
  - `gate_portability`: fraction of distinct event_types with at least one neutral event
  - `contract_test_pass_rate`: fraction of `contract.test` events with outcome=`passed`

A new `INSUFFICIENT_EVIDENCE` constant is exported from
`lib.harness_effectiveness`; consumer code should compare against the
constant instead of hardcoding the string. `build_report` also bumps
`schema_version` from 1 → 2 so consumers can detect the new submetric
without breaking the 4-component contract.

## Output

The full reducer JSON is printed to stdout (one line per field, pretty-printed). Exit code is 0 on every successful invocation — this skill does not gate; the gating decision belongs to the caller. The one-line status verdict is **always** present in the JSON; callers that want a hard fail condition can check `status == "ROT"` and exit 1 themselves.

```
$ /dev-kit:harness-effectiveness
{
  "schema_version": 4,
  "contract_version": "harness-effectiveness-v1",

From sh-ai-x/dev-harness-kit, MIT.

More from sh-ai-x/dev-harness-kit

Signals

Stars
1
Forks
2
Open issues
8
Last commit (this folder)
Sep 25, 2026 (1 month ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from sh-ai-x

Done for you

Rather not set it up yourself?

Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.

See how we build AI agents →Read the case studies

Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.

launches shipped
50+
launches shipped
media placements
500+
media placements
founders on every account
2
founders on every account

Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block

Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.

Get harness-effectiveness running in your business

Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.

Goes to the Chalk Labs founders, not the newsletter. Privacy

Need help deploying harness-effectiveness?

CallGet help
Cite this page

harness-effectiveness skill. Agent Mart. https://agentmart.dev/skills/sh-ai-x-harness-effectiveness (last updated 2026-10-24)