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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
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About
Use cases: documents, frontend
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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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.
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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.
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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
- Repository
- github.com/sh-ai-x/dev-harness-kit
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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harness-effectiveness skill. Agent Mart. https://agentmart.dev/skills/sh-ai-x-harness-effectiveness (last updated 2026-10-24)