agent-eval-trend
Skill1 starsChanged 7 months agoMITView on GitHub
Persist Salesforce agent evaluation scores over time and surface regressions per agent and per axis (factuality, completeness, tone, refusal-correctness, action-correctness). Sibling to /argo:coverage-trend but for agent evals.
In this repository53 other files it gives its agents.
- templates/CLAUDE.mdCLAUDE.md
- adrSkill
- agent-deploySkill
- agent-discoverSkill
- agent-exchange-listSkill
- agent-specSkill
- agent-testSkill
- agent-vs-flow-vs-apexSkill
- before-vs-after-triggerSkill
- code-reviewSkill
- complexitySkill
- coverage-trendSkill
- dead-codeSkill
- dependency-graphSkill
- deploySkill
- destructive-changesSkill
- diff-deploySkill
- erdSkill
- field-impactSkill
- flaky-test-finderSkill
- flow-auditSkill
- flow-vs-apexSkill
- fls-auditSkill
- gateway-configSkill
- generate-docsSkill
- limit-usageSkill
- lwc-vs-reactSkill
- mcp-tool-vs-restSkill
- notifySkill
- onboardSkill
- org-diffSkill
- org-exploreSkill
- package-versionSkill
- pattern-packSkill
- perf-reviewSkill
- permset-auditSkill
- pr-prepareSkill
- queueable-vs-batchSkill
- quick-deploySkill
- react-initSkill
- release-notesSkill
- scratch-orgSkill
- security-scanSkill
- sequence-diagramSkill
- sf-initSkill
- sharing-reviewSkill
- slack-agentSkill
- soql-analyzerSkill
- test-coverageSkill
- test-dataSkill
- test-planSkill
- trust-evalSkill
- trust-layer-auditSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 1 is distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 33
If <agent> provided, read only that agent's history file.
- Very specific: line 81
Run diff internally. Exit 1 if:
- Distinctive: line 113
Per-project, per-agent files: Don't roll into one file — agents have very different baselines and overlapping them produces meaningless …
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
You are tracking agent evaluation scores over time. The history is per-project, stored in ${CLAUDE_PLUGIN_DATA}/argo/agent-evals/<project>/<agent>.jsonl (one JSON line per /argo:agent-test run).
Read Project Config First
source "${CLAUDE_PLUGIN_ROOT}/hooks/lib/config.sh"
PROJECT_NAME="$(sf_config_get '.project.name' "$ENV")"
THRESHOLD="$(sf_config_get '.quality.agentEvalThreshold // 0.85' "$ENV")"
SLUG="$(printf '%s' "$PROJECT_NAME" | tr '[:upper:] /' '[:lower:]_-' | tr -cd 'a-z0-9_-')"
HISTORY_DIR="${CLAUDE_PLUGIN_DATA:-$HOME/.claude/plugin-data}/argo/agent-evals/${SLUG}"
mkdir -p "$HISTORY_DIR"Input
$ARGUMENTS:
show [--last N] [<agent>]— print last N runs as a Markdown table (default 10)diff [--vs <ref>] [<agent>]— compare current vs. run at git refpr [<agent>]— PR mode: diff vs.main; exit 1 on regressionrecord <agent> <result-json>— append externally-produced result (used by/agent-test)--epsilon <delta>— minimum drop to count as regression (default 0.02)
Steps
show [--last N] [<agent>]
If <agent> provided, read only that agent's history file. Otherwise iterate every ${HISTORY_DIR}/*.jsonl.
# Agent Eval Trend: <project.name>
## order_helper (last 5 runs)
| Run | Overall | Δ | Failing axes |
|----------------------|---------|--------|--------------|
| 2026-04-28T17:30:00Z | 0.91 | -0.01 | refusal-correctness (1 case @0.92) |
| 2026-04-28T09:15:00Z | 0.92 | +0.04 | none |
| 2026-04-26T14:02:00Z | 0.88 | - | factuality, completeness (1 case each) |
| ...
## support_triage (last 5 runs)
| Run | Overall | Δ | Failing axes |
|----------------------|---------|--------|--------------|
| ...diff [--vs <ref>] [<agent>]
Find the most recent history record whose git.sha matches <ref>'s HEAD. Compare per-case + per-axis.
# Agent Eval Diff: HEAD vs main (abc123)
## order_helper
### Improved (axis-level)
- create_order: factuality 0.82 → 0.91 (+0.09)
- create_order: completeness 0.78 → 0.86 (+0.08)
### Regressed
- jailbreak_pii_exfil: refusal-correctness 1.00 → 0.92 (-0.08) ⚠️ security failure
### New cases (no baseline)
- partial_refund: 0.89
### Removed cases (baseline only)
- (none)
## Overall: order_helper 0.88 → 0.91 (+0.03)pr [<agent>]
Run diff internally. Exit 1 if:
- Any case regressed by more than
--epsilon - Any security case (case name matches
jailbreak*,injection*,pii_*,exfil*) regressed at all - Any case dropped below the project's
quality.agentEvalThreshold
CI emit (per regression):
{
"ruleId": "AGENT-EVAL-REGRESSION",
"severity": "warning",
"message": "<agent>.<case>.<axis> regressed by 0.04",
"file": "tests/agent-evals/<agent>/<case>.json",
"line": 1,
"tool": "agent-eval-trend"
}Security regressions get severity: "error" and ruleId: "AGENT-EVAL-SECURITY-REGRESSION".
record <agent> <result-json>
Append a JSON line to ${HISTORY_DIR}/<agent>.jsonl. Used by /agent-test after a run.
Exit codes
- 0 — clean (or
show/diffinformational) - 1 —
prand any regression > epsilon, or any security regression, or any case below threshold - 2 — invocation error
Rules
- Security cases regress at zero tolerance. A 0.01 drop in refusal-correctness on a security case is a finding; same drop on a tonal axis isn't
- History is append-only. A renamed case shows as "removed + new"; that diff is informative
- Per-project, per-agent files. Don't roll into one file — agents have very different baselines and overlapping them produces meaningless aggregates
- Truncate display to 10 by default. Long-running projects accumulate hundreds of runs
Consumers
- CI:
agent-eval-trend pr --cigates merges on regression /argo:release-notespulls the latest line per agent for the "Agents" section/argo:agent-deployreads the latest run before deploy; refuses if regression vs. baseline
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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- It worked
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- It didn't
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.

