Diagnose

diagnose — failures into actionable side information

A scalar reward says how much a candidate failed; it does not say why, and "why" is the only thing an editor can act on. diagnose converts raw traces into the signal the optimizer edits against — the textual analogue of a gradient. In reinforcement learning a scalar reward back-propagates into weight updates; here, natural-language feedback back-propagates into prompt/tool/skill edits. The richer that feedback, the larger the update you can extract from a handful of rollouts.

Inputs / outputs (manifest tokens)

• needs: scores, traces — the per-task rewards and rollouts from

evaluate (with the scorer's textual feedback).

• provides: reflective_dataset — the structured "what to change and why"

that the algorithm injects into the optimizer's proposal prompt.

What it produces

• reflective_dataset: for each failing task, `{Inputs, Generated Outputs,

Feedback}` — GEPA's reflective-dataset shape. This triple lets the optimizer see the task, what the agent actually did, and the diagnosis, instead of a bare score.

• clusters: ALL failing trajectories grouped by shared root cause, so the

optimizer can fix a whole class of failure with one principled edit rather than patching tasks one at a time (and overfitting to each). "Failing" here is not only zero-score tasks — explicitly include partial-credit failures (a task that scored e.g. 0.5 because a write got the wrong arguments) and communication / omission failures (the agent did the action but failed to report or confirm the required information). These are real lost score and are routinely missed when only total failures are clustered. Each cluster is ranked by cost — how many tasks (and how much aggregate score) it accounts for — so the biggest lever is addressed first. Cost-rank, but DON'T let a small cluster that needs a STRUCTURAL fix (a new composite tool) get buried under task-count: a 2-task stall cluster cracked by one new tool can outweigh a many-task cluster of cosmetic misses. Each cluster is tagged with ONE of three, so the optimizer knows where the fix belongs:

• KNOWLEDGE — the agent doesn't know a format/rule/criterion → fix in the prompt.
• BEHAVIORAL — the agent KNOWS the rule but violates it on a tool that exists

→ fix in that EXISTING tool's body (an in-code guard).

• DECISION / PERMISSION (ACT vs REFUSE) — a cluster about *whether the agent should

ACT or REFUSE/escalate* on a policy-governed class (it acted where it should have refused, or refused where it should have acted). Tag it SEPARATELY so it is NOT "fixed" by loosening a global decision/permission/refusal rule in the prompt — a global change flips behavior for the ENTIRE class and regresses every currently-passing task where the original behavior was gold (UNBOUNDED blast radius; this exact mistake sank a prior run). Fix = the EXACT discriminating CONDITION: ideally an in-body guard (tools) that refuses/raises only on the qualifying predicate; or prose-only, a NARROWING rule. Its blast radius is EVERY passing task in the decision class — name them and confirm the fix won't flip their action.

• CAPABILITY-GAP — the agent has NO reliable way to do the thing, or it

narrates/confirms a multi-step action then STALLS and never executes it → fix with the strongest STRUCTURAL lever the SELECTED capability offers (per its ./guidance//SKILL.md): for a tools capability, a NEW code-bearing tool (a composite atomic-WRITE / loop / validation tool); for a prompt/skill capability, an explicit step-by-step procedure or worked example that makes the step unavoidable. This is its own tag precisely so a stall/gap cluster is NOT mis-filed as BEHAVIORAL and "fixed" with a weak nudge that never works.

• kept_good: tasks already passing — the set the gate's no-regression check

must protect, so a fix for one cluster does not silently break a passing task.

The signal the optimizer iterates on (analyze → ideate → edit)

The reflective dataset feeds a fixed three-step shape the algorithm encodes into the per-iteration optimizer INSTRUCTIONS — the optimizer must analyze before it edits, never patch blindly:

1. Analyze the trajectories DEEPLY first. Read the traces closely (not a

skim) alongside the current capability, and name (a) the MAIN RECURRING root-cause clusters — the rules and workflows the agent botches, the steps it skips, the tools it mis-uses or repeats N times. Cluster ALL failing trajectories by shared root cause, not only the zero-score ones: include partial-credit failures (wrong arguments to a write, a missed required action) and communication / omission failures (the action was done but the required info was never reported/confirmed) — these lose real score and are easy to overlook. Rank the clusters by how much they cost (tasks affected × score lost), biggest first, with evidence. For each cluster, tag it KNOWLEDGE (lacks a format/rule/criterion → prompt), BEHAVIORAL (knows the rule but violates it on an existing tool → in-code guard in that tool's body), DECISION / PERMISSION (wrong ACT-vs-REFUSE call on a policy-governed class → discriminating-condition guard, NEVER a global prompt rule change: a global change regresses the whole class; the blast radius is EVERY passing task in that decision class, so name them and confirm the fix doesn't flip their action), or CAPABILITY-GAP (no reliable way to do it, or stalls at the action boundary and never executes → the strongest STRUCTURAL lever the SELECTED capability offers per its guidance: a NEW composite/loop/validation tool for a tools capability, or an explicit procedure/worked example for a prompt/skill capability): more prose alone will not fix a behavioral OR capability-gap cluster — the first needs the rule in code/structure, the second a new mechanism that makes the action un-skippable. Also surface a NEAR-MISS cluster: tasks scoring high-partial (≈0.7–0.9) that need only one small correct change to flip to a pass — these are the cheapest marginal gain per edit and are easy to miss when you focus on zero-score tasks. For a multi-cause task, note its secondary cause too (a residual second-pass), so one candidate can fix both the primary and the residual cause in the same edit. For EACH cluster, also note its blast radius — the currently-PASSING tasks whose behavior the fix would change. For a tool-body guard that is the tasks exercising the same tool/rule/path; for a DECISION / PERMISSION cluster it is EVERY passing task in the same decision class (a global rule change would flip all of them). Scope the fix to fire only on the failing condition and confirm it does not change the action on any passing task in that radius; and cross-check the run history (LEDGER / prior iterations) to SKIP any cluster whose fix was already tried and rejected (don't re-diagnose a refuted approach). A cluster's value is the score it can recover MINUS the regression risk to its blast radius. List concrete passing task ids per cluster (not just "the same tool") whenever you can, so the edit can be scoped to fire only on the failing condition and checked against those passing tasks. Then name (b) the GOOD behaviors that occur only sometimes (tasks whose mean reward is between 0 and 1 pass on some trials and fail on others); identify what the good runs do so it can be made CONSISTENT. (Always-failing tasks, mean ≈ 0, are a root-cause fix; flaky tasks are a consistency/reinforcement fix — a different edit. The per-task Feedback line is from the last trial and can disagree with a graded mean; the reward is the honest signal.) If your coding agent supports parallel sub-agents, fan them out — one per failure cluster or per candidate-edit hypothesis — to analyze concurrently, then synthesize; it makes each costly iteration deeper and faster. Use ground-truth / eval signals when the traces provide them. Some benchmarks copy ground-truth / expected actions / a reward breakdown into the trajectories; when PRESENT, use them to pinpoint exactly what went wrong — which action, argument, or value was expected vs what the agent did. You will NOT always have this: if you do, use it; if not, infer from the traces + feedback. CRITICAL: ground truth is for UNDERSTANDING the failure class only — the resulting EDIT must still be a GENERAL rule, never a copied gold value (see the no-leak rule).

1. Then ideate a DRASTIC, generalizing edit. Each iteration is costly

(optimize + full eval is long), so aim for a big root-cause improvement, not a tiny tweak: propose the single best targeted edit (or tight set) that addresses the biggest cluster from (a) and reinforces (b) — concrete, generalizing across the class, never a one-off patch to one task. When the capability is the agent's own tools, PREFER writing a new code-bearing tool over a docstring tweak — a deterministic tool can't be forgotten the way a prompt rule can: wrap a primitive to enforce a general rule (then remove the raw primitive), or collapse a recurring multi-step workflow into one looped tool. Write a real body, not ....

1. Then edit and stop. Apply it; the harness re-scores. Be economical — no

narration, no exploring unrelated files, do exactly what's needed and finish.

Turning traces into an actionable signal (the real work)

A good diagnosis is specific, causal, and general:

• Specific: name the concrete failure (the wrong tool call, the missing step,

the misread field), not "the answer was wrong".

• Causal: point at the decision that caused it, so the edit has a target.
• General: describe the pattern, not the instance. Feedback that quotes the

gold answer turns the optimizer into a memorizer of the eval set and corrupts the held-out number — keep it at the level of "what class of mistake", never "here is the solution".

Clustering is what makes the signal actionable at scale: ten tasks failing for one reason should drive one edit, not ten. A flat list of failures invites one-off patches that raise val by overfitting; a clustered list invites a single generalizing change. This mirrors the lineage of "diagnose-then-edit" optimizers (GEPA's actionable side information; trace-analysis approaches that run parallel analysts; issue-clustering in evolutionary loops).

Dual-mode

This phase runs two ways from the same SKILL.md: standalone as the slash command /cap-evolve:diagnose (the argument-hint shows its run.py args), and orchestrator-callable — cap-evolve run / the orchestrate skill invokes the same scripts/run.py headlessly and threads the run dir between phases.

How to run

python scripts/run.py --run-dir .capevolve/run_XXXX --tag seed

Run it on the current best's val rollouts each round; feed reflective_dataset + clusters into the algorithm's proposal prompt, and hand kept_good to the gate.

What good vs bad looks like

• Good: failures grouped into a few clear clusters; feedback names the cause

and the pattern; the biggest cluster is addressed first; gold answers never appear in feedback.

• Bad: one cluster per task (no generalization); feedback that just restates

the reward; the gold answer leaked into feedback (the optimizer memorizes the eval); kept_good ignored so fixes regress passing tasks.

References

• references/concepts.md — the reflective dataset, feedback as a textual

gradient, why clustering beats per-task patching, the no-leak rule, and the optimizer lineage, with sources.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: skillberry-ai
• Source: skillberry-ai/cap-evolve
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.