Source-backed example. Practical decision guidance is proposed and untested; this is not a full implementation guide.
The problem
Recurring session errors and user corrections can expose weak skills, but the patterns are difficult to turn into specific repair proposals.
Agent job
Analyze failed sessions and correction signals, propose skill changes and record comparisons while respecting protected bundled skills.
Expected output
Proposed patches or new skills and a comparison report.
Where this framing comes from
Core problem: Editorial purpose framing
Agent job: Editorial purpose framing
Expected output: Editorial purpose framing
Relevant job roles: Editorial purpose framing
Benefit type: Editorial purpose framing
Editorial framing and outcome limits
Describes the task’s purpose and intended output; measured benefit, safe unsupervised operation and successful delivery are not established by this framing.
Editorial synthesis based on the linked source. This framing does not add evidence of successful delivery or results.
Case-specific decision guidance · untested
Case-specific decision guidance and first checks are original proposals. The linked source supports the attributed task; this added guidance has not been executed.
Repeated session failures may point to a weak instruction, missing capability or unsuitable task rather than one simple defect. Automatically rewriting a skill from a few corrections can overfit those examples and damage behaviour that already works.
This job could organize failure evidence into narrowly scoped repair proposals and comparison criteria. Separating diagnosis, candidate edits and evaluation decisions would let maintainers judge whether a change addresses the cause without expanding authority or altering protected material.
Why an agent / simpler alternative
Useful judgment
Cluster error patterns and connect them to specific skill instructions while preserving alternative explanations.
Simpler alternative
Use an issue log and a manual patch when one reproducible failure has a clear cause.
Unsuitable when
The analysis requires unauthorized session data or modifying protected bundled instructions.
Proposed inputs
Name
Authorized failure samples
Owner
System owner
Minimum scope
Redacted errors, corrections and relevant task context
Quality check
Exclude credentials and separate user feedback from untrusted content.
Name
Editable skill scope
Owner
Maintainer
Minimum scope
Owned skill versions, protected paths and expected behaviours
Quality check
Confirm which instructions may be proposed for change.
Access and setup — proposed or unknown
Proposed minimum
Inspect supplied logs and editable-skill copies; generate candidate diffs without applying them.
Compatibility unknowns
Unknown: log schema, evaluation tooling, version support and local data access.
Editorial relevance from the title and source-supported actions; not evidence of the operator’s occupation.
Hermes AgentIndependent testing: No
Editorial candidate. This is source-backed reporting or a documented workflow. BotCurrent has not independently tested this task. A proposed Canadian adaptation is not evidence of a Canadian deployment or compliance certification.
Analyzes session errors and correction signals, identifies weak or missing skills, proposes patches or new skills, invokes evolution and records a comparison report.
Source outcome limits
This community recipe describes a procedure; it does not prove successful execution or independently reproduced results.
Evidence attribution caveat
This community recipe describes a procedure; it does not prove successful execution or independently reproduced results.
Source approval description
Source says never modify bundled skills and flags small improvements for manual review; broader changes are automatic in its example.
Restrict analysis to approved sessions and redact Canadian personal/business data before external model evaluation.
Recommended approval boundaries
Review proposed skill changes, tests, evaluation data, model spend and rollout; require a reversible branch or backup.
More examples of this task
Source-backed approaches using this or another agent. Not independently tested.
Claude Code
Build an evaluation and improve an agent against held-out cases
The claude-api skill guides Claude Code to assemble representative cases, calibrate a grader and run a baseline. Its hillclimb command proposes scoped patches, reruns evaluations and checks held-out performance for overfitting.
Official recipe · Published 2026-09-28 · Independently tested: No
This official recipe describes a procedure; it does not prove successful execution or independently reproduced results.
Source facts, caveats and approval gates
Source attribution
Lance Martin / Anthropic
Source publication date
2026-09-28
Source checked
2026-10-14
Model or version
Unknown
Named tools
Claude Code; claude-api skill; evaluation runner; train/test cases
Source approval description
The workflow pauses for input review and grader validation; users choose the objective and permitted changes.
Evidence attribution caveat
This official recipe describes a procedure; it does not prove successful execution or independently reproduced results.
Source outcome limits
This official recipe describes a procedure; it does not prove successful execution or independently reproduced results.
Recommended approval gates
Approve data use, evaluation cases, grader calibration, run budget and allowed change surfaces. Review final changes before deployment.
Canadian availability
Unknown
Demonstrated in Canada
Unknown
Proposed Canadian adaptation
Proposed: use deidentified Canadian task examples and reviewer-checked bilingual cases where relevant.
Claude Code
Improve a reusable skill using paired tests and human review
Draft or revise a skill from the user's workflow; run realistic tasks both with the skill and with a baseline; grade artifacts and compare time/token measurements; collect human feedback and revise. An optional loop evaluates and tunes when the skill triggers.
Official recipe · Publication date unknown · Independently tested: No
This official recipe describes a procedure; it does not prove successful execution or independently reproduced results.
Source facts, caveats and approval gates
Source attribution
Anthropic
Source publication date
Unknown
Source checked
2026-10-14
Model or version
Unknown
Named tools
Claude Code subagents; evals JSON; grader/analyzer instructions; aggregate_benchmark; generate_review.py; run_loop; package_skill; HTML review and feedback JSON
Source approval description
The source asks the user to confirm intent and test prompts, review produced outputs, and approve the quality before optional trigger optimization. It prohibits malicious or deceptive skills and preserves existing skill identity when updating.
Evidence attribution caveat
This official recipe describes a procedure; it does not prove successful execution or independently reproduced results.
Source outcome limits
This official recipe describes a procedure; it does not prove successful execution or independently reproduced results.
Recommended approval gates
Approve test data, run budget and any external actions that test prompts would cause. Review the final skill before installation or team distribution.
Canadian availability
Unknown
Demonstrated in Canada
Unknown
Proposed Canadian adaptation
Proposed: test with permissioned Canadian examples and French/English cases where relevant; redact sensitive task data and review every tool permission embedded in the resulting skill.