feat: add daily-function-namer agentic workflow

[Copilot]

Go function names optimized for human readability often fail agent discoverability — generic verbs like process(), handle(), run() give agents no signal when searching for relevant code. This adds a daily workflow that systematically renames functions to improve agent-findability across the codebase.

New workflow: daily-function-namer

• Round-robin via cache-memory: Persists a last_index cursor in /tmp/gh-aw/cache-memory/function-namer-state.json; each run processes 3 non-test .go files from pkg/, wrapping with modulo across the full file list
• Serena-powered analysis: Uses shared/mcp/serena-go.md (LSP-backed) to extract symbols and resolve call sites via get_symbols_overview + find_referencing_symbols
• Rename heuristics: Flags generic names (process, handle, run, mkCfg, genSO) and proposes specific verb+noun replacements (e.g. mkCfg → buildMCPServerConfig)
• Agentic issue output: Creates a [function-namer] issue with a rename table per file, full function inventory, and a ready-to-assign implementation plan (search commands, commit conventions, validation checklist); stale issues are automatically closed via close-older-issues: true

tools:
  cache-memory: true          # round-robin state across runs
  github:
    toolsets: [default, issues]
  bash:
    - "find pkg -name '*.go' ! -name '*_test.go' -type f | sort"

safe-outputs:
  create-issue:
    expires: 7d
    title-prefix: "[function-namer] "
    labels: [refactoring, code-quality, automated-analysis, cookie]
    max: 1
    close-older-issues: true

Original prompt

We’re going to create an agentic workflow that runs daily and analyzes up to three Go files at a time. It will extract the function names from these files, analyze the functions, and potentially rename them so that their names better match their behavior and intent, making them more intuitive for an agent.

The goal is to increase the likelihood that, when an agentic task is created and an agent searches for relevant functions, it can find them more reliably because the functions use clearer, more logical names.

Use Serena to extract function file names and use cache-memory to perform a round-robin across all files. Write the results as an issue that includes an agentic plan for an agent, so we can assign the issue to an agent and implement the plan as code.

Custom agent used: agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing

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[pelikhan]

Do not use the SafeOutputClosedissue. Instead, use the CreateIssue.CloseOlderIssues field, which provides a built-in way to close older issues.@copilot

[Copilot]

Do not use the SafeOutputClosedissue. Instead, use the CreateIssue.CloseOlderIssues field, which provides a built-in way to close older issues.@copilot

Done in 7c54dbe. Replaced the close-issue safe output with close-older-issues: true on the create-issue block, and removed the manual close-issue Step 1 from the prompt body.

[Copilot]

Pull request overview

Adds a new daily gh-aw workflow that uses Serena + cache-memory to rotate through Go files in pkg/ and open a refactoring issue with function rename recommendations to improve agent discoverability.

Changes:

• Introduces daily-function-namer workflow prompt defining the round-robin selection + rename heuristics + issue template.
• Adds the compiled workflow manifest (.lock.yml) generated from the .md definition.

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.

File	Description
.github/workflows/daily-function-namer.md	Defines the daily agentic analysis steps, caching strategy, and issue output format for rename suggestions.
.github/workflows/daily-function-namer.lock.yml	Compiled GitHub Actions workflow generated by gh-aw to execute the new workflow.

Comments suppressed due to low confidence (1)

.github/workflows/daily-function-namer.md:129

• Section 5.2 references “functions identified in 6.1”, but there is no 6.1 (and the relevant symbol extraction step is 5.1). Please fix the step reference so the agent can follow the intended sequence reliably.

For each function identified in 6.1, read enough of the implementation to understand its behavior:

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