Skip to content
FunCoding

Search

Search docs, agents, posts, Skills and MCP servers

pathfinder

Map a codebase into feature-grouped flowcharts, identify duplicated concerns across features, and propose a unified architecture. Use when asked to "find the ideal path," unify duplicated systems, or audit architecture before a refactor. Emits a proposed unified flowchart plus per-system /make-plan prompts.

数据库与数据97kplugin/skills/pathfinder/SKILL.md

Install

Send this to Claude Code, Codex or Cursor. The agent reviews the Skill for safety first and installs it after you confirm.

读取 https://funcoding.ai/skills/thedotmack/claude-mem/pathfinder/install.md ,按里面的步骤帮我安装这个 Skill。

SKILL.md

Pathfinder

You are an ORCHESTRATOR. Map the codebase into feature-grouped flowcharts, identify duplicated concerns, propose the simplest unified architecture, and hand off per-system plans to /make-plan.

You do not write implementation code. You produce diagrams, a duplication report, a proposed unified flowchart, and handoff prompts.

Delegation Model

Use subagents for discovery and extraction (file reading, flow tracing, grep, diagramming). Keep synthesis (deciding feature boundaries, picking unification strategies, final flowchart) with the orchestrator. Reject subagent reports that lack source citations and redeploy.

Subagent Reporting Contract (MANDATORY)

Each subagent response must include:

  1. Sources consulted — exact file paths and line ranges read
  2. Concrete findings — exact function names, call sites, data flow
  3. Mermaid diagram(s) with nodes labeled by file:line
  4. Confidence note + known gaps

Output Artifacts

All artifacts go in PATHFINDER-<YYYY-MM-DD>/ at repo root:

  • 00-features.md — feature inventory with boundaries
  • 01-flowcharts/<feature>.md — one Mermaid flowchart per feature
  • 02-duplication-report.md — cross-cutting duplicated concerns with evidence
  • 03-unified-proposal.md — proposed unified architecture + Mermaid
  • 04-handoff-prompts.md — copy-pasteable /make-plan prompts per unified system

Phases

Phase 0: Feature Discovery (ALWAYS FIRST)

Deploy ONE "Feature Discovery" subagent to:

  1. Walk the source tree (not built artifacts) and read top-level README / CLAUDE.md
  2. Propose feature boundaries based on directory structure, import graph, and naming
  3. Return a flat list of features with: name, entry points (file:line), core files, brief purpose

Orchestrator reviews the proposal, adjusts boundaries if needed, writes 00-features.md. Do NOT fan out until feature boundaries are approved.

Phase 1: Per-Feature Flowcharts (FAN OUT)

Deploy ONE "Flowchart" subagent per feature in parallel. Each receives only its feature's scope. Each must:

  1. Trace the feature's primary happy path from entry point to terminal state
  2. Identify side effects (DB writes, HTTP calls, file I/O, process spawns)
  3. Note error and fallback branches but do not let them dominate the diagram
  4. Produce a Mermaid flowchart TD with every node labeled Name<br/>file:line
  5. List external dependencies (other features it calls into) at the bottom

Orchestrator writes each flowchart to 01-flowcharts/<feature>.md. Reject any diagram missing file:line labels.

Phase 2: Duplication Hunt

Deploy TWO subagents in parallel:

"Within-Feature Duplication" subagent:

  • For each feature, find repeated code/logic patterns inside the feature only
  • Report only duplications worth consolidating (ignore trivial repetition)

"Cross-Feature Duplication" subagent:

  • Compare flowcharts across features for concerns that appear in multiple places
  • Examples of what to look for: multiple capture paths, parallel queue implementations, duplicated storage/migration code, repeated agent scaffolding, parallel parsing layers
  • For each duplication, report: (a) the concern, (b) every location with file:line, (c) why they diverged, (d) whether the divergence is legitimate specialization or accidental

Orchestrator synthesizes both into 02-duplication-report.md. Every duplication claim must cite ≥2 file:line locations.

Phase 3: Unified Proposal (ORCHESTRATOR)

The orchestrator writes 03-unified-proposal.md itself — do not delegate synthesis.

For each duplicated concern from Phase 2 that is NOT legitimate specialization:

  1. Propose the simplest unified design (one path, one store, one handler — whatever applies)
  2. Name the consolidated component and its single entry point
  3. Show what each old call site becomes
  4. Call out any loss of capability and whether it's acceptable

End the document with ONE combined Mermaid flowchart showing the proposed unified system. Nodes still labeled with target file:line (new or existing) where knowable.

Anti-patterns to reject in your own proposal:

  • Adding a new abstraction layer "for flexibility"
  • Keeping both old paths behind a feature flag
  • Introducing a registry/factory when a switch statement suffices
  • Preserving divergent behavior "just in case"

Phase 4: Per-System Handoff Prompts

For each unified system in the proposal, write a ready-to-run /make-plan prompt to 04-handoff-prompts.md. Each prompt must:

  1. State the target unified component and its single entry point
  2. List the exact call sites to rewrite (from Phase 2 evidence)
  3. Cite the relevant flowchart file from 01-flowcharts/
  4. Include anti-pattern guards specific to this system

Format each as a fenced code block the user can copy directly into /make-plan.

Key Principles

  • Evidence over intuition — every diagram node and duplication claim cites file:line
  • Current state before ideal state — Phases 0–2 describe what IS; Phase 3 describes what SHOULD BE
  • Simplest unification wins — prefer deletion over abstraction; prefer one path over configurable paths
  • Specialization is not duplication — two components serving different trust models or data sources are legitimate even if their code looks similar
  • Handoff, don't implement — Pathfinder ends at plan prompts; /make-plan and /do take it from there

Failure Modes to Prevent

  • Drawing flowcharts from memory instead of source — redeploy subagent with grep evidence requirement
  • Proposing unification of legitimately specialized components — re-examine trust/data-source divergence
  • Handoff prompts that lack concrete call sites — rewrite with Phase 2 evidence
  • Skipping Phase 0 boundary review — fanning out on bad feature boundaries wastes all of Phase 1

Similar Skills

xlsx
Official
anthropics/skills180k

xlsx

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

Databases & data

deprecation-and-migration
addyosmani/agent-skills102k

deprecation-and-migration

Manages deprecation and migration. Use when removing old systems, APIs, or features. Use when migrating users from one implementation to another. Use when migrating a database schema in production, such as renaming or dropping a column without downtime (expand/contract). Use when deciding whether to maintain or sunset existing code.

Databases & data

connect
ComposioHQ/awesome-claude-skills77k

connect

Connect Claude to any app. Send emails, create issues, post messages, update databases - take real actions across Gmail, Slack, GitHub, Notion, and 1000+ services.

Databases & data

datalad
K-Dense-AI/scientific-agent-skills48k

datalad

Retrieves, versions, and publishes scientific datasets with DataLad and git-annex, and captures computational provenance with datalad run, rerun, and containers-run. Use when cloning or fetching data from OpenNeuro, DANDI, datasets.datalad.org, or any DataLad dataset; when a file in a dataset reads as a broken symlink or a small pointer instead of real data; when an analysis needs a machine-readable record of how each output was produced so it can be re-executed; or when publishing a dataset to siblings such as a GitHub repository plus a storage remote. Also use to decide between DataLad and plain Git for a data-carrying repository.

Databases & data

database-lookup
K-Dense-AI/scientific-agent-skills48k

database-lookup

Queries documented public database APIs with explicit endpoints, filters, pagination, and provenance. Used when a scientific, regulatory, financial, or other database-backed fact must be retrieved reproducibly from a named source rather than inferred from general knowledge.

Databases & data

dask
K-Dense-AI/scientific-agent-skills48k

dask

Scales pandas, NumPy, and custom Python research workflows beyond memory or across clusters with Dask. Covers DataFrames, Arrays, Bags, Futures, chunking, schedulers, and distributed diagnostics. Use for partitioned file processing, scientific array computation, or parallel tasks whose memory and dependency structure require Dask.

Databases & data