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Codebase Recon

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PriNova
codebase-recon

Structured reconstruction workflow for unfamiliar or undocumented codebases. Produces agent-first YAML artifacts only under a resolved structured docs root, with no prose Markdown fallback.

Overview

PublisherPriNova
Repositorypi-agent-codebase-workflows
Skill namecodebase-recon
Stars
82
Forks
2
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by PriNova on GitHub. Read the source before you install it.

Installation

Install the Codebase Recon AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/PriNova/pi-agent-codebase-workflows/tree/main/skills/codebase-recon
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/PriNova/pi-agent-codebase-workflows.git /tmp/pi-agent-codebase-workflows
mkdir -p .claude/skills
cp -r /tmp/pi-agent-codebase-workflows/skills/codebase-recon .claude/skills/codebase-recon
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Codebase Recon in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Codebase Recon on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Codebase Recon is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Codebase Reconstruction — Structured API Only

Goal: reconstruct durable project understanding into canonical YAML artifacts for coding-agent ingestion.

Structured Artifact API Contract

Legacy prose artifacts are deprecated. Do not create, update, or rely on docs/agent/*.md, scoped prose docs, or generated human-readable Markdown views. Use structured YAML for canonical artifacts. Root AGENTS.md remains a harness interoperability file and may be generated/updated only by workflows that explicitly say so.

Resolved structured docs root:

Treat docs/agent/api as a logical layout rooted at a resolved structured docs root, not a fixed repo path.

Resolution rules:

  1. Resolve workspace_root with git rev-parse --show-toplevel 2>/dev/null or fallback to pwd.
  2. Canonicalize workspace_root before fingerprinting when possible (realpath, pwd -P, Path(...).resolve(), or equivalent).
  3. safe-start always creates and uses the initial repo-local root: <workspace_root>/docs/agent/api.
  4. codebase-recon uses repo-local only when <workspace_root>/docs/agent/api already exists.
  5. Otherwise use the global overlay root: ~/.pi/agent/workspaces/<workspace-fingerprint>/docs/agent/api.
  6. Compute <workspace-fingerprint> exactly from canonical workspace_root: strip one leading slash/backslash, replace every slash, backslash, and colon with -, then wrap with --. This keeps the same workspace stable.
  7. Example: /data/data/com.termux/files/home/CodeProjects/pi-mono -> --data-data-com.termux-files-home-CodeProjects-pi-mono--.
  8. Do not create new repo-local structured docs in unadopted repos unless the user explicitly asks for repo-local adoption there.

Logical structured layout under the resolved docs root:

text
repo/
  scopes.yaml
  repo-inventory.yaml
  project-intent.yaml
  architecture.yaml
  data-flow.yaml
  data-model.yaml
  invariants.yaml
  dependency-rules.yaml
  design-issues.yaml
  risk-register.yaml
  change-guide.yaml
  testing-strategy.yaml
  validation-baseline.yaml
  contracts.yaml
  adr.yaml
  agent-operating-guide.yaml
scopes/
  by-path/<repo-relative-path>/...
  by-domain/<slug>/...

Every structured artifact must conform to ../_shared/references/schemas/common.schema.json plus its artifact-specific schema. Do not inline, invent, or vary envelope fields.

Stable IDs required: scope:*, component:*, entity:*, invariant:*, risk:*, contract:*, flow:*, command:*, issue:*, adr:*, testplan:*.

Ownership rules:

  • scopes: scope routing, ownership, cross-scope discovery only.
  • repo-inventory: file tree, commands index, entry points, external boundaries, configs.
  • validation-baseline: command status, blockers, recommended validation order.
  • project-intent: product goal, users, journeys, must-have features, non-goals, constraints, assumptions, open questions, success metrics, prioritized quality attributes, operating constraints, risk areas.
  • architecture: components, architecture style, style rationale, alternatives/tradeoffs, side-effect boundaries, deployment/operating shape, reliability expectations, observability expectations, security assumptions, high-level flow refs.
  • data-flow: typed flow graph/steps, trust boundaries, sensitive-data handling steps, inputs, outputs, error states, degradation/recovery notes.
  • data-model: entities, IDs, schemas, relationships, lifecycles, serialized formats, retention/compliance notes.
  • invariants: rules, forbidden states, enforcement locations, invariant-test refs.
  • dependency-rules: layers, allowed/forbidden dependencies, violations, coupling hotspots.
  • design-issues: structural drift, deferred decisions, ambiguity, ownership gaps.
  • risk-register: failure modes, severity/confidence, affected refs, mitigations/recommended actions, suggested tests/fixes.
  • contracts: cross-scope APIs, schemas, events, generated clients, DB/file/deployment/env/auth/telemetry contracts.
  • testing-strategy: test topology, quality-attribute coverage, coverage gaps, risk-to-test priorities, operability checks.
  • change-guide: workflow routing and checklists; references owner artifacts, duplicates no facts.
  • adr: structured decision records with bounded prose fields, including alternatives and consequences for major design choices.
  • agent-operating-guide: structured source for agent operating rules. Root AGENTS.md may mirror this in compact harness-readable Markdown when produced by safe-start or codebase-recon Pass 6.

Redundancy rule: define each fact in its owner artifact exactly once. Other artifacts reference IDs. Current truth rule: canonical YAML artifacts represent current state, not audit history. Remove resolved or superseded records from canonical owner artifacts by default. Keep them only when another live record still references them or an active migration requires temporary continuity. Use Git history, PRs, issues, or ADRs for audit/history. Prose rule: bounded prose allowed only in summary, notes, rationale, context, decision, recommended_action, and similar scalar fields. Scope rule: if focus is path-like, write under <docs-root>/scopes/by-path/<focus>/; otherwise under <docs-root>/scopes/by-domain/<slug>/. Always update <docs-root>/repo/scopes.yaml.

Runtime Schema Loading

When a workflow creates, updates, migrates, or validates structured artifacts, read ../_shared/references/artifact-api.md first. Then read only the shared skill package schemas needed for the artifacts being written:

  • ../_shared/references/schemas/common.schema.json
  • ../_shared/references/schemas/<artifact-file-base>.schema.json

Do not read all schemas. Do not use templates. Schemas are runtime API contracts; project docs outside the shared runtime refs are maintainer aids unless the user asks about this package itself.

Structured Artifact Write/Update Protocol

Use this protocol whenever creating or updating YAML artifacts.

1. Scope and owner resolution

  1. Resolve scope first from task/focus and <docs-root>/repo/scopes.yaml when present.
  2. Path focus uses longest prefix match; domain focus requires explicit domain/contract/task evidence.
  3. Select the single owner artifact for each fact using the ownership rules above.
  4. Never duplicate owner facts in router/checklist artifacts; reference stable IDs instead.

2. Read-before-write

  1. Read the existing target YAML if it exists.
  2. Read directly referenced owner artifacts needed to preserve refs and avoid duplication.
  3. If target YAML is absent, create it with the common envelope and artifact-specific top-level keys.
  4. Preserve unknown fields unless they conflict with this protocol; do not silently drop agent/user-added structured data.

3. Stable ID generation

  1. Reuse existing IDs whenever the semantic object is the same, even if name/path changed.
  2. New record IDs use deterministic slugs from owner scope + semantic name: risk:<slug>, entity:<slug>, component:<slug>, etc.
  3. Envelope artifact_id values use repo:<artifact-slug> for repo-level artifacts and <scope.id>/<artifact-slug> for scoped artifacts, e.g. repo:architecture and scope:packages/ai/architecture.
  4. Never append an artifact slug to a scope ID with a second colon; scope:packages/ai:architecture is invalid.
  5. If two objects slug-collide, append shortest stable discriminator from path/component/contract, not a random suffix.
  6. Never renumber IDs because order changed.

4. Upsert semantics

For each discovered fact/object:

  1. Match existing record by ID first.
  2. If no ID match, match by stable source-of-truth fields: path+symbol, contract source path, command string+cwd, entity name+owner scope, rule owner+kind.
  3. If matched, update only changed fields, append/refresh evidence, and preserve unrelated fields.
  4. If unmatched, insert new record in deterministic order by ID or explicit order field.
  5. If an existing observed record is resolved, superseded, or no longer supported, delete it from the canonical owner artifact by default.
  6. Keep a record with status: stale or deprecated only when a live reference still depends on it or an active migration needs temporary continuity. Add evidence/unknown explaining why, and link replacement ID when known.
  7. Delete accidental duplicates, malformed records, and unreferenced resolved/superseded records, and mention deletion in final response.

5. Evidence and confidence

  1. Every observed record needs at least one evidence ref with file/symbol/command/doc/diff observation.
  2. Planned records may use evidence_mode: planned and confidence low or medium.
  3. Mixed records must separate observed fields from planned/assumed fields via evidence refs or unknowns.
  4. Do not upgrade status: current or confidence high without source or command evidence.

6. Reference integrity

Before writing final artifacts:

  1. Check every *_ref, *_refs, and depends_on ID points to a record in the same artifact set or is explicitly listed as external/unknown.
  2. Prefer adding missing owner records as compact stubs over leaving dangling refs.
  3. For cross-scope refs, ensure scopes.yaml and contracts.yaml identify owner/consumer relationship.
  4. If ownership is ambiguous, create/update design-issues.yaml with kind: ownership_gap and reference it.

7. Status transitions

Allowed transitions:

  • planned -> partial -> current
  • current -> stale -> current
  • current|stale|partial -> deprecated

Rules:

  • current requires sufficient observed evidence for the represented scope.
  • partial means useful but incomplete evidence.
  • stale means contradicted by newer source evidence or missing source path. Use it as a temporary migration/quarantine state, not a permanent archive state.
  • deprecated means superseded; include replacement_ref when known. Use it only when a live reference still needs continuity during migration; otherwise remove the record from the canonical artifact.

8. Deterministic formatting

  1. Use YAML with two-space indentation.
  2. Use stable top-level key order: envelope keys first, artifact-specific keys next.
  3. Sort unordered arrays by id; keep ordered flow/checklist arrays by order.
  4. Use null, [], or {} consistently rather than omitting required envelope fields.
  5. Keep prose scalar fields concise; no long narrative blocks.

9. Validation before completion

Perform best-effort validation after writing:

  1. Re-read changed YAML for parse/syntax sanity when practical.
  2. Validate against the shared schemas by inspection/re-read: envelope keys, artifact-specific top-level keys, required arrays/items, stable ID prefixes, and obvious dangling refs.
  3. Verify no legacy Markdown artifacts were created or updated by the workflow, except root AGENTS.md when explicitly produced for harness interoperability.
  4. Report changed YAML files, validation performed, unresolved unknowns, and any records intentionally retained or pruned as part of compaction.

Core Rules

  • Do not edit production code.
  • No Markdown artifacts except root AGENTS.md for harness interoperability in Pass 6.
  • Each pass writes/updates only its owner artifacts.
  • Later passes read prior YAML artifacts instead of re-reading whole repo.
  • Evidence is mandatory for observed claims.
  • Unknowns are explicit records, not vague prose.
  • When newer schemas require fields that are not inferable from the codebase, still create those fields with evidence-backed low-confidence placeholders, empty arrays, null-capable subfields, and explicit unknowns rather than omitting them.
  • Reconstruct decision-driving quality attributes, trust boundaries, reliability/observability/security expectations, and tradeoffs when the repo provides enough evidence; otherwise record the ambiguity explicitly.
  • Use stable IDs and cross-references to mirror codebase ownership and relationships.

Passes

Users may invoke this skill directly for any pass, or use the matching prompt template as a pass shortcut.

  1. Inventory (/recon-01-inventory): write repo-inventory.yaml, validation-baseline.yaml, and initial project-intent.yaml; update scopes.yaml for focus. Infer product goal, users, journeys, quality attributes, operating constraints, and risk areas from repo evidence when possible; otherwise record explicit unknowns.
  2. Architecture (/recon-02-architecture): write architecture.yaml with components, style, style rationale, alternatives/tradeoffs when inferable, boundaries, execution flow refs, and reliability/observability/security expectations.
  3. Data/invariants (/recon-03-data-invariants): write data-model.yaml and invariants.yaml; surface trust boundaries, sensitive data, retention/compliance implications, and correctness/safety rules.
  4. Dependencies/drift (/recon-04-dependency-rules): write dependency-rules.yaml and design-issues.yaml; record structural gaps where quality/security/operability evidence is weak.
  5. Risks (/recon-05-risk-register): write risk-register.yaml with affected refs, recommended actions, and quality/security/reliability risks.
  6. Agent operating guide (/recon-06-agents): write agent-operating-guide.yaml and root AGENTS.md.
  7. Change guide (/recon-07-change-guide): write change-guide.yaml.
  8. Consolidation (/recon-08-consolidate): reconcile YAML artifacts, resolve contradictions with evidence, preserve owner-only facts, and backfill required schema fields when older artifacts are incomplete.
  9. ADR (/recon-09-adr): write adr.yaml with structured ADR records, alternatives, and consequences where architectural decisions are inferable from history/docs/code.
  10. Risk-to-tests (/recon-10-risk-tests): write/update testing-strategy.yaml with quality-attribute coverage, risk-to-test priorities, and operability checks.

All-in-one shortcut: /recon-all runs the pass sequence until the requested focus is complete or the repo size requires stopping at a pass boundary.

Artifact Shape Source

For each pass, use the shared runtime schemas as the only shape contract: ../_shared/references/artifact-api.md, ../_shared/references/schemas/common.schema.json, and the matching artifact schema(s). Do not rely on prose key lists.

Frequently asked questions

What does the Codebase Recon AI skill do?

Structured reconstruction workflow for unfamiliar or undocumented codebases. Produces agent-first YAML artifacts only under a resolved structured docs root, with no prose Markdown fallback.

Why use Codebase Recon on TypingMind?

Because you install it once and use it with any model. Codebase Recon is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Codebase Recon in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/PriNova/pi-agent-codebase-workflows/tree/main/skills/codebase-recon. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Codebase Recon?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Codebase Recon?

As many as you like. As long as a model supports skills, you can use Codebase Recon with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Codebase Recon AI skill free?

Yes. It is published on GitHub by PriNova under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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