Agent skill

Create Adapter

by marcus in marcus/sidecar

Create conversation adapters for importing AI chat history from different tools (Claude Code, Cursor, Warp, Codex, etc.).

MITAuto-check passedBackend & APIs

Install Create Adapter

skills CLI
$ npx skills add marcus/sidecar --skill create-adapter -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install marcus/sidecar create-adapter --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/marcus/sidecar.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/create-adapter .claude/skills/create-adapter && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
create-adapter
GitHub stars
1.1k
Token cost
~4.8k tokens
SKILL.md length
2,134 words
Files
3 (incl. references)
Skills in repo
19
Repo updated
First seen
Licence
MIT

At a glance

Create conversation adapters for importing AI chat history from different tools (Claude Code, Cursor, Warp, Codex, etc.).

  • Works in 12 steps: Cache metadata and messages aggressively → Incremental parsing for append-only… → Two-pass metadata for large files → …
  • Creating a new adapter
  • SKILL.md covers Why Performance Matters, Reference Adapters, Required Interface and Performance Standards, plus 4 more sections
  • Calls go

What it does

Create Adapter is an agent skill from marcus/sidecar. Create conversation adapters for importing AI chat history from different tools (Claude Code, Cursor, Warp, Codex, etc.). Covers the adapter.Adapter interface, caching strategies, incremental parsing, watch/FD management, and performance standards. Use when creating a new adapter, modifying adapter behavior, or debugging adapter performance issues. See references/ for Cursor DB and Warp SQLite schema details.

Its SKILL.md is about 4.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/cursor-db-format.md` and `references/warp-sqlite-schema.md`).

It sits in Backend & APIs, covering Caching. It works with SQLite. The repository describes itself as: Use sidecar next to CLI agents for diffs, file trees, conversation history, and task management with td. The licence is MIT.

When your agent uses it

  • Creating a new adapter
  • Modifying adapter behavior
  • Debugging adapter performance issues

Example prompts

  • “/create-adapter”

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. Cache metadata and messages aggressively
  2. Incremental parsing for append-only formats
  3. Two-pass metadata for large files
  4. Avoid repeated expensive path work
  5. Return defensive copies from caches
  6. Keep DB access FD-safe
  7. Preserve aggregate facts across incremental loads
  8. Prefer directory-level watches
  9. Implement watch scope
  10. Always emit SessionID when known
  11. Debounce and non-blocking sends
  12. Leverage FROZEN tier for file-based adapters

What it can do on your machine

Read from SKILL.md and the folder at commit 3792a4e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • go

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Create Adapter loads about 4.8k tokens when it runs, and up to ~6.7k if it reads all its reference files. Until then it costs about 107 tokens; SKILL.md has 2,134 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~107
When it runs · the whole SKILL.md, loaded when a task matches
~4.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from marcus/sidecar at commit 3792a4e, republished under its MIT licence (© marcus). 2,134 words, ~4,793 tokens.

Download SKILL.mdSave it as .claude/skills/create-adapter/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
create-adapter
description
Create conversation adapters for importing AI chat history from different tools (Claude Code, Cursor, Warp, Codex, etc.). Covers the adapter.Adapter interface, caching strategies, incremental parsing, watch/FD management, and performance standards. Use when creating a new adapter, modifying adapter behavior, or debugging adapter performance issues. See references/ for Cursor DB and Warp SQLite schema details.

Create Adapter

Why Performance Matters

Adapters are the largest performance risk in Sidecar. Conversations refresh on watch events in a hot path that runs continuously during active sessions:

watch event -> coalescer -> session refresh -> adapter.Sessions() -> metadata parsing

If an adapter does full directory scans and full-file reparses on every change, CPU and FD usage spike quickly.

Reference Adapters

Study these before writing a new adapter:

  • internal/adapter/claudecode - Incremental JSONL parsing, targeted refresh
  • internal/adapter/codex - Directory cache, two-pass metadata parsing, global watch scope
  • internal/adapter/cursor - SQLite/WAL-aware cache invalidation, FD-safe DB access
  • internal/adapter/pi - Global scope, JSONL, CWD-based filtering, session classification, message prefix stripping

Required Interface

All adapters implement adapter.Adapter:

go
type Adapter interface {
    ID() string
    Name() string
    Icon() string
    Detect(projectRoot string) (bool, error)
    Capabilities() CapabilitySet
    Sessions(projectRoot string) ([]Session, error)
    Messages(sessionID string) ([]Message, error)
    Usage(sessionID string) (*UsageStats, error)
    Watch(projectRoot string) (<-chan Event, io.Closer, error)
}
Required Session Fields

Every session from Sessions() must set:

  • ID, Name
  • AdapterID, AdapterName, AdapterIcon
  • CreatedAt, UpdatedAt
  • MessageCount, FileSize

FileSize is used for dynamic debounce and huge-session auto-reload protection.

Treat source identity separately from lineage. Use the source's durable thread/session ID for Session.ID; parent, root, fork, or lineage IDs describe relationships and must not collapse distinct sessions. Decode metadata fields defensively when the source has emitted multiple shapes over time (for example, a string in one version and an object in another).

Path and Watch Strategy

Set Session.Path only when Sidecar should use tiered file watching for that adapter:

  • File-based append-only (JSONL/log): set Path to absolute file path — this opts into TieredWatcher with HOT/COLD/FROZEN tiers
  • DB/WAL adapters (Cursor, Warp, Kiro): prefer adapter-specific Watch() with WAL-aware invalidation; do not set Path unless tiered watching covers your write surface

FROZEN tier: File-based sessions with Path set automatically benefit from the FROZEN tier. Sessions unchanged for 24 hours (FrozenThreshold) are excluded from cold polling entirely — zero syscalls. They unfreeze when promoted to HOT (e.g., user selects the session). This is critical for adapters with thousands of session files; without it, pollColdSessions() does one os.Stat() per file every 30 seconds.

Performance Standards

1) Cache metadata and messages aggressively

Minimum cache keys:

  • Metadata: path + size + modTime
  • Messages: path + size + modTime
  • SQLite/WAL: include WAL size+mtime in the key

Use bounded LRU behavior for every cache and index. Prune stale paths. Assume caches evict independently: a hit in one cache must restore any derived state required by another, or the authoritative source must remain available so eviction cannot change results such as aggregate usage or ID-to-path resolution.

2) Incremental parsing for append-only formats

For JSONL/event-log adapters:

  • Cache last parsed byte offset
  • Parse only appended bytes
  • Fall back to full parse on shrink/rotation/corruption
  • Preserve immutable head metadata from prior parse
3) Two-pass metadata for large files

When incremental metadata parse is impractical:

  • Head pass: ID, CWD, first user message, first timestamp
  • Tail pass: latest timestamp, token totals
  • Skip middle of large files

When the source owns a metadata index, prefer its read-only index over scanning large event logs. Probe the schema and required columns before use, open it read-only with bounded/FD-safe access, and fall back to event-log discovery when it is missing, locked, or incompatible. The source index is an adapter seam, not a second source of truth to mutate.

4) Avoid repeated expensive path work

Resolve project path once per Sessions() call (Abs/EvalSymlinks), reuse for all matches.

5) Return defensive copies from caches

Never return cache-owned slices/maps directly. Copy message/session structures to avoid mutation bugs.

6) Keep DB access FD-safe

For SQLite adapters:

  • Open read-only (mode=ro)
  • SetMaxOpenConns(1), SetMaxIdleConns(0)
  • Close rows and DB handles promptly
  • Avoid multiple DB connections per Messages() call
7) Preserve aggregate facts across incremental loads

Usage and similar cumulative facts may arrive as repeated totals or deltas. Define the source semantics, retain the authoritative aggregate across incremental parsing, and include all components the source exposes. Do not reconstruct a partial aggregate from whichever message cache entry survived eviction.

Watching and FD Management

1) Prefer directory-level watches

Do not watch per-session files when directory-level watch gives equivalent signals.

2) Implement watch scope

If adapter watches a global path (same location regardless of worktree):

go
func (a *Adapter) WatchScope() adapter.WatchScope {
    return adapter.WatchScopeGlobal
}

This prevents duplicate watchers across worktrees.

3) Always emit SessionID when known

Watch events should include session ID for targeted refresh (avoids full reloads).

4) Debounce and non-blocking sends
  • Debounce bursty write events
  • Use buffered channels
  • Non-blocking sends: select { case ch <- evt: default: }
5) Leverage FROZEN tier for file-based adapters

File-based adapters that set Session.Path get TieredWatcher's three-tier system (HOT → COLD → FROZEN). Sessions unchanged for 24h are frozen and cost zero polling overhead. This is the primary defense against CPU spikes with thousands of session files. If your adapter has file-based sessions, always set Path — the FROZEN tier scales automatically.

6) Ensure cleanup

All watcher paths must close cleanly on plugin stop. No goroutine or FD leaks.

7) Combine known-file and discovery watching when needed

Tiered watching of known Session.Path values handles cheap appends, but it cannot discover a project's first session or a new time-partitioned directory. Global file adapters may need both known-file watching and one adapter-native discovery watcher. Watch creation at every directory level that can appear later (including month/year rollover), and keep discovery project-filtered.

8) Resolve path IDs through the adapter

Do not assume a new file's basename is its session ID. If identity lives in metadata, implement SessionPathResolver so watch events carry the same durable ID returned by Sessions().

Adapter Call Lifecycle

Some global adapters maintain caches and indexes across calls. Consumers must serialize calls to a stateful adapter, make gate admission and work lifecycle/epoch cancellable, and reject stale results after project switches or shutdown. A slow Sessions() result must remain observable and eventually load (with a visible loading state); never time it out, silently discard it, and leave its goroutine running. Global targeted refresh must admit only sessions already belonging to the current project; unknown IDs require a project-filtered discovery pass.

Message and Content Rendering

Adapters must provide rich structured content for Conversation Flow UI.

Required message mapping

Map source records to:

  • Message.Role, Message.Content, Message.ContentBlocks
  • Message.ToolUses (legacy compatibility)
  • Message.ThinkingBlocks (if available)
  • Message.Model when available
Tool linking rule

Use consistent ToolUseID for tool_use and tool_result blocks. If incremental parsing is used, preserve pending tool-link state across cache updates.

Optional Interfaces

TargetedRefresher
go
type TargetedRefresher interface {
    SessionByID(sessionID string) (*Session, error)
}

Reduces refresh from O(N sessions) to O(1). Implement when adapter can resolve a session directly.

ProjectDiscoverer

Implement when source format allows discovery of sessions beyond current git worktrees.

SessionPathResolver

Implement when a file path alone does not encode the source's durable session ID. Tiered and discovery watchers use it to turn new or changed paths into targeted refresh events.

ProjectDiscoveryWatcher

Implement for global sources that must discover the first matching session even when Sessions() initially returns no known files. Share only one global watcher per adapter, and ensure its events trigger a project-filtered load before any session is admitted.

Error Handling

  • Detect(): return (false, nil) for missing data directories
  • Sessions(): skip corrupt/unreadable entries and continue; hard-fail only on systemic errors
  • Messages(): return nil, nil for missing session files; fail on parse errors
  • Watch(): return (nil, nil, err) when watch setup fails
  • Slow calls: surface loading/error state and accept the eventual result; do not translate a consumer timeout into a false empty history

Benchmark Targets

New adapters should meet these performance targets:

  • Messages() full parse (~1MB): under 50ms
  • Messages() incremental append: under 10ms
  • Messages() cache hit: under 1ms
  • Sessions() on 50 session files: under 50ms

Also benchmark realistic source shapes: hundreds or thousands of indexed sessions, a large live- scale transcript, cache hits, and incremental appends. Record fixture size and session/event count with the result so a tiny synthetic benchmark cannot hide discovery or parsing regressions.

Show full SKILL.md (912 more words)Show less

Testing Requirements

Required tests for every new adapter:

  • Relative vs absolute project path behavior in Detect()/Sessions()
  • Sessions() sorted by UpdatedAt desc
  • Required session fields populated (Adapter*, FileSize, Path when applicable)
  • Cache hit behavior (no reparsing on unchanged files)
  • File growth behavior (incremental parse path)
  • File shrink/rotation behavior (fallback full parse)
  • Tool use/result linking (including incremental append cases)
  • Incremental ContentBlocks tool-use/result ID parity, not only legacy ToolUses
  • Aggregate usage across multiple events, cache-write/read components, and independent cache eviction
  • Watcher event emission includes SessionID
  • Watcher cleanup (no leaked closers)
  • Repeated-call FD stability
  • Zero-history project followed by its first session creation
  • Time-partition rollover (for example, a new month under an existing year)
  • Global discovery and targeted refresh remain isolated across two projects/worktrees

Run tests:

bash
go test ./internal/adapter/<adapter> -run .
go test ./internal/adapter/<adapter> -bench . -benchmem

PR Compliance Checklist

A) Correctness
  • Full adapter.Adapter contract implemented
  • Sessions() sets required identity and timestamp fields
  • Durable source identity is distinct from parent/root lineage; historical metadata shapes parse
  • FileSize populated for every session
  • Path strategy explicit and correct for adapter type
  • Message role/content mapping correct
  • ContentBlocks include text/tool/thinking data
  • Tool result linking correct (ToolUseID parity)
B) Performance
  • Metadata cache implemented and bounded
  • Message cache implemented and bounded
  • Every auxiliary index/aggregate cache is bounded and correct under independent eviction
  • Incremental parse or two-pass strategy implemented
  • Source-owned metadata indexes are read-only, schema-probed, and have a safe fallback
  • No repeated Abs/EvalSymlinks in per-session loops
  • No duplicate parsing for single-pass data
  • Benchmarks added with realistic fixtures
C) FD / Watching
  • Directory-level watches preferred
  • Global adapters implement WatchScopeProvider
  • Global discovery is project-isolated, including zero-history first creation
  • Watch events include SessionID
  • Metadata-backed IDs resolve through SessionPathResolver
  • Debounce + buffered + non-blocking send pattern
  • DB adapters account for WAL in invalidation/watch
  • Known-file and discovery watches cover time-partition rollover without duplication
  • Watchers and goroutines close cleanly
D) Integration
  • Adapter registered via register.go and main import
  • Search uses adapter Messages() path
  • Large-session behavior validated (FileSize-driven)
  • Slow/global calls are serialized, lifecycle-cancellable, and never discarded as empty

Session Classification

Adapters can classify sessions by setting SessionCategory on adapter.Session. The conversations plugin supports category filtering (f menu: i/r/s keys) and a quick toggle (C key).

Category Constants

Defined in internal/adapter/adapter.go:

  • adapter.SessionCategoryInteractive — user-initiated interactive sessions
  • adapter.SessionCategoryCron — automated/scheduled sessions
  • adapter.SessionCategorySystem — system/gateway sessions
Implementation Guidelines
  • Classify during metadata parsing (zero extra I/O) — extract category from the first user message or session header
  • Only set SessionCategory if the adapter has meaningful categories. Don't set it if all sessions are the same type
  • If the category filter is active and SessionCategory is empty, sessions pass through (non-breaking for adapters that don't classify)
  • Gateway/system messages may need special classification — e.g., "System: WhatsApp gateway connected" is actually interactive, not system. Check for known preamble patterns before defaulting to system category
Example (from Pi adapter)
go
func extractSessionMetadata(firstUserMessage string) (category, cronJobName, sourceChannel string) {
    if strings.HasPrefix(firstUserMessage, "[cron:") {
        return adapter.SessionCategoryCron, extractCronJobName(firstUserMessage), ""
    }
    if strings.HasPrefix(firstUserMessage, "System:") {
        if strings.Contains(firstUserMessage, "WhatsApp gateway") {
            return adapter.SessionCategoryInteractive, "", "whatsapp"
        }
        return adapter.SessionCategorySystem, "", ""
    }
    return adapter.SessionCategoryInteractive, "", detectSourceChannel(firstUserMessage)
}

Rich Metadata Fields

Optional fields on adapter.Session for richer display and filtering:

  • CronJobName string — for cron/scheduled sessions; used as session name when set
  • SourceChannel string — for multi-channel adapters (e.g., "telegram", "whatsapp", "direct")

Optional field on adapter.Message:

  • SourceLabel string — per-message source attribution badge (e.g., "[TG] Marcus", "[WA]", "[cron] job-name")

Set these during parsing when the source format contains channel/origin metadata. The conversations plugin and conversation flow UI use these for display.

Message Content Cleaning

For adapters whose source format embeds structured prefixes in user messages (e.g., channel tags, cron headers), strip them during parsing to keep the conversation view clean.

Pattern
  1. Extract metadata (source label, channel, category) from the raw message prefix
  2. Strip the prefix from Message.Content and text ContentBlocks
  3. Store the extracted label in Message.SourceLabel for badge display
go
// In processMessageLine for user messages:
content, _, _, contentBlocks := parseContent(raw.Message.Content)
sourceLabel := extractSourceLabel(content)    // "[TG] Marcus"
content = stripMessagePrefix(content)          // clean body only
for i := range contentBlocks {
    if contentBlocks[i].Type == "text" {
        contentBlocks[i].Text = stripMessagePrefix(contentBlocks[i].Text)
    }
}
msg := adapter.Message{
    Content:       content,
    ContentBlocks: contentBlocks,
    SourceLabel:   sourceLabel,
}

This keeps Content human-readable while preserving origin metadata in SourceLabel.

Global Adapter Gotchas

Lessons learned from building global-scope adapters (Pi, Codex):

CWD-based Project Filtering

Global adapters (WatchScopeGlobal) store sessions in a single directory regardless of project. They must filter by CWD matching projectRoot in Sessions():

  • Resolve projectRoot once per Sessions() call (Abs + EvalSymlinks)
  • Read CWD from the cheapest authoritative source (a read-only metadata index or event-log header), and bound any cache used to avoid full-file parses for non-matching sessions
  • Match with filepath.Rel — a session matches if its CWD is equal to or a subdirectory of the project root
Category Filter Interaction
  • The conversations plugin category filter only filters sessions that HAVE a SessionCategory set — empty passes through
  • Don't enable category filter by default in the plugin — it breaks non-classifying adapters
  • When adding classification to a new adapter, test that existing adapters without categories still display correctly
Project Switching

Global adapters need to handle project switching gracefully:

  • Each Sessions(projectRoot) result is project-filtered, even when adapter caches retain source-global metadata
  • Cross-project/path identity indexes may accumulate across serialized calls, but must be bounded; eviction must fall back to authoritative index/log lookup rather than changing resolution behavior
  • Project reinitialization cancels and replaces the prior watcher lifecycle/epoch; stale loads and events must be rejected
  • Cache entries keyed only by source path may be shared across projects, while membership in a returned session list remains project-specific
Watcher Lifecycle and Refresh
  • Create only one adapter-native global discovery watcher within the active lifecycle, alongside tiered watches for known files when applicable
  • Target refresh only for IDs already admitted to the current project's session list, and only through their owning adapter
  • Treat an unknown ID from a global watcher as discovery: run a project-filtered full load before admitting it
  • Ensure watch goroutines and queued calls use the current lifecycle context and cannot retain stale project state

Schema References

See references/cursor-db-format.md for Cursor's per-session SQLite database structure (Merkle tree blobs, hex-encoded metadata, WAL considerations).

See references/warp-sqlite-schema.md for Warp's single SQLite database structure (ai_queries, agent_conversations, blocks tables, protobuf tasks).

© marcus, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in .claude/skills/create-adapter of marcus/sidecar.

  • SKILL.md
  • references/cursor-db-format.md
  • references/warp-sqlite-schema.md

Open the folder on GitHubat commit 3792a4e

Compare with similar skills

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Works with

Questions about Create Adapter

What does Create Adapter do?

Create conversation adapters for importing AI chat history from different tools (Claude Code, Cursor, Warp, Codex, etc.). Create Adapter is an agent skill from marcus/sidecar.).

When should I use Create Adapter?

Create Adapter fits situations like: creating a new adapter; modifying adapter behavior; debugging adapter performance issues.

How do I install Create Adapter in Claude Code?

Run `npx skills add marcus/sidecar --skill create-adapter -a claude-code`. Or copy the skill folder (.claude/skills/create-adapter in marcus/sidecar) into .claude/skills/create-adapter in your project. Claude Code loads it when a task matches its description.

How do I install Create Adapter in Codex?

Run `npx skills add marcus/sidecar --skill create-adapter -a codex`. Or copy the skill folder (.claude/skills/create-adapter in marcus/sidecar) into .agents/skills/create-adapter in your project. Codex loads it when a task matches its description.

Can I use Create Adapter in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add marcus/sidecar --skill create-adapter -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/create-adapter, .gemini/skills/create-adapter, .github/skills/create-adapter and .opencode/skills/create-adapter in your project.

What does Create Adapter need to run?

Going by SKILL.md and its folder, Create Adapter needs the command-line tools its instructions call (go).

Does Create Adapter access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Create Adapter safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Create Adapter use?

Create Adapter is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Create Adapter use?

About 4.8k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.9k tokens, read only when the agent opens those files.

What are the alternatives to Create Adapter?

Skills that share tags, products or a category with Create Adapter: Ar Io Gateway Operator (ar-io/ar-io-node, 127 stars), Nxv (utensils/nxv, 136 stars), Flowfile Architecture Contract (Edwardvaneechoud/Flowfile, 370 stars) and Data Layer Footguns (maxrave-dev/kotlin-footguns, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Create Adapter?

marcus (a GitHub user) maintains it in marcus/sidecar, which has 1,085 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on October 5, 2026.

Source: marcus/sidecar on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.