* fix(claude): use APPDATA for Windows Desktop sessions
* test(claude): harden withPlatform mock against cross-file leak
The process.platform mock omitted configurable:true and never restored
the value when the property had no own descriptor, leaving win32 mocked
for later tests on the same Vitest worker. Add configurable + an else
branch that deletes the override to expose the real inherited value.
---------
Co-authored-by: AgentSeal <hello@agentseal.org>
v1 modern execution files carry the same metered credits as v2
(usageSummary[].usage, unit "credit" — the predecessor of v2's
promptTurnSummaries), but the parser only harvested usedTools from
that array and priced executions from estimated tokens. Since v2 only
ships in brand-new IDE builds, v1 is the format nearly all Kiro IDE
users are on today. Sum usage across usageSummary entries and price at
the public overage rate.
Align all three credit parsers (CLI, v1, v2) on one fallback contract —
gate on summed credits > 0 and fall back to token-estimated pricing
with costIsEstimated: true:
- CLI turns without metering_usage were priced at a frozen $0
- CLI turns with an EMPTY metering_usage array (75 of 10,105 real turn
metadatas — meta written before metering lands) passed the truthy
presence check and froze $0 marked as real cost
- legacy .chat calls now carry costIsEstimated: true, which was always
the reality but never stated
Validated against a real machine: 221 of 277 v1/legacy calls now carry
metered cost ($20.11) instead of token estimates that had overstated
them ~5x; verified usageSummary values are per-turn amounts, not
cumulative counters (sequences fluctuate). Call counts unchanged before
and after — nothing gained, lost, or double-counted.
Also document the companion-file fingerprint blind spot at
fingerprintFile: kiro CLI credits and v2 modelId live in companion
files the single-file fingerprint never sees, so a parse racing the
companion write can cache fallback values that only self-heal while
the transcript keeps changing.
AI-Origin: ai-generated
AI-Tool: kiro
Kiro's new IDE builds write sessions to ~/.kiro/sessions/<hash>/sess_<id>/
(session.json + messages.jsonl event log) instead of globalStorage. Add a
v2 parser and discovery, plus fixes surfaced while validating against a
real upgraded machine:
- Parse v2 event-sourced turns (user/turn_start/assistant/tool_call/
tool_result/usage_summary/turn_end) with per-execution dedup keys
(kiro-v2:<session>:<execId>) and defensive flushes for out-of-order
events and in-progress sessions
- Price turns from real metered credits at the public overage rate
(USD_PER_KIRO_CREDIT = $0.04/credit, individual plan: $20/mo for
1,000 credits), falling back to token estimation only when a turn has
no usage_summary (aborted/in-flight); costIsEstimated distinguishes
the two
- Fix pre-existing CLI parser bug: metering_usage values are credits,
not dollars — costs were overstated 25x at the real rate
- Count v2 tool_result content as input context (matching the CLI
parser's ToolResults treatment); previously only the user prompt was
counted, undercounting input ~25x on agentic turns
- Keep reasoningTokens disjoint from outputTokens: downstream
aggregation (models-report, audit-report, parser) sums the two
fields, so folding reasoning into output would double-count. Combine
them only for the token-pricing fallback, matching codex/gemini
- Take the real modelId from session.json, so v2 sessions are not
mislabeled kiro-auto
- Extract parseWorkspaceSession from the inline createParser block for
parity with the other four format parsers
- Add v2SessionsRootOverride and stop deriving the v2 scan root from
the cliDir parent when only agentDirOverride is set (tests were
scanning the system tmpdir)
- Tests: v2 parsing/discovery/dedup/routing, credit vs fallback
pricing, tool_result turn scoping, reasoning disjointness, cli-dir
skip guard, and a mixed-format coexistence suite (legacy .chat + v1
executions + workspace-sessions + CLI + v2 on one machine) asserting
exact aggregate counts and no double counting
- Docs: v2 store layout, credit pricing, dedup namespaces, and the
disjoint-store verification (v1->v2 is a clean cutover, no
migration/dual-write observed)
AI-Origin: human
Users who run MCP tools through a CLI wrapper (e.g. philschmid/mcp-cli)
instead of registering servers natively don't produce Codex
mcp_tool_call_end events; Codex logs a plain exec_command. So their MCP
usage showed only as a shell command and was absent from the MCP
breakdown, even after #513 fixed the native path.
- Recognize `mcp-cli [options] call <server> <tool>` in the exec command
and also attribute it as mcp__<server>__<tool>. The exec still counts as
Bash since it genuinely is a shell exec. Flag-tolerant, quote/path/bash-lc
tolerant, and scoped to the mcp-cli binary (not foo-mcp-cli); only the
`call` subcommand (a real execution) matches, not info/grep/listing.
- Register `codex` in PROVIDER_PARSE_VERSIONS. session-cache.json serves
unchanged session files without invoking the provider parser, so the
codex-cache version bump alone would never re-attribute already-cached
sessions. This also retroactively repairs #513's native-MCP fix for
users whose files were cached before it shipped.
- Bump CODEX_CACHE_VERSION 4->5 for the provider-internal layer.
Tested: parse cases (bash -lc wrapper, flags-before-call, argv array,
plus info/grep/foo-mcp-cli negatives), and a mutation-verified cache
regression test that fails without the PROVIDER_PARSE_VERSIONS entry.
ReDoS-checked. Reviewed with a Fable 5 adversarial pass (Codex is down);
its two must-fixes (the cache gap and the flags-before-call miss that
would have missed the reporter's own sessions) are folded in.
Fixes#478
Scopes the menubar to one Claude config for multi-config (CLAUDE_CONFIG_DIRS)
setups, with All as the default. Rebased onto main and fixed the review
findings from the original #635:
- Fix a TS2206 build break (a 'type' modifier inside an import type block).
- Reject --claude-config-source with a non-Claude --provider, and scan Claude
only in the scoped branch (a config is Claude-only): fixes provider data
leaking into a scoped query and avoids parsing every provider's corpus.
- Scope the macOS menu-bar figure to the selected config (badge matched the
popover), clear the selection when switching to a non-Claude provider tab,
and stop the on-disk badge fallback from showing an unscoped number while
scoped.
- Tag Claude Desktop / Cowork sessions as their own 'claude-desktop' source so
they are a selectable bucket instead of silently vanishing from per-config
views (sum of options now equals All).
- Skip the redundant Claude discovery walk for plain single-config users while
keeping idle configs and Claude Desktop selectable.
Reviewed by Codex 5.6; all findings addressed. Full suite: 1581 TS tests, 76
Swift tests, tsc clean.
A planted or corrupt .agent.json that is valid JSON but has a non-string
field (e.g. agent_name as an object) reached sanitizeProject().trim() and
threw. discoverAllSessions loops providers with no try/catch, so that one
file broke usage discovery for every provider, not just LingTai. Normalize
the manifest to string-or-undefined at read time; add a wrong-typed-field
regression test.
Maintainer follow-up:
- Derive JetBrains dedup keys from the reply content (sha256 prefix plus a
per-hash occurrence counter) instead of the blob's scan position. Copilot
is a durable provider: cached turns are never deleted and a re-parse
appends any unseen key, while MVStore compaction can rewrite the store
with blobs in a different byte order. With positional keys, a rewrite
that moves a new blob ahead of an old one hands the new turn the old
key (skipped as seen) and re-emits the old turn under a fresh index,
double-billing it. Covered by a regression test that fails on the
positional scheme.
- Add CODEBURN_COPILOT_JETBRAINS_DIR to the env-isolation cleared list so
a developer's real JetBrains store never bleeds into fixture tests.
Four fixes for the Kiro IDE provider:
1. Add 'entries' to extractText() key list — Kiro IDE stores message
content in context.messages[].entries (not .content), causing the
parser to extract 0 chars from every execution file.
2. Check data.context[key] for conversation arrays in parseModernExecution
— current Kiro builds store messages at data.context.messages, not at
the top-level data.messages path the parser was checking.
3. Scan both ~/.kiro-server/data/... AND ~/.config/Kiro/... on Linux —
remote dev boxes use .kiro-server while local installs use .config/Kiro.
Both can have data simultaneously; the old code short-circuited on the
first path found.
4. Discover and parse workspace-sessions/<base64>/*.json files — newer
Kiro builds write session state here with history[].message format.
Skips stub entries (executionId refs + 'On it.' only) to avoid
double-counting with execution files parsed separately.
5. Add kiro: 'ide-parsing-v1' to PROVIDER_PARSE_VERSIONS for automatic
cache invalidation — users upgrading from the broken parser will get
a fresh re-parse without manually clearing session-cache.json.
Bonus: Extract tool names from usageSummary[].usedTools, add chatSessionId
to session ID resolution, add Kiro-specific tool name mappings.
AI-Origin: human
JetBrains Copilot plugin ≤1.5.x (e.g. 1.5.59-243) stores all session turns
inside ONE large binary-framed outer Nitrite document, rather than the
per-turn {"__first__":{"type":"Subgraph",...}} blobs introduced in later
plugins (≥1.12.x, e.g. 1.12.1-251).
In the old format each assistant turn is a UUID-keyed Value entry whose
value field contains a JSON-string-escaped AgentRound record:
{"<uuid>":{"type":"Value","value":"{\"type\":\"AgentRound\",
\"data\":\"{...reply...}\"}"}, ...}
The extractResponseText depth-unescape loop already handles this one extra
level of escaping; the only gap was that extractJetBrainsDbTurns never fed
it the outer document — it only scanned for __first__/Subgraph blobs, which
the old plugin never writes.
Add a fallback that activates when the Subgraph scan produces zero turns but
'AgentRound' text is present in the raw file (old-format signal). It locates
the binary-framed outer document (UUID-keyed Value entry, hex matched
case-insensitively so an uppercase UUID does not fall through to $0), extracts
it with matchJsonObject, and passes it to extractResponseText. Because the outer
document holds every turn in one blob, this emits ONE session-level call per
document (all rounds' replies joined): cost/tokens are correct, only the
per-turn call-count granularity is coarser — an accepted tradeoff for legacy
data. MVStore keeps two identical collection copies; seenReplies dedupes them.
The fallback is guarded by turns.length === 0 so new-format sessions (whose
Subgraph scan succeeds) are completely unaffected and never double-counted.
Tests: old-format doc with multiple AgentRound rounds → 1 call whose token
count equals the two non-empty replies joined (the empty tool-call round is
excluded); an uppercase-UUID variant (fails without the case-insensitive
match); and a guard that new-format Subgraph turns are not double-counted.
docs/providers/copilot.md documents the old format and the one-call-per-session
limitation.
JetBrains Copilot has two turn shapes in the Nitrite .db:
- ask mode — the reply is a `Markdown` record's `text`;
- agent / plan mode (e.g. PyCharm agent sessions, `/plan …`) — the reply is the
`reply` field of an `AgentRound` record, and the `Markdown` record instead
holds the USER's prompt.
extractResponseText only read Markdown, so agent-mode turns yielded no reply
text: they were discovered (session/turn counts showed up) but priced at $0
because output tokens came out zero. On this machine that silently
under-counted a PyCharm session ($0 → $0.35) and several IntelliJ agent turns.
Determine the mode by the PRESENCE of an `AgentRound` record and read only that
record's `reply` (collecting every non-empty round in a multi-round blob).
Crucially, an agent blob whose reply is empty — a failed turn or a pure
tool-call round — does NOT fall back to the Markdown record, so a user prompt
is never mistaken for the assistant's output; such turns bill $0 as before.
Ask-mode blobs (no AgentRound) keep reading Markdown. Plan mode's sidecar
records — Thinking, PendingChanges (proposed diff, under `content`), AskQuestion,
Notification, SubTurn, and file-read `text` results — are never read as output.
Verified across all local stores: the two reply shapes never coexist in one
blob, so the split is unambiguous.
Tests: agent-mode reply extraction (ignoring the prompt Markdown), pure
tool-call rounds → $0, multi-round collection, and a failed agent turn → $0.
docs/providers/copilot.md documents both turn shapes and the ignored sidecar
records.
## What & why
The JetBrains Copilot plugin (IntelliJ, PyCharm, RubyMine, …) stores its
chat/agent sessions under `~/.config/github-copilot/<ide>/<kind>/<storeId>/` —
a location none of the existing Copilot sources (CLI JSONL, VS Code chat
sessions/transcripts, OTel SQLite) read. As a result all JetBrains Copilot
usage was silently uncounted in every CodeBurn report. This adds a reader for
that store so those sessions are discovered, priced, and attributed to the
right project.
## How it works
- **Reader.** The store's session content is a Nitrite `.db` — an H2 MVStore of
Java-serialized documents. It is scanned as `latin1` for byte-offset
stability: no Java deserializer, no new dependency, and it is not SQLite so
`node:sqlite` is not involved.
- **Reply text.** Assistant replies live in nested-escaped
`{"__first__":{"type":"Subgraph"…}}` blobs. The text is recovered by
unescaping one level at a time and, at the depth where the Markdown record's
`data` field is a well-formed one-level-escaped JSON document, reading it
structurally — so a reply containing its own quotes is never truncated or
duplicated (which would otherwise inflate the estimate).
- **Tokens/cost.** The store records no token counts, so output tokens are
estimated from the reply text (`CHARS_PER_TOKEN = 4`, re-decoded
latin1→utf8 so multibyte replies count by codepoint) and every call is marked
`costIsEstimated`. Failed generations (error status, no reply) are billed $0.
- **Sessions.** One `.db` holds many chat tabs; turns are grouped back to their
conversation GUID so the UI shows one session per tab, deduped by reply
content per conversation.
- **Project attribution**, most authoritative first:
1. the plugin-recorded `projectName` field (JetBrains Copilot 1.12+), joined
across kind dirs by store id — the billable turns live in
`chat-agent-sessions`, but the label is usually written into the sibling
`chat-sessions`/`chat-edit-sessions` store. Read length-delimited and
re-decoded latin1→utf8 so non-ASCII repo names round-trip.
2. the `.git` repo root of a referenced `file://` path.
3. a generic `copilot-jetbrains` bucket when neither signal exists.
The conversation title is a chat-thread name, not a project, so it is kept
out of the project field and surfaced as the session label instead.
Override the JetBrains github-copilot root with
`CODEBURN_COPILOT_JETBRAINS_DIR`.
## Docs
- `docs/providers/copilot.md` — full JetBrains section (store layout, latin1
scan, reply extraction, projectName precedence + cross-kind join).
- `docs/providers/README.md` — Copilot storage updated to note the Nitrite .db.
## How to verify
- `npm test -- copilot` and `npx tsc --noEmit` (fixtures reproduce the real
nested-escaped .db framing, including quote- and multibyte-bearing replies).
- End to end against a real install:
`CODEBURN_CACHE_DIR=$(mktemp -d) node dist/cli.js status --provider copilot \
--period all --format menubar-json`
— JetBrains sessions appear By-Project under their real repo names.
- Set `CODEBURN_COPILOT_JETBRAINS_DIR` to a fixture root to parse a controlled
store without touching the real config dir.
Recent Cursor builds renamed the per-workspace composer list from
ItemTable['composer.composerData'] to 'composer.composerHeaders' (identical
{ allComposers: [{ composerId }] } shape). loadWorkspaceMap only read the old
key, so on these builds the composer->workspace map came back empty and every
composer fell through to the 'cursor' orphan bucket, losing per-project
attribution.
Read both keys and merge their allComposers lists (backward compatible with
older installs). Add a regression test covering the new composer.composerHeaders
key and the legacy composer.composerData key.
Verified against a real workspace state.vscdb: composer.composerData absent,
composer.composerHeaders present with composerIds matching the bubbleId/
composerData rows; 21/22 calls now attribute to the real workspace instead of
the 'cursor' orphan bucket.
AI-Origin: human
Validated against a real thread: request_token_usage is keyed by user
message and covered only part of the requests (cumulative was ~3x the
map sum), so per-request calls are now supplemented by one remainder
entry that brings each thread exactly to cumulative_token_usage. Live
run matches the store token-for-token on all four fields.
Zed's DataType enum carries both zstd (the current save path) and json;
verified against crates/agent/src/db.rs, along with the exact TokenUsage
field names, the SerializedLanguageModel {provider, model} shape, and
RFC3339 updated_at serialization.
Reads Zed's agent threads from the single threads.db SQLite store: each
row's zstd-compressed JSON carries the thread model and per-request
Anthropic-shaped token usage, so calls are emitted per request with
exact input/output/cache fields and priced through the existing engine.
In-progress threads with an empty per-request map fall back to the
cumulative counter. zstd comes from node:zlib, which ships it from
22.15; on older Nodes the provider skips with a notice instead of
failing. On-disk format documented by @chatzinikolakisk in #480 and the
schema verified against a real Zed install.
* fix(cursor): use Cursor's real context tokens for input
Current Cursor builds leave the per-bubble tokenCount at {0,0}, so the provider
fell back to estimating input from visible text plus a second agentKv
content-char pass that double-counted the same conversation. Cursor records its
own tokenizer-accurate context size per conversation in
composerData.promptTokenBreakdown (the number behind the in-app context-window
bar); read that and credit it once per conversation for input instead.
Measured on a real local DB: today's Cursor input went 44,873 -> 168,486 tokens,
matching the sum of per-conversation context. The admin portal still counts
cumulative-per-turn plus cache, which are server-side only, so an opt-in Cursor
API stays the path to exact parity.
Output is a reply-text estimate; agentKv is retained for a tools/bash breakdown
in a follow-up.
* feat(cursor): add tools and bash-command breakdown from agentKv
Cursor logs the agent's tool calls (Read, Grep, Glob, Shell, ...) in agentKv
blobs. Join them to conversations via the turn requestId (carried on the bubble's
$.requestId and inherited positionally by the turn's agentKv rows) and attach each
conversation's tool list and Shell commands to the call that carries its input.
Measured on a real DB: Cursor now reports tools {Read, Grep, Glob, Shell,
SemanticSearch} and the executed shell commands, which were previously empty.
Removes the now-superseded parseAgentKv content-char estimate.
* fix(cursor): price composer-2.5 as Sonnet 4.6
composer-2.5 was missing from the built-in Cursor model aliases, so its usage
showed $0. Map it to claude-sonnet-4-6 like composer-2 (per cursor.com/blog).
* fix(cursor): cache version, model attribution, user message join, tool classification
- Bump CURSOR_CACHE_VERSION to 5: parser semantics changed (parseAgentKv
removed, real context tokens from composerData.promptTokenBreakdown),
stale v4 caches would show double-counted agentKv calls.
- Fix model attribution: real input tokens are credited on user bubbles
(type=1) which carry no modelInfo. Add a pre-pass building composerId ->
model from assistant bubbles so pricing/display uses the conversation's
actual model instead of the default cursor-auto/sonnet-4.5.
- Fix buildUserMessageMap: was keying by JSON conversationId (empty in
current Cursor builds). Now extracts composerId from the bubble key,
matching parseBubbles.
- Add 'Shell' to BASH_TOOLS in classifier: Cursor's agent uses 'Shell'
as the tool name, but it was missing from the bash tool set so Cursor
agent turns with shell commands wouldn't classify as bash/build/test.
- Fix null coalescing in loadComposerInputTokens: r.used ?? r.ctx would
fall through on a valid totalUsedTokens of 0. Use explicit null check.
- Decouple agentTools attachment from input credit: tools/bash were only
attached on the first credited turn (creditedHere), silently dropping
tool usage from subsequent turns in multi-turn conversations.
- Update stale comment about parseAgentKv being kept for a follow-up.
- Add tests for real token crediting, once-per-conversation, fallback,
contextTokensUsed, tool/bash attribution, and model attribution.
* fix(cursor): avoid duplicating aggregated agent tools
* fix(cursor): price house composer models from Cursor's published rates
composer-1/1.5/2/2.5 were proxied to Claude Sonnet, overcounting cost
(~6x for composer-2/2.5). Use Cursor's published per-model rates instead,
and note in the parser why local reads undercount the admin console.
Co-authored-by: AgentSeal <hello@agentseal.org>
* fix(cursor): estimate non-Composer turn input from the agent stream
Non-Composer sessions (e.g. GPT) record no context-window meter and keep
the prompt in the agent stream, so the user bubble's own text is empty.
Those turns hit the 0/0-token fallback with text_length 0 and were dropped
entirely, so that model's traffic never appeared in the report.
loadAgentToolsByComposer now also sums the user-role stream text length per
conversation, and the meterless fallback estimates input from it (chars/4),
credited once per conversation, when the bubble text is empty. Turns with no
stream text are left untouched, so no phantom tokens are invented.
* fix(cursor): stable conversation crediting, restored stream coverage, and cache invalidation
Review fixes for the real-token accounting:
Conversation input now lands on one composer-anchored record
(cursor:composer-input:<id>) timestamped at composerData.createdAt, so
the credited day no longer depends on the parse window or cache floors,
daily-cache gap fills dedupe instead of multiplying, and each
conversation picks exactly one input source (real bubble tokenCounts,
the context meter, the agent stream, or visible text) so sources can
never stack or double count. A zero totalUsedTokens no longer shadows
contextTokensUsed.
The agent stream regained what the parseAgentKv removal dropped: tool
and system rows count as context, stream-only replies count as output,
and sessions with no bubble join are emitted again (DB mtime timestamp,
as before). Block-array content is measured by its text, not its JSON
envelope. Rows written before their requestId appears buffer forward
instead of inheriting the previous conversation, and a system row closes
the boundary. Tool names canonicalize to Bash and commands go through
extractBashCommands so cross-provider breakdowns merge; the classifier
no longer special-cases Shell (which also reclassified Copilot turns).
User bubbles consume their own queue entry so assistant replies pair
with the right question, and every cursor call is flagged
costIsEstimated.
The requestId and model joins ride the existing budgeted bubble scan
instead of two new unbounded full-table decodes, and the composerData
read seeks the key range. SQLITE_BUSY now propagates to the parser's
retry path instead of caching a silently degraded parse.
Upgrades actually take effect: the session cache gets a cursor parse
version, DAILY_CACHE_VERSION bumps to 10 so finalized days re-hydrate
under the new accounting, the cursor results cache bumps to v6, and the
builtin composer rates participate in the price config hash (rates now
cite cursor.com/docs/models).
Verified against a real Cursor store: all metered conversations match
the on-disk meter exactly, narrow and wide parse windows anchor
identically, repeat runs are byte-identical, and agentKv-only sessions
reappear.
---------
Co-authored-by: ozymandiashh <234437643+ozymandiashh@users.noreply.github.com>
* fix(devin): report friendly GPT model names with effort tier (#487)
Devin transcripts label each step with a generation_model in dash form with
an effort suffix (gpt-5-3-codex-xhigh). getShortModelName is keyed in dot form
(gpt-5.3-codex), and its version-boundary check matches the dash id against the
base gpt-5 entry, collapsing every Devin GPT variant to GPT-5.
Normalize the dash form to canonical dot form, map through the short-name table,
and surface the effort tier, e.g. GPT-5.3 Codex (xhigh). Fall back to the
friendly model_name when generation_model is an opaque MODEL_* id, and read
extra.generation_model so ATIF v1.7 transcripts behave like v1.4. Devin rows
in the JSON report and model-efficiency now key on the friendly name.
* fix(devin): stop dated GPT snapshots being mislabeled as versions
The friendly-name path treated any two-number dash id (gpt-4-1106-preview) as a
dotted minor version, producing corrupt labels like 'GPT-4.1106 Preview'.
- Restrict the dash-to-dot rewrite to a single-digit minor at a token boundary,
matching Devin's real ids (gpt-5-3-codex) while leaving dated snapshots alone.
- In getFriendlyGptName, defer to getShortModelName when any suffix token is
purely numeric, so unknown snapshot ids pass through raw instead of being
fabricated into a fake friendly name.
Adds a regression case (gpt-4-1106-preview) to the Devin variant matrix.
* chore: remove accidental node_modules symlink
An absolute-path node_modules symlink was committed by mistake. It leaked a
local username, was a dangling symlink for everyone else, and broke git/npm on
checkout (git won't replace a real node_modules/ with the symlink). It slipped
past .gitignore because the ignore rule is 'node_modules/' with a trailing
slash, which matches a directory but not a symlink.
Pi and OMP have no dedicated skill tool: a native skill load is emitted as
an ordinary `read` tool call whose path points at the skill's SKILL.md (or a
`skill://<name>` URI in newer OMP builds). The parser mapped every read to the
Read tool and never populated `skills`, so Pi/OMP sessions over-counted Reads
AND always showed an empty "Skills & Agents" breakdown.
Detect these reads (basename === 'SKILL.md', or a skill:// URI), extract the
skill name (parent directory, or the URI segment), and surface the call as the
`Skill` tool with the name recorded in `skills` -- exactly how the Claude
parser represents a skill invocation. That both removes the Read over-count and
lets the shared classifier tag the turn `general` so the Skills & Agents
breakdown picks it up (populating a field the dashboard never received before).
The path is read from arguments.path with a defensive arguments.file_path
fallback.
Tests cover SKILL.md / skill:// / file_path detection, a non-skill read staying
a Read, and an end-to-end check that a parsed skill load reaches the classifier
subCategory that feeds skillBreakdown.
VS Code GitHub Copilot Chat users with no OTel agent-traces.db and no
~/.copilot/session-state/ saw $0.00 cost: codeburn never read VS Code's
core chat persistence, the only on-disk source carrying their token counts.
Add a fourth Copilot source that reads the VS Code chat delta-journals at
workspaceStorage/<hash>/chatSessions/*.jsonl and
globalStorage/emptyWindowChatSessions/*.jsonl. The files are a kind:0
snapshot / kind:1 path-set / kind:2 array-append journal; we replay it
(prototype-pollution-safe: __proto__/prototype/constructor segments are
rejected and containers use Object.create(null)) and read each request's
result.metadata.promptTokens / outputTokens (falling back to
completionTokens) and resolvedModel for pricing.
Dedup so users with multiple sources are not double-counted: prefer OTel
(skip chatSessions discovery when an OTel source is present), and skip a
workspace's legacy GitHub.copilot-chat/transcripts when that workspace has
chatSessions. New env overrides CODEBURN_COPILOT_GLOBAL_STORAGE_DIR and the
existing CODEBURN_COPILOT_WS_STORAGE_DIR keep discovery testable.
Tests cover the reporter's real request shape (promptTokens 32543 /
outputTokens 60 -> non-zero cost), empty sessions, emptyWindow discovery,
append-then-edit replay, requestId dedup, prototype-pollution paths, the
transcript skip, and the OTel-prefer skip.
The Skills & Agents panel was always empty for OpenCode sessions even
when skills and subagents were used. buildAssistantCall (shared by the
OpenCode SQLite and file-based parsers, and Kilo Code) counted the
skill/task tool invocations but never read the skill name or subagent
type from the tool-call input, so the dedicated breakdowns had no names
to aggregate.
In OpenCode's part files the identifier lives in state.input: the skill
tool carries input.name and the task tool carries input.subagent_type.
Extract both in buildAssistantCall and surface them on
ParsedProviderCall.skills / .subagentTypes, then stop hard-coding
skills: [] in providerCallToTurn and providerCallToCachedCall so the
classifier's subCategory and the subagent breakdown receive the data.
Verified against real OpenCode data: the previously empty skills[] and
subagents[] now populate (pipeline-investigation, plan-spec, splunk, ...
and explore/general subagents).
Fixes#556
Co-authored-by: Kevin Addison <kevin.addison3@tesco.com>
* fix: add mssing support for ATIF v1.7
Co-Authored-By: bmcdonough <18721778+bmcdonough@users.noreply.github.com>
* fix(devin): drop unused MCP-coupled types, dead guard, harden metrics fallback
- Remove the @modelcontextprotocol/sdk JsonSchemaType import and the unused
ToolDefinition/FunctionDefinition types it served; type Agent.tool_definitions
as unknown since the parser never reads it (no dependency warranted).
- Remove isImageContentPart: unused, and its `"image" in part` check could never
be true (image parts carry `source`/`type`, not `image`).
- Use the `type` discriminant in isTextContentPart instead of property presence.
- getMetricsFromStep: fall back to legacy metadata.metrics when step.metrics is
present but empty, so a partial metrics object cannot silently zero usage.
- Tests: cover the empty step.metrics fallback and image-only message normalization.
---------
Co-authored-by: bmcdonough <18721778+bmcdonough@users.noreply.github.com>
Co-authored-by: AgentSeal <hello@agentseal.org>
* fix(antigravity): read current agy antigravity-cli on-disk layout
* fix(antigravity): propagate SQLITE_BUSY from the .db read so the run retries
---------
Co-authored-by: AgentSeal <hello@agentseal.org>
* fix(cursor-agent): ingest workspace-less CLI transcript layout
* fix(cursor-agent): bump parse version so cached sessions pick up the new ingestion
---------
Co-authored-by: AgentSeal <hello@agentseal.org>
gpt-5.3-codex-spark is a distinct model variant, but the longest-first
startsWith(key+'-') matcher (intended for reasoning suffixes -high/-low)
swept it into the base 'GPT-5.3 Codex' label, making a distinct model look
like the retired base in today/models/status output. Add an explicit
display entry in SHORT_NAMES and the codex provider so the longer key wins;
-high/-low still fall through to the base. Pricing is unaffected (LiteLLM
has no spark entry; cost falls back to the base rate as before).
Fixes#461
- Read the populated sessions.cwd column for project grouping; fall back to
scraping the transcript, then the profile name. The current Hermes build no
longer writes "Current working directory:" lines into messages.
- Set costIsEstimated when the figure is the LiteLLM fallback (no recorded cost).
- Re-throw SQLITE_BUSY from the discovery and read paths so a transient lock on
the live state.db is retried instead of being cached as an empty result.
- Tests: add the cwd column to fixtures; cover cwd grouping, the estimated flag,
and tool-result tool_name extraction.
Reads session-level usage from Hermes SQLite state databases
(~/.hermes/state.db and per-profile state.dbs). Tracks input,
output, cache read/write, reasoning tokens, stored costs, tools,
shell commands, and inferred projects.
- Provider reads both root and profile state databases
- Cost fallback: actual_cost_usd > estimated_cost_usd > LiteLLM pricing
- inferProject filters to user/system messages only
- Discovery query capped at LIMIT 10000
- sanitizeProject handles repeated leading separators
- All five review items from PR #386 addressed
- Tested against real Hermes data (557MB state.db with 198 sessions)
Closes#368. Supersedes #386.
Reads ZCode CLI usage from ~/.zcode/cli/db/db.sqlite. The model_usage
table has exact per-request tokens; cost is computed from the pricing
table since ZCode stores none (GLM-5.2 runs on the z.ai start-plan
subscription).
- Split cached tokens out of input_tokens (OpenAI-style) so fresh input
and cache reads price correctly
- Attach each turn's tool calls to one request to avoid double-counting
- Map GLM-5.2 to glm-5p1 (GLM-5.1 rate) until LiteLLM lists it
- Register as a lazy SQLite provider; add test and provider doc
* fix: fix and improve test isolation and collision with environment
* docs: remove unnecessary comment
* test(env-isolation): clear CODEBURN_FORCE_MACOS_MAJOR and pin TZ
Two env vars read in src/ were not isolated: CODEBURN_FORCE_MACOS_MAJOR
(now cleared so it cannot leak between tests) and TZ (now pinned to UTC,
since clearing it falls back to the OS zone and would shift date buckets
versus a clean CI runner).
---------
Co-authored-by: AgentSeal <hello@agentseal.org>
OpenCode 1.1+ stores sessions as file-based JSON under
storage/{session,message,part}/ instead of a SQLite DB, so the
SQLite-only provider discovered zero sessions on current installs and
reported no usage at all.
Add a file-based discovery and parser path, preferred when present and
falling back to the SQLite DB for pre-migration installs. Extract the
shared message-to-call logic (token extraction, tool and bash parsing,
cost) into session-message.ts so the file path, the SQLite path, and
Kilo Code stay identical.
Adds Grok Build (xAI coding CLI) as an eager provider with caching- and compaction-aware token estimation, real tool/shell extraction, Skills & Agents from spawn_subagent, grok-build pricing, and SuperGrok plan presets. Tested against real sessions; full suite green.
* Add zerostack provider scaffold
Register zerostack (gi-dellav/zerostack) as a core provider reading
plain-text sessions from $XDG_DATA_HOME/zerostack/sessions/. Parser is
modeled on pi.ts; on-disk schema is unverified and must be confirmed
against real sessions before a PR (see docs/providers/zerostack.md).
* Rework zerostack provider against real session schema
Replace the initial scaffold (modeled on pi.ts JSONL) with the verified
format from a real local run and the zerostack source:
- Sessions are one JSON file per session under the platform data dir
(~/Library/Application Support/zerostack/sessions on macOS,
$XDG_DATA_HOME/zerostack/sessions on Linux; ZS_DATA_DIR overrides).
- Tokens are cumulative session totals, not per-call, so emit one
ParsedProviderCall per session from total_input/output_tokens.
- Recompute cost via LiteLLM (calculateCost); OpenRouter ids arrive
route-prefixed and resolve through canonical names.
- zerostack persists only final assistant text, so tools/bash are empty.
Verified against a real session (DeepSeek v4 Pro via OpenRouter):
34.1K in / 961 out / $0.016, matching the recorded total_cost. Adds a
fixture-based test and rewrites the provider doc to match.
Recent Codex sessions report completed MCP tool calls as an event_msg with
type mcp_tool_call_end instead of a function_call response_item. The parser
only built its tool list from function_call and patch_apply_end events, so
these MCP calls were never attributed: token usage was still counted, but the
MCP tool itself was missing from recent lookback windows (today/week/month)
while older history still showed it.
Add a handler that reads invocation.server and invocation.tool from the
event and rebuilds the canonical mcp__<server>__<tool> name the classifier
recognizes. Bump the Codex result cache to v4 so sessions cached under v3
re-parse and pick up the previously dropped MCP calls.
Validated on real local data: 5 mcp_tool_call_end events that were dropped
before are now attributed as mcp__node_repl__js, with no change to the total
call count.
Fixes#478
Large Cursor DBs were scanned as "the most-recent 250k bubbles by ROWID",
which dropped in-range older sessions from long-range reports and warned even
when the requested window fit comfortably. The bubble timestamp lives in the
JSON value (no index), so the date filter forces a full decode per row, which
is why a scan bound exists.
Replace the blind cap with a range-aware paged scan: for DBs over the budget,
page ROWID-descending, keep only rows within the window (createdAt > timeFloor),
and stop once a full page falls past the window floor. The hard budget remains
as a backstop for genuinely enormous in-range scans, and the "older sessions
may be missing" warning now fires only when that budget is actually hit.
Effect: short ranges decode far fewer rows and no longer warn; long ranges
return the full window when it fits the budget; truncation keeps the newest
in-range bubbles and warns only then. Small DBs are unchanged (un-paged query).
Budget is overridable via CODEBURN_CURSOR_MAX_BUBBLES for tests.
The Kiro provider previously only detected sessions from the Kiro IDE
(VS Code-based) stored in globalStorage. This adds support for Kiro CLI
(`kiro-cli chat`) sessions stored in ~/.kiro/sessions/cli/.
CLI sessions use a different format:
- .jsonl files with Prompt/AssistantMessage/ToolResults entries
- Companion .json files with session metadata, model info, and
per-turn metering usage (credits)
Changes:
- Add CLI session discovery (scans ~/.kiro/sessions/cli/ for .jsonl)
- Add CLI JSONL parser that extracts turns, tools, and cost from
metering_usage credits
- Add CLI tool name mappings (read, write, shell, grep, glob)
- Make CLI sessions dir configurable via third param to
createKiroProvider for testability
- Add unit tests for CLI session discovery and parsing
The synthetic source path has no on-disk file, so fingerprintFile returned
null and parseProviderSources dropped the source before parsing — the provider
always reported $0 even with a key set. Add a `network` provider flag; such
sources skip the fingerprint gate, re-fetch each run with a synthetic
fingerprint, and keep the gateway's reported cost (instead of recomputing from
tokens, which would be $0 for any model codeburn can't price). Adds an
end-to-end test through parseAllSessions.
Opt-in Vercel AI Gateway provider (AI_GATEWAY_API_KEY/VERCEL_OIDC_TOKEN), inert without a key. Cursor lookback now period-aligned with a 6-month floor. Gateway fetch hardened on merge with an 8s timeout and try/catch fallback.
