Session Profiler

Session Profiler

Turn one Hermes main transcript plus its subagent transcripts into analysis artifacts. Route every operation through the bundled wrapper so it uses a consistent environment and remembers the latest parsed work directory.

SP="$SKILL/scripts/sp"

Resolve SKILL to this skill's directory. If it is unavailable, use the absolute path to scripts/sp beside this file.

Workflow

1. Find and inspect the session.

$SP projects
$SP list [--provider hermes] [--n 20]
$SP list --project-dir "${HERMES_HOME:-$HOME/.hermes}/sessions"
$SP find 
$SP info 

Hermes stores rollouts under ${HERMES_HOME:-~/.hermes}/sessions/YYYY/MM/DD/rollout-*.jsonl and archived rollouts under archived_sessions/. Hermes subagent rollouts identify their parent thread recursively in session_meta.

1. Parse the session before analyzing it. Re-run this for a session that is still active.

$SP parse 

The command prints and remembers its work directory. It writes events.parquet, events.csv, and agents.json. Override the remembered directory with global --data-dir or SESSION_DATA_DIR.

1. Start with a readable profile, then narrow the investigation.

$SP brief
$SP agent-summary
$SP costs
$SP slowest-tools --n 20 [--agent ]
$SP tool-breakdown [--agent ]
$SP inference-vs-tool
$SP errors
$SP turns
$SP timeline 2026-07-08T19:14:00Z --window 120
$SP events [--agent main] [--tool Bash] [--grep rebase] [--since 2026-07-08] [--n 30] [--long]

Use brief first when explaining a session to a person. It writes brief.md with the session's headline metrics, standout slow paths or failures, prompt trail or TOC storyline, and an agent scoreboard. Use the table commands for specific questions after the brief identifies the interesting region.

For ad hoc pandas analysis, set SESSION_DATA_DIR, add scripts/ to PYTHONPATH, and use from session_profiler.dataset import load; df = load() inside the wrapper's venv.

1. Summarize the session and preserve lessons for the next run.

$SP toc --dry-run
$SP toc
$SP review

Use toc --dry-run to inspect the sanitized, junk-free digest. toc builds a local chronological table of contents and writes toc.json plus toc.md without calling an external agent CLI. Show toc.md to orient the user. review writes review.md with deterministic observations about failed calls, slow paths, active-time mix, delegation, and estimated cost. Turn those observations into concrete changes to prompts, tool batching, preflight checks, or subagent assignments; do not claim the profiler automatically changes agent behavior.

1. Build and open the trace.

$SP trace
$SP open

Load the emitted trace.json.gz in Perfetto. Read it top-down: Session overview gives the whole run, Table of contents shows the story when toc.json exists, each agent's work timeline shows inference and tools, prompt trail marks user turns, and usage tokens + cost shows cumulative counters. Click a slice to inspect sanitized input/output previews, duration, concurrency, failure state, token count, and estimated cost.

Interpretation Rules

Trust these derivations instead of recomputing them from raw rows:

• For transcript-style JSONL, deduplicate usage per API message.id: content-block records repeat a growing usage snapshot, so keep only the final snapshot once. For Hermes, consume last_token_usage once per token_count event and treat cached_input_tokens as a subset of input tokens.
• Sum tokens across agents. Subagent token usage lives in the subagent transcript, not the parent transcript.
• Measure a tool from tool_use to its matching tool_result within the same transcript. Treat multiple tool uses sharing one assistant message as concurrent.
• Measure inference from the first to last assistant record for one message ID. Do not treat human idle time as inference.
• Measure each subagent's wall runtime from the first to last timestamp in its own transcript, not from the parent's spawn tool span.
• Treat active-time totals as sums of spans, not wall time. Concurrent tools and agents can make summed active time exceed elapsed wall time.
• Label all dollar values as estimates. Hermes uses model-specific public API rates when the model is known, including long-context multipliers recorded by the pricing docs. Subscriptions, credits, priority processing, tool fees, and negotiated rates can differ; an unknown model is marked unavailable instead of guessed.

Privacy And Failure Handling

• Treat transcripts and traces as sensitive. Previews redact long opaque tokens and common auth/cookie headers, but can still contain source code, prompts, paths, personal data, or secrets. Review before sharing and share only where the session owner would.
• Expect UTC timestamps throughout.
• If parsing a growing transcript, rerun parse immediately before final analysis.
• If dependency bootstrap fails, report the uv or pip error and let the user fix local network/package configuration. Do not add machine-specific proxy settings to this skill.
• If toc cannot build a table of contents from the parsed data, keep the parsed data and analyses; use toc --dry-run as the session digest and explain which timestamps or events were missing.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: tamdogood
• Source: tamdogood/builder-essential-skills
• License: MIT
• Homepage: https://www.npmjs.com/package/@tamng0905/builder-essential-skills

Install and usage instructions live in the source repository linked above.