agent-log-forensics

name	agent-log-forensics
description	Scan Claude/Codex session logs to find agent behavior patterns, Toolsmith adoption gaps, repeated frustrations, and candidates for new skills/tools.
display_name	Agent Log Forensics
brand_color	#6D28D9
local_only	true
group	Dev Workflow
usage	/agent-log-forensics:run
summary	Mine your AI coding sessions for the habits that quietly waste time, and turn them into workflow fixes.
default_prompt	Scan recent Claude and Codex session logs on this machine and any named remote hosts. Report Toolsmith adoption, lost opportunities, recurring frustrations, and concrete new skills/scripts to add. Keep examples privacy-light.

Agent Log Forensics

Use this when the goal is not one bug fix, but making the human+agent system smarter after observing real sessions.

Third-order stance: skip the obvious "agent forgot tool" answer. Ask what hidden loop, missing affordance, brittle instruction, or human workaround made the tool forgettable in the first place. Prefer new mental paths over more reminders.

When to Use

• The user asks whether agents are using a tool, skill, MCP server, or workflow
• The user wants "lost opportunities" where agents should have used better tools
• The user asks for patterns in interactions, repeated frustrations, jank, or productivity bottlenecks
• The user asks "why did that take so many turns?", "why did you struggle?", or "how do we level this up?"
• You need to turn observed behavior into new user-level skills, scripts, or project instructions

When NOT to Use

• One isolated transcript or pasted error is enough
• The user asks for a narrow code change unrelated to agent behavior
• Logs may contain sensitive data and the user did not ask for forensic analysis

---

Phase 1: Scope the Observation Window

Default to the last 7 days unless the user names a different range. Include remote hosts explicitly named by the user, commonly vesta.

Prefer Toolsmith's built-in scanner when available:

toolsmith scan-agent-logs --days 7 --max-examples 12
toolsmith opportunities --days 7 --max-examples 8
toolsmith scan-agent-logs --days 7 --remote vesta --max-examples 12

If the scanner is not installed yet, say that and fall back to a lightweight count of recent ~/.claude/projects/**/*.jsonl and ~/.codex/sessions/**/*.jsonl files rather than dumping raw prompts.

Phase 2: Preserve Privacy

• Do not paste raw user prompts or full transcript content.
• Redact home paths, secrets, tokens, keys, cookies, project-private filenames when they look sensitive.
• Prefer counts, categories, tool names, workspace basenames, and short sanitized examples.
• Treat shell commands with secrets as sensitive snippets.

Phase 3: Classify Findings

Create five sections:

1. Tool adoption — registered vs actually called, by agent/client, with evidence counts.
2. Lost opportunities — where the agent used a worse tool. Separate hard misses from candidates.
3. Behavior/productivity themes — repeated user frustrations, recurring workflow patterns, and quality gaps.
4. Shadow workflows — places the human manually intervened, re-ran commands, pasted exact instructions, or corrected the agent after loops. Treat these as highest-value skill/script candidates.
5. Silence anomalies — expected logs, tool calls, or telemetry are missing. Lack of evidence can mean broken instrumentation, not success.

For Toolsmith, classify:

• Hard miss: native Read/Edit/Write, cat, nl, or broad sed -n on >200-line files.
• Candidate: apply_patch on a large file after a search/read could have used anchors, but may still be acceptable.
• Positive adoption: file_skeleton, find_and_anchor, get_function, anchored_read, anchored_edit, symbol_replace.

Phase 4: Convert Observations Into Improvements

For each repeated pattern, propose one of:

• Skill — when judgment, sequencing, or cross-tool behavior matters.
• Script — when the action is deterministic and repeatable.
• Project instruction — when it is repo-specific and should activate automatically.
• Tool/MCP improvement — when missing affordances prevent agents from doing the right thing.

Use this table:

Signal	Better artifact
Same review checklist repeated	Skill
Same shell command chain repeated	Script
Same repo-specific gotcha repeated	CLAUDE.md / AGENTS.md update
Agents know the right thing but forget	Prompt snippet / guardrail
Agents cannot inspect evidence cheaply	Toolsmith/MCP feature
User manually intervenes after agent loops	Skill or deterministic helper that removes the human bridge
Expected telemetry is silent	Logging/instrumentation fix before drawing conclusions
Same failure appears across projects	User-level skill plus project opt-out, not per-repo copy-paste

Phase 5: Third-order Synthesis

After listing findings, do one further pass:

• First-order: what tool or skill should have been used?
• Second-order: why did the agent not choose it under real pressure?
• Third-order: what new path would make the better behavior feel inevitable, delightful, or weirdly obvious next time?

Name the proposed path in memorable language, for example "shadow workflow collector", "silence detector", "release exit-door drill", or "jank weather report".

Phase 6: Write the Report

Store reports in:

• Temporary artifacts: ~/dev/agent-notes/<project>/
• Durable reports: Obsidian project folder if the user asks or the work should guide future sessions

Include:

• Date range and hosts
• What was scanned
• Adoption verdict
• Lost-opportunity examples, sanitized
• Skills/scripts/tools to add next
• Third-order ideas worth trying even if they sound whimsical
• Follow-up observation plan

Use scripts/collect_toolsmith_scan.py to run local and remote Toolsmith scans and write a combined Markdown report.

• Use remote-host-verifier when comparing a command across local and remote hosts.
• Use skill-creator when turning findings into new skills.
• Use status-copy-trust-audit when confusing CLI output appears repeatedly in logs.
• Use beads-knowledge or the project tracker to preserve hard-won patterns that should not be rediscovered.
• Use quick-check --cheap for broad synthesis and a different model's view before writing new skills.