Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/techwolf-ai/ai-first-toolkit/task-profilenpx skills add techwolf-ai/ai-first-toolkit --skill task-profilegit clone --depth 1 https://github.com/techwolf-ai/ai-first-toolkitWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00123 | $0.03140 |
| Opus 5 | $0.00062 | $0.01570 |
| Sonnet 5 | $0.00025 | $0.00628 |
| Haiku 4.5 | $0.00012 | $0.00314 |
Grade B, and why
task-profile scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Enumerates other installed skillsmediumAgent snooping
Other skills' SKILL.md files reveal prompts, capabilities and secrets that should be invisible to peers.
ls ~/.claude/skills/ 2>/dev/null How it starts
The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
task-profile
Platforms: Claude Code / Cowork and Codex.
scripts/inventory.pydetects the host (via theplatformstampinstall.shwrites, orAI_FIRST_PLATFORM) and routes: Claude Code (~/.claude/projects) + Cowork transcripts, or Codex rollouts (~/.codex/sessions), building the same session condensate + token aggregates either way. Antigravity is unsupported: its IDE store is AEAD-encrypted at rest and its CLI store has no parseable turn content, so the skill prints a clear "not available" message and exits.
End-to-end skill: session inventory → LLM clustering → parallel Haiku analysis → aggregation → branded explorer HTML + shareable CSV + atomic skill proposals.
When to run
When the user asks to understand their own Claude usage patterns: what tasks they repeat, how much friction those tasks generate where tokens go which principles they already follow vs. where they slip, and which new skills would compound across many tasks.
Prerequisites
- Session history on this machine:
- Claude Code:
~/.claude/projects/*/\*.jsonl - Claude Cowork:
~/Library/Application Support/Claude/local-agent-mode-sessions/*/*/local_*/audit.jsonl
- Claude Code:
- The
session-searchskill is already installed at~/.claude/skills/session-search/(optional but recommended; this skill does its own inventory pass). - None beyond Python 3, the HTML generator ships with its own light theme baked in. No external design or logo skill required.
Workflow
Run from any working directory, outputs land under ./out/ in that directory.
Phase A, Inventory (deterministic script)
~/.claude/skills/task-profile/scripts/inventory.py --out out/inventory.json
Flags: --since YYYY-MM-DD, --until YYYY-MM-DD, --all (default window: last 6 months).
Writes per-session rows with: summary, token totals (per model, from message.usage), automation flag + reason, and a structured condensate (intent turns + correction turns + tool-flail episodes + outcome turns). Automated sessions (paperclip, scheduled-task, sdk-cli, ditto-routine) are flagged and excluded from downstream analysis but kept for transparency.
What ships with it
15 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/logo.svg 1.9 KB
- references/ai-first-principles.md 5.8 KB
- references/automation-filters.md 2.6 KB
- references/friction-signals.md 2.3 KB
- references/personas.md 11 KB
- references/redaction-rules.md 2.8 KB
- references/success-rubric.md 2.0 KB
- references/task-style.md 4.3 KB
- scripts/build_explorer.py 49 KB runs code
- scripts/codex_sessions.py 8.8 KB runs code
- scripts/host_platform.py 3.8 KB runs code
- scripts/inventory.py 24 KB runs code
- scripts/persona_emblems.py 13 KB runs code
- scripts/persona_features.py 9.7 KB runs code
- scripts/write_profile.py 13 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 212 lines · 123 tokens per session scan B 19ced81f8f75
task-profile is a skill published in the GitHub repository techwolf-ai/ai-first-toolkit (96 stars, last pushed 1mo ago), licensed MIT. It adds 123 tokens to every session and 3,140 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it B with 1 finding (enumerates other installed skills). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Use whenever asked to set up, onboard, initialize, or spec a project — the front door when the workspace has no spec graph yet (brand-new or an existing codebase); also seeded by the app's Set-up-project card (/skill:setting-up-a-project). Not for feature work in an already-specced project — use the brainstorming…
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Use when a workflow step drafts or revises a spec artifact — a goal-and-requirements, an architecture, or a module SPEC — or when a workflow skill names it at such a step. The shared quality bar for specs — not a workflow, nothing to execute.
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