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/danielsmithdevelopment/clawql/deep-thinkingnpx skills add danielsmithdevelopment/ClawQL --skill deep-thinkinggit clone --depth 1 https://github.com/danielsmithdevelopment/ClawQLWhat 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 | $0.00077 | $0.01581 |
| Opus 5 | $0.00039 | $0.00790 |
| Sonnet 5 | $0.00015 | $0.00316 |
| Haiku 4.5 | $0.00008 | $0.00158 |
Grade C, and why
deep-thinking scanned grade C 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
- Launch a multi-minute/hour eval, train, or data wipe (`rm -rf output/…`) How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep thinking (chain of thought)
Externalize a written reasoning chain before irreversible or expensive
actions, and a short after-action when outcomes falsify assumptions. The
goal is not ceremony — it is to catch shortcut reasoning (e.g. nohup in a
dying agent shell) before it burns a run.
This is thinking on the page, not a silent checklist. Prefer prose that shows why, then compress for the user-facing reply.
When to run (gates)
Invoke before acting when any of these are true:
- Kill / restart / rebind a long-lived process (inference, MCP, Docker, tunnels)
- Launch a multi-minute/hour eval, train, or data wipe (
rm -rf output/…) - Choose a new start pattern over a known-good one already used this session
- Symptoms could be process-lifecycle vs app-crash vs model-quality
- User asks to “think hard,” “CoT,” “deep think,” or explain a decision
- About to claim a root cause from a single datapoint
Skip for trivial edits, pure Q&A, or when the user already dictated the exact command.
How to use
- Write the chain (tool scratch, todo note, or a short internal block) using the template below — fill every section; mark unknowns as unknowns.
- Act only after the Decision section names the pick and why alternatives lost.
- After-action when the result surprises you or burns >~5 minutes — update
assumptions and leave one sticky takeaway (optionally
memory_ingestif vault tools are available).
Do not dump the full chain into every user reply unless they asked for it. User-facing: 2–6 sentences of the conclusion + the sticky takeaway. Keep the full chain in the agent trail.
Chain template (fill in order)
Copy and complete:
### CoT — <short decision title>
#### 1. Goal
What success looks like for *this* step (not the whole project).
#### 2. World state (observed, not hoped)
- Processes / ports / terminals still alive:
- What was started how (Cursor background terminal vs nohup vs systemd vs Docker):
- What dies if *this* shell exits:
- Artifacts / run ids / last known-good scores:
- Clock / duration expectations:
#### 3. Evidence so far
Bullet facts with sources (log line, exit code, healthz, pack_errors).
Separate **signal** from **interpretation**.
#### 4. Assumptions (explicit)
| # | Assumption | If false, what breaks? | How to falsify quickly? |
|---|------------|------------------------|-------------------------|
| A1 | … | … | … |
Challenge at least one assumption that feels “obvious.”
#### 5. Hypotheses (competing)
H1: …
H2: …
H3: …
What would we observe if each were true?
#### 6. Options
For each option: steps, durability, blast radius, time cost, reuse of known-good patterns.
| Option | Durability | Blast radius | Time | Notes |
|--------|------------|--------------|------|-------|
| O1 … | | | | |
| O2 … | | | | |
#### 7. Decision
Pick: Ox
Because: …
Rejected: … because …
Risk I am accepting: …
Rollback: …
#### 8. First verification
The smallest probe that proves the decision held (healthz, one smoke chat,
one doc, listener still up after parent shell ends).
#### 9. Stop conditions
Abort / rethink if: …
What ships with it
1 file 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.
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 · 165 lines · 77 tokens per session scan C dd44ac365ee6
deep-thinking is a skill published in the GitHub repository danielsmithdevelopment/ClawQL (12 stars, last pushed 2d ago), licensed Apache-2.0. It adds 77 tokens to every session and 1,581 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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