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 skills add product-on-purpose/thinking-framework-skills --skill think-decision-journalgit clone --depth 1 https://github.com/product-on-purpose/thinking-framework-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/product-on-purpose/thinking-framework-skills/think-decision-journal)<a href="https://agentmods.dev/skills/product-on-purpose/thinking-framework-skills/think-decision-journal"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-decision-journal/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/product-on-purpose/thinking-framework-skills/think-decision-journal"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-decision-journal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00109 | $0.01348 |
| Opus 5 | $0.00055 | $0.00674 |
| Sonnet 5 | $0.00022 | $0.00270 |
| Haiku 4.5 | $0.00011 | $0.00135 |
Grade A, and why
think-decision-journal scanned grade A with 0 findings 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 12d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Decision Journal
A decision journal records a decision at the moment it is made - the decision, the rationale, the predicted outcome, an explicit confidence level, and the assumptions it rests on - so it can be reviewed later against what actually happened. The load-bearing move is timing: the prediction is fixed in place before the outcome is known, while the reasoning and the felt confidence are still uncontaminated by the result. That contemporaneous record is the one reliable defense against hindsight bias ("I knew it all along"), it separates decision quality from outcome quality, and it supplies the recorded-prediction half of a calibration loop. The output is a structured decision journal entry, not prose, designed to be reopened and scored later.
When to Use
- At the point of committing to a consequential, genuinely uncertain decision: a launch, hire, investment, vendor choice, bet, or strategic direction.
- When you can still state an honest prediction, confidence level, and set of assumptions before the outcome is known.
- When you intend to review the decision later against reality - it pairs with an after-action review (record now, review later).
- When you want to build calibration over many decisions, not judge a single one.
When NOT to Use
- To review a decision after the outcome is already known. That is an after-action review (
think-after-action-review); writing a "journal entry" after the result back-fits the prediction, the exact distortion this method exists to prevent. - For trivial or fully reversible (two-way-door) decisions. The capture overhead is not worth it for a cheaply undone choice with no real uncertainty.
- When no expectation can be honestly stated. If there is no genuine prediction, confidence, or assumption to record, the entry is theater.
- To surface only the conditions that must hold for a choice to be right. That is
think-what-would-have-to-be-true; the journal captures the whole decision plus a predicted outcome and confidence for calibration. - As a substitute for actually reviewing entries later. A journal nobody revisits delivers no calibration; if there is no intent to review, skip it.
What ships with it
5 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.
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.
- 12d ago First seen · 67 lines · 109 tokens per session scan A f15f5834d5dc
think-decision-journal is a skill published in the GitHub repository product-on-purpose/thinking-framework-skills (15 stars, last pushed today), licensed Apache-2.0. It adds 109 tokens to every session and 1,348 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
ops-demo
CocoOps demo mode activator — populates .cocoplus/ops/demo/ with realistic mock data and sets cocoplus.toml [demo] enabled = true. Invoked via $ops demo.
acl-rule-analysis
Vendor-agnostic ACL and firewall rule analysis with shadowed rule detection, overly permissive rule identification, unused rule discovery, redundant rule flagging, and rule ordering optimization. Covers ACLs (Cisco/JunOS/EOS) and firewall policies (PAN-OS/FortiGate/CheckPoint).
add-analytics
Add Google Analytics 4 tracking to any project. Detects framework, adds tracking code, sets up events, and configures privacy settings.
agent-teams-simplify-and-harden
Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or…
agent-team-orchestration
Orchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with different specializations, (2) Defining task routing and lifecycle (inbox → spec → build → review → done), (3) Creating handoff protocols between agents, (4)…
agent-bom-enforce
Enforce security policies on MCP tool calls and block dangerous operations at runtime. Use when: "block risky calls", "apply policy", "proxy", "runtime protection", "policy enforcement", "intercept MCP calls".