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 ychampion/cskill-agents --skill exact-rendered-decision-journalinggit clone --depth 1 https://github.com/ychampion/cskill-agentsWrote 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/ychampion/cskill-agents/exact-rendered-decision-journaling)<a href="https://agentmods.dev/skills/ychampion/cskill-agents/exact-rendered-decision-journaling"><img src="https://agentmods.dev/badge/skills/ychampion/cskill-agents/exact-rendered-decision-journaling/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/ychampion/cskill-agents/exact-rendered-decision-journaling"><img src="https://agentmods.dev/badge/skills/ychampion/cskill-agents/exact-rendered-decision-journaling.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 | $0.00028 | $0.00536 |
| Opus 5 | $0.00014 | $0.00268 |
| Sonnet 5 | $0.00006 | $0.00107 |
| Haiku 4.5 | $0.00003 | $0.00054 |
Grade A, and why
exact-rendered-decision-journaling 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 — 30 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SKILL: Exact Rendered Decision Journaling
Domain: tool-orchestration
Trigger: Apply whenever per-message budgeting rewrites tool_result content so transcripts capture the preview string and resume logic can reapply the same decisions.
Source Pattern: Distilled from reviewed tool-loop and result-shaping patterns.
Core Method
After enforceToolResultBudget persists a candidate, produce a ToolResultReplacementRecord (kind: 'tool-result', toolUseId, replacement) that stores the literal preview returned by buildLargeToolResultMessage. Pass newlyReplaced to the writeToTranscript callback so every replacement is serialized in the conversation transcript; later, reconstructContentReplacementState(messages, records, inheritedReplacements) can rebuild the same seenIds/replacements map, and provisionContentReplacementState gates the feature flag (tengu_hawthorn_steeple) so journaling stays optional.
Key Rules
- Persist the exact preview string the model consumed—the replacement record must not approximate or trim the visible content, or prompt-cache alignment breaks.
- Include
toolUseIdwith each record so resume enforcement can quickly look up replacements without re-running persistence. - Forward
parentState.replacementsintoreconstructForSubagentResumewhen resuming forks so inherited decisions reapply exactly. - Guard
writeToTranscript; it may be undefined in ephemeral contexts, so only call it when provided. - When the flag is disabled,
provisionContentReplacementStateshould returnundefinedand journaling remains inert.
Example Application
In the query loop pass the transcript writer to applyToolResultBudget; the callback serializes newlyReplaced records, which are later replayed by provisionContentReplacementState(initialMessages, records) during resume so the budget enforces the same replacements it originally made.
Anti-Patterns (What NOT to do)
- Do not journal before
persistToolResultsucceeds and the preview is built—the replacement string must exist before you write it down. - Do not drop
kind: 'tool-result'or thetoolUseIdfields;reconstructContentReplacementStaterelies on them to filter and lookup. - Avoid journaling duplicates (e.g., from re-applied replacements); only persist what is new so transcripts stay minimal.
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 · 30 lines · 28 tokens per session scan A bd2d1443abae
exact-rendered-decision-journaling is a skill published in the GitHub repository ychampion/cskill-agents (36 stars, last pushed 5mo ago), licensed MIT. It adds 28 tokens to every session and 536 once invoked, about $0.0001 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.
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