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 ijust/intent-planner --skill intent-writebackgit clone --depth 1 https://github.com/ijust/intent-plannerWrote 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/ijust/intent-planner/intent-writeback)<a href="https://agentmods.dev/skills/ijust/intent-planner/intent-writeback"><img src="https://agentmods.dev/badge/skills/ijust/intent-planner/intent-writeback/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/ijust/intent-planner/intent-writeback"><img src="https://agentmods.dev/badge/skills/ijust/intent-planner/intent-writeback.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 | $0.00055 | $0.03811 |
| Opus 5.5 | $0.00022 | $0.01524 |
| Sonnet 5.5 | $0.00011 | $0.00762 |
| Haiku 4.5 | $0.00006 | $0.00381 |
Grade A, and why
intent-writeback 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 1mo 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
intent-writeback Skill
Core Mission
- Success Criteria:
- The target packet is identified as exactly one via the 5-tier priority (argument → latest row of export-log → Source Packet heading → direct-implementation route [explicit exit record
format=directprimary, 3-condition AND inference fallback] → text matching + confirmation) - Learnings from the 5 perspectives are extracted and presented by cross-checking the implementation reality against the packet definition and the compass
- The learnings are first recorded into deltas.md as a delta, and the canonical deliverables are not edited directly
- Only the approved items are reflected into the canonical deliverables, with Status and reflection targets recorded in the delta
- Declined items carry one of the two tags "rejected (no re-proposal) | on-hold (re-propose at the next writeback)"
- The completed packet has state: done, closed_at, and spec_refs filled in, has been moved to archive//, and index.md has been regenerated
- The outcome branch is selected only when the user explicitly says "outcome learning", and recording its pending observation does not run Packet completion processing
- No application code has been changed at all
- The target packet is identified as exactly one via the 5-tier priority (argument → latest row of export-log → Source Packet heading → direct-implementation route [explicit exit record
Execution Steps
Before suggesting that a journey can be closed, also compare its original requirements and completion conditions in the journey body against observations. If any requirement is uncovered by packets or a required condition is unmet, unverified, or awaiting a decision, do not suggest closure even when all member packets are done.
Perform completion operations and reports only for targets that pass the pre-completion comparison in writeback-protocol.md. For unmet or unverified conditions, report delta records and remaining work; do not mark done, archive, or suggest closing the journey.
Step 0: Determine the operation kind
- Select the outcome branch only when the user explicitly says "outcome learning." If ordinary implementation learning versus outcome recording is ambiguous, confirm the operation kind in plain words and wait for the answer (rules §0).
What ships with it
2 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.
- 1mo ago Changed · +4 lines 7e5af12f09e6
- 2mo ago First seen · 98 lines · 55 tokens per session scan A fe03bcd7e347
intent-writeback is a skill published in the GitHub repository ijust/intent-planner (5 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 3,811 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
Other skills, from other repositories
repo-hygiene
Use when the scheduled repo-hygiene workflow runs from GitHub Actions (or an operator dry-run) to scan the repository for small, certain docs/test/code hygiene issues and fix them as one batched branch.
test-failure-explorer
Import and query an existing Vitest 3.2 JSON report to explore test failures, file-level errors and original diagnostic evidence.
requirements-writer
Use when converting PM intake into a slice SPEC.md with observable acceptance outcomes, explicit scope, and advisory sibling build dependencies.
refocusing
Use when a long-running agent may have lost the product outcome, when context was compacted, when work has crossed a major boundary, or when a fresh path-based trace is needed before the next consequential action. Not for fresh-session orientation, waking an idle seat, or checkpoints.
orchestration-team
Use when coordinating assignments, selected review boundaries, or blocked work across a rig.
oversight-team
Use when you are a seat on the oversight pod (a standing monitor-mode rig that keeps OTHER rigs healthy), configuring or running the drift detectors, or choosing whether to intervene vs escalate. Covers the pull-not-poll posture, the v0 detectors (premature-park, process-drift, off-task, token-burn), the intervention…