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 iliaal/whetstone --skill ia-reflectgit clone --depth 1 https://github.com/iliaal/whetstoneWrote 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/iliaal/whetstone/ia-reflect)<a href="https://agentmods.dev/skills/iliaal/whetstone/ia-reflect"><img src="https://agentmods.dev/badge/skills/iliaal/whetstone/ia-reflect/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/iliaal/whetstone/ia-reflect"><img src="https://agentmods.dev/badge/skills/iliaal/whetstone/ia-reflect.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 | $0.00036 | $0.02003 |
| Opus 5.5 | $0.00014 | $0.00801 |
| Sonnet 5.5 | $0.00007 | $0.00401 |
| Haiku 4.5 | $0.00004 | $0.00200 |
Grade A, and why
ia-reflect 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 11d 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.
This is a copy
100% identical to reflect — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflect
Success Criteria
- Every mistake/friction point cites the specific moment and its impact
- Findings state the reviewed evidence's limits; missing observations do not establish success or complete coverage
- Improvements are actionable and prioritized (cap defined in step 4)
- Each skill audit proposes measurable changes (not vague suggestions)
- Memory persistence follows existing authorization, or the user selects concrete proposed items before any write
- If review activity occurred, review-trap candidates are reported; persist only with authorization, or explicitly report no candidates
Process
1. Session Review
Scan the available conversation and relevant artifacts. State any missing or truncated evidence and the scope actually reviewed. For each finding, cite the specific exchange or artifact (quote or paraphrase) and its impact. Do not infer a clean session from gaps in the record.
| Category | Signal |
|---|---|
| Mistakes | Wrong outputs, incorrect assumptions, hallucinated facts |
| Friction | Repeated clarifications, verbose responses, misread intent |
| Wasted effort | Work discarded, wrong approaches tried first |
| Wins | Approaches worth repeating, smooth interactions |
Skip one-time typos, external tool failures, and issues outside agent control.
Before proposing durable guidance from inferred behavior, apply the recurrence gate and exceptions in capture-and-routing.md.
2. Review Activity Scan (if applicable)
Collect candidates in the response. A retrospective alone does not authorize memory writes or skill edits; apply only changes already authorized by the user or approved in steps 4 and 5.
If the session included PR or MR review activity in either direction, run this scan before moving on. Skip only if no reviews happened.
Inbound (my code was reviewed): For each review comment received:
- Did I accept it? If yes, what pattern did the reviewer catch that I missed? Is it a recurring blind spot? Propose a one-line memory candidate for step 4.
- Did I push back? If I was right and the reviewer was wrong, nothing to capture. If I was wrong and had to retract mid-thread, capture what I learned.
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.
- 11d ago Changed · +2 lines 64ecbed43490
- 16d ago Changed bef2589d7659
- 24d ago Changed · +7 lines 4b1426dff9d0
- 1mo ago Changed bb6dc083a2a2
- 1mo ago Changed · +2 lines 315b5bc45b26
- 1mo ago First seen · 110 lines · 36 tokens per session scan A 1d976efc9513
ia-reflect is a skill published in the GitHub repository iliaal/whetstone (35 stars, last pushed yesterday), licensed MIT. It adds 36 tokens to every session and 2,003 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to reflect, differing in 2 lines, and is treated as a copy.
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