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 daddia/claude-for-strategy --skill set-targetsgit clone --depth 1 https://github.com/daddia/claude-for-strategyWrote 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/daddia/claude-for-strategy/set-targets)<a href="https://agentmods.dev/skills/daddia/claude-for-strategy/set-targets"><img src="https://agentmods.dev/badge/skills/daddia/claude-for-strategy/set-targets/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/daddia/claude-for-strategy/set-targets"><img src="https://agentmods.dev/badge/skills/daddia/claude-for-strategy/set-targets.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 | $0.00059 | $0.01031 |
| Opus 5 | $0.00030 | $0.00515 |
| Sonnet 5 | $0.00012 | $0.00206 |
| Haiku 4.5 | $0.00006 | $0.00103 |
Grade A, and why
set-targets 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 9d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Set Targets
When to use
Attach baseline, target, commit/aspirational label, and scoring formula — same number means opposite things without commitment level.
What this skill does not do
- Does not instrument metrics — route unknown baselines to
/okr:instrument-metricsfirst. - Does not write KRs — route to
/okr:write-key-results. - Does not score the cycle — route to
/okr:score-and-retro.
Preconditions
| Input | If missing |
|---|---|
| KRs with known baselines | Halt — route to instrument-metrics |
| Practice profile (philosophy, formula) | Default linear interpolation; tag [PROVISIONAL] |
| Explicit commit/aspirational label per KR | Ask — do not infer from number |
Provisional mode
Without seed history: set-level sandbagging check limited to trivial-target flags on current set.
Trust spine
- Confidence bands (
governance-tracking):- High: Every KR has type, baseline, target, formula, calibration flag.
- Medium: Some calibration flags on commit/aspirational mismatch.
- Low: Baselines unknown — halt, route to instrument-metrics.
- Failure modes:
- Strategic advice vs. support: Targets are draft for calibration approval.
- Client confidentiality: Targets may be pre-approval — CONFIDENTIAL header.
- Accountability gap: Sandbagging patterns named, not praised.
- Analytical Rigor: N/A — governance shape.
- Incentive Gaming: Guards sandbagging — trivial aspirational targets and commit labels on stretch goals flagged.
- Escalation triggers: Consistent ~1.0 history in seed data — name sandbagging pattern for calibration.
Workflow
- Read practice profile for philosophy and scoring formula.
- Get explicit commit/aspirational label per KR — don't infer.
- Set baseline and target — halt if baseline unknown.
- State scoring formula alongside each KR.
- Calibration check: commits realistic? aspirational genuinely stretch? history of ~1.0?
- Gaming-pattern check before output.
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.
- 9d ago First seen · 112 lines · 59 tokens per session scan A 6f36de62ae5e
set-targets is a skill published in the GitHub repository daddia/claude-for-strategy (2 stars, last pushed 2mo ago), licensed MIT. It adds 59 tokens to every session and 1,031 once invoked, about $0.0003 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 2026-08-31.
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