Conciseness

The body is lean and imperative — short Protocol steps, bullet Guidelines, no explanations of concepts Claude already knows. It misses anchor 5 because Guidelines and Anti-Patterns overlap ("One entry per correction event" vs "No duplicate entries"; the promote-on-second-entry rule appears in both), and the "Canonical response anchors" section adds marginal value.

Actionability

Guidance is mostly concrete and executable: exact file name, the header pattern to count ("## Agent Learning Log: Iteration" headers → N), Iteration #(N+1), default status `proposed`, and the promotion rule. Not anchor 5 because several guideline sentences are garbled and lose their operative words — "'Better Approach' must actionable — state what to , not what to avoid" and "name specific file, rule, or action that wrong" — leaving a reader to reconstruct the instruction.

Workflow Clarity

The Protocol is a clear 5-step sequence (Detect signal → Redact → Read/count → Append → Continue) with a concrete signal taxonomy for step 1. Not anchor 5 because the redaction step (the one risky operation, since sensitive data is being persisted) has no verification checkpoint, and there is no check that the appended entry is well-formed.

Progressive Disclosure

The body is a well-organized ~70-line overview that correctly delegates the entry template, bootstrap text, and signal taxonomy to references/log-format.md, which exists and matches, referenced three times with clear labels ([Log Entry Format](references/log-format.md)). References are one level deep with no nesting — this matches the anchor-5 example structure.

Specificity

The description names one concrete action — "Append a learning entry to AGENTS_LEARNING.md" — plus the trigger surfaces ("pre-write audit auto-fix, a retrospective correction loop, or a mid-session user correction"), but those surfaces are when-conditions rather than capabilities, so action coverage is not comprehensive. It sits at anchor 3 (domain + 1-2 concrete actions); not 4 because it never says what the entry contains or what happens after (promotion, iteration counting).

Completeness

It explicitly answers what ("Append a learning entry to AGENTS_LEARNING.md when an AI agent makes a mistake") and when ("Auto-activates after a pre-write audit auto-fix, a retrospective correction loop, or a mid-session user correction. Use when: mistake, wrong, correction, my bad, agent error, learning log") with concrete trigger phrases. This matches the anchor-5 example pattern of what + explicit 'Use when' with trigger terms; not below 5 since neither half is vague.

Trigger Term Quality

"Use when: mistake, wrong, correction, my bad, agent error, learning log" covers natural phrases a user would actually say, including the colloquial "my bad". It falls short of anchor 5 because common variations like "oops", "that's not right", or "redo" (which appears in the frontmatter metadata but not the description) are absent.

Distinctiveness Conflict Risk

The target file (AGENTS_LEARNING.md) and the agent-mistake niche are distinct from typical user-facing skills. However, generic trigger words like "mistake", "wrong", and "correction" could overlap with other feedback/retrospective skills, matching anchor 4 (mostly distinct, minor overlap risk) rather than 5 (clear niche with minimal conflict).